From 3a4fcd3f93d15ee1a9938168e506ee8615a707e3 Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 22:05:40 +0100
Subject: [PATCH 01/11] fix docs for cli commands

---
 ENVIRONMENT_VARIABLES.md | 20 ++++++++++----------
 MODELS.md                | 16 ++++++++--------
 2 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/ENVIRONMENT_VARIABLES.md b/ENVIRONMENT_VARIABLES.md
index f5128ea1..b7abceeb 100644
--- a/ENVIRONMENT_VARIABLES.md
+++ b/ENVIRONMENT_VARIABLES.md
@@ -1,9 +1,9 @@
 ## Environment variables for running Reginald
 
-To set up the Reginald app (which consists of _both_ the full response engine along with the Slack bot), you can use the `reginald_run` on the terminal. To see the CLI arguments, you can simply run:
+To set up the Reginald app (which consists of _both_ the full response engine along with the Slack bot), you can use the `reginald run_all` on the terminal. To see the CLI arguments, you can simply run:
 
 ```bash
-reginald_run --help
+reginald run_all --help
 ```
 
 **Note**: specifying CLI arguments will override any environment variables set.
@@ -33,7 +33,7 @@ For creating a data index, you must set the GitHub token environment variable `G
 
 ### Model environment variables
 
-Lastly, to avoid using CLI variables and be able to simply use `reginald_run`, you can also set the following variables too:
+Lastly, to avoid using CLI variables and be able to simply use `reginald run_all`, you can also set the following variables too:
 
 - `REGINALD_MODEL`: name of model to use (see the [models README](MODELS.md)) for the list of models available
 - `REGINALD_MODEL_NAME`: name of sub-model to use with the one requested if not using `hello` model.
@@ -59,24 +59,24 @@ source .env
 
 ## Environment variables for running _only_ the response engine
 
-To set up the Reginald response engine (without the Slack bot), you can use the `reginald_run_engine` on the terminal. To see the CLI arguments, you can simply run:
+To set up the Reginald response engine (without the Slack bot), you can use the `reginald run_all_engine` on the terminal. To see the CLI arguments, you can simply run:
 
 ```bash
-reginald_run_api_llm --help
+reginald run_all_api_llm --help
 ```
 
-The CLI arguments are largely the same as `reginald_run` except that the Slack bot tokens are not required (as they will be used to set up the Slack bot which will call the response engine via an API that is set up using `reginald_run_api_llm`). You can also use the same environment variables as `reginald_run` except for the Slack bot tokens.
+The CLI arguments are largely the same as `reginald run_all` except that the Slack bot tokens are not required (as they will be used to set up the Slack bot which will call the response engine via an API that is set up using `reginald run_all_api_llm`). You can also use the same environment variables as `reginald run_all` except for the Slack bot tokens.
 
-You can still use the same `.env` file that you used for `reginald_run` to set up the environment variables or choose to have a separate `.response_engine_env` file to store the environment variables required for the response engine set up.
+You can still use the same `.env` file that you used for `reginald run_all` to set up the environment variables or choose to have a separate `.response_engine_env` file to store the environment variables required for the response engine set up.
 
 ## Environment variables for running _only_ the Slack-bot
 
-To set up the Reginald Slack bot (without the response engine), you can use the `reginald_run_api_bot` on the terminal. To see the CLI arguments, you can simply run:
+To set up the Reginald Slack bot (without the response engine), you can use the `reginald run_all_api_bot` on the terminal. To see the CLI arguments, you can simply run:
 
 ```bash
-reginald_run_api_bot --help
+reginald run_all_api_bot --help
 ```
 
 This command takes in an emoji to respond with and will set up a Slack bot that responds with the specified emoji (by default, this is the :rocket: emoji if no emoji is specified). You can also set an environment variable for the emoji to respond with using `REGINALD_EMOJI`.
 
-You can use the same `.env` file that you used for `reginald_run` to set up the environment variables or choose to have a separate `.slack_bot_env` file to store the environment variables required for the Slack bot set up. This must include the Slack bot tokens.
+You can use the same `.env` file that you used for `reginald run_all` to set up the environment variables or choose to have a separate `.slack_bot_env` file to store the environment variables required for the Slack bot set up. This must include the Slack bot tokens.
diff --git a/MODELS.md b/MODELS.md
index c7557e87..6f4bfd84 100644
--- a/MODELS.md
+++ b/MODELS.md
@@ -32,7 +32,7 @@ When running the Reginald Slack bot, you can specify which data index to use usi
 - `public`: builds an index with the all the public data listed above
 - `all_data`: builds an index with all the data listed above including data from our private repo
 
-Once a data index has been built, it will be saved in the `data` directory specified in the `reginald_run` (or `reginald_run_api_llm`) CLI arguments or the `LLAMA_INDEX_DATA_DIR` environment variable. If you want to force a new index to be built, you can use the `--force-new-index` or `-f` flag, or you can set the `LLAMA_INDEX_FORCE_NEW_INDEX` environment variable to `True`.
+Once a data index has been built, it will be saved in the `data` directory specified in the `reginald run_all` (or `reginald run_all_api_llm`) CLI arguments or the `LLAMA_INDEX_DATA_DIR` environment variable. If you want to force a new index to be built, you can use the `--force-new-index` or `-f` flag, or you can set the `LLAMA_INDEX_FORCE_NEW_INDEX` environment variable to `True`.
 
 There are several options of the LLM to use with the `llama-index` models, some of which we have implemented in this library and which we discuss below.
 
@@ -45,7 +45,7 @@ We have two models which involve hosting the LLM ourselves and using the `llama-
 This model uses the [`llama-cpp-python`](https://github.com/abetlen/llama-cpp-python) library to host a quantised LLM. In our case, we have been using quantised versions of Meta's Llama-2 model uploaded by [TheBloke](https://huggingface.co/TheBloke) on Huggingface's model hub. An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model llama-index-llama-cpp \
   --model-name https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf \
   --mode chat \
@@ -64,7 +64,7 @@ Running this command requires about 7GB of RAM. We were able to run this on our
 If you wish to download the quantised model (as a `.gguf` file) and host it yourself, you can do so by passing the file name to the `--model-name` argument and using the `--is-path` flag (alternatively, you can re-run the above but first set the environment variable `LLAMA_INDEX_IS_PATH` to `True`):
 
 ```bash
-reginald_run \
+reginald run_all \
   --model llama-index-llama-cpp \
   --model-name gguf_models/llama-2-7b-chat.Q4_K_M.gguf \
   --is-path \
@@ -82,7 +82,7 @@ given that the `llama-2-7b-chat.Q4_K_M.gguf` file is in a `gguf_models` director
 This model uses an LLM from [Huggingface](https://huggingface.co/models) to generate a response. An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model llama-index-hf \
   --model-name microsoft/phi-1_5 \
   --mode chat \
@@ -107,7 +107,7 @@ To use this model, you must set the following environment variables:
 An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model llama-index-gpt-azure \
   --model-name "reginald-gpt35-turbo" \
   --mode chat \
@@ -124,7 +124,7 @@ To use this model, you must set the `OPENAI_API_KEY` environment variable and se
 An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model llama-index-gpt-openai \
   --model-name "gpt-3.5-turbo" \
   --mode chat \
@@ -145,7 +145,7 @@ To use this model, you must set the following environment variables:
 An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model chat-completion-azure \
   --model-name "reginald-curie"
 ```
@@ -160,7 +160,7 @@ To use this model, you must set the `OPENAI_API_KEY` environment variable and se
 An example of running this model locally is:
 
 ```bash
-reginald_run \
+reginald run_all \
   --model chat-completion-openai \
   --model-name "gpt-3.5-turbo"
 ```

From dc9a7fe3aebd9ee68168e118c77f6c15734c0be6 Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 22:06:07 +0100
Subject: [PATCH 02/11] remove any unused dependencies even for notebooks

---
 poetry.lock    | 1723 +-----------------------------------------------
 pyproject.toml |   29 +-
 2 files changed, 32 insertions(+), 1720 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 92a9f6fb..a5c2ccf1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -31,17 +31,6 @@ test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "py
 test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
 testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
 
-[[package]]
-name = "aiofiles"
-version = "23.2.1"
-description = "File support for asyncio."
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"},
-    {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
-]
-
 [[package]]
 name = "aiohttp"
 version = "3.9.5"
@@ -151,30 +140,6 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
-[[package]]
-name = "altair"
-version = "5.3.0"
-description = "Vega-Altair: A declarative statistical visualization library for Python."
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "altair-5.3.0-py3-none-any.whl", hash = "sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a"},
-    {file = "altair-5.3.0.tar.gz", hash = "sha256:5a268b1a0983b23d8f9129f819f956174aa7aea2719ed55a52eba9979b9f6675"},
-]
-
-[package.dependencies]
-jinja2 = "*"
-jsonschema = ">=3.0"
-numpy = "*"
-packaging = "*"
-pandas = ">=0.25"
-toolz = "*"
-
-[package.extras]
-all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.3.0)"]
-dev = ["geopandas", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pytest", "pytest-cov", "ruff (>=0.3.0)", "types-jsonschema", "types-setuptools"]
-doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
-
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -206,17 +171,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
-[[package]]
-name = "appnope"
-version = "0.1.4"
-description = "Disable App Nap on macOS >= 10.9"
-optional = true
-python-versions = ">=3.6"
-files = [
-    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
-    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
-]
-
 [[package]]
 name = "arpeggio"
 version = "2.0.2"
@@ -309,7 +263,7 @@ msal-extensions = ">=0.3.0"
 name = "azure-storage-file-share"
 version = "12.16.0"
 description = "Microsoft Azure Azure File Share Storage Client Library for Python"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "azure-storage-file-share-12.16.0.tar.gz", hash = "sha256:412fb7e6c3c28f6f722af98195aa59407aaa32323afa13a4a01f62d0b8774eb3"},
@@ -346,17 +300,6 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
-[[package]]
-name = "bitsandbytes"
-version = "0.41.3.post2"
-description = "k-bit optimizers and matrix multiplication routines."
-optional = true
-python-versions = "*"
-files = [
-    {file = "bitsandbytes-0.41.3.post2-py3-none-any.whl", hash = "sha256:ceb301a3d4e6bf52bdad8d09f3064ac194bdfdeae535994c0315bd2ef7639cca"},
-    {file = "bitsandbytes-0.41.3.post2.tar.gz", hash = "sha256:7d25a51fb3b74b58e569473f8b70a5239124c0593dc053479c41cf2cd6730502"},
-]
-
 [[package]]
 name = "black"
 version = "24.4.2"
@@ -401,24 +344,6 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
-[[package]]
-name = "bleach"
-version = "6.1.0"
-description = "An easy safelist-based HTML-sanitizing tool."
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
-    {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
-]
-
-[package.dependencies]
-six = ">=1.9.0"
-webencodings = "*"
-
-[package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
-
 [[package]]
 name = "build"
 version = "1.2.1"
@@ -688,86 +613,6 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
-[[package]]
-name = "comm"
-version = "0.2.2"
-description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
-    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
-]
-
-[package.dependencies]
-traitlets = ">=4"
-
-[package.extras]
-test = ["pytest"]
-
-[[package]]
-name = "contourpy"
-version = "1.2.1"
-description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = true
-python-versions = ">=3.9"
-files = [
-    {file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"},
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-
-[package.dependencies]
-numpy = "*"
-
 [[package]]
 name = "fastapi"
 version = "0.110.3"
@@ -1272,97 +978,22 @@ files = [
 [package.extras]
 devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
 
-[[package]]
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-description = "A simple Python wrapper for ffmpeg"
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-python-versions = "*"
-files = [
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-
 [[package]]
 name = "filelock"
-version = "3.15.0"
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 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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-
-[package.extras]
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-graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "pycairo", "scipy"]
-lxml = ["lxml (>=4.0)"]
-pathops = ["skia-pathops (>=0.5.0)"]
-plot = ["matplotlib"]
-repacker = ["uharfbuzz (>=0.23.0)"]
-symfont = ["sympy"]
-type1 = ["xattr"]
-ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.1.0)"]
-woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
-
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
@@ -1451,24 +1082,22 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.3.1"
+version = "2024.6.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
-    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
+    {file = "fsspec-2024.6.0-py3-none-any.whl", hash = "sha256:58d7122eb8a1a46f7f13453187bfea4972d66bf01618d37366521b1998034cee"},
+    {file = "fsspec-2024.6.0.tar.gz", hash = "sha256:f579960a56e6d8038a9efc8f9c77279ec12e6299aa86b0769a7e9c46b94527c2"},
 ]
 
-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
 arrow = ["pyarrow (>=1)"]
 dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
 dropbox = ["dropbox", "dropboxdrivefs", "requests"]
 full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
 fuse = ["fusepy"]
@@ -1485,6 +1114,9 @@ s3 = ["s3fs"]
 sftp = ["paramiko"]
 smb = ["smbprotocol"]
 ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -1519,68 +1151,6 @@ gitdb = ">=4.0.1,<5"
 doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
 test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
-[[package]]
-name = "gradio"
-version = "4.36.1"
-description = "Python library for easily interacting with trained machine learning models"
-optional = true
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.dependencies]
-aiofiles = ">=22.0,<24.0"
-altair = ">=4.2.0,<6.0"
-fastapi = "*"
-ffmpy = "*"
-gradio-client = "1.0.1"
-httpx = ">=0.24.1"
-huggingface-hub = ">=0.19.3"
-importlib-resources = ">=1.3,<7.0"
-jinja2 = "<4.0"
-markupsafe = ">=2.0,<3.0"
-matplotlib = ">=3.0,<4.0"
-numpy = ">=1.0,<3.0"
-orjson = ">=3.0,<4.0"
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-pandas = ">=1.0,<3.0"
-pillow = ">=8.0,<11.0"
-pydantic = ">=2.0"
-pydub = "*"
-python-multipart = ">=0.0.9"
-pyyaml = ">=5.0,<7.0"
-ruff = {version = ">=0.2.2", markers = "sys_platform != \"emscripten\""}
-semantic-version = ">=2.0,<3.0"
-tomlkit = "0.12.0"
-typer = {version = ">=0.12,<1.0", markers = "sys_platform != \"emscripten\""}
-typing-extensions = ">=4.0,<5.0"
-urllib3 = ">=2.0,<3.0"
-uvicorn = {version = ">=0.14.0", markers = "sys_platform != \"emscripten\""}
-
-[package.extras]
-oauth = ["authlib", "itsdangerous"]
-
-[[package]]
-name = "gradio-client"
-version = "1.0.1"
-description = "Python library for easily interacting with trained machine learning models"
-optional = true
-python-versions = ">=3.8"
-files = [
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-
-[package.dependencies]
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-httpx = ">=0.24.1"
-huggingface-hub = ">=0.19.3"
-packaging = "*"
-typing-extensions = ">=4.0,<5.0"
-websockets = ">=10.0,<12.0"
-
 [[package]]
 name = "greenlet"
 version = "3.0.3"
@@ -1854,21 +1424,6 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
-[[package]]
-name = "importlib-resources"
-version = "6.4.0"
-description = "Read resources from Python packages"
-optional = true
-python-versions = ">=3.8"
-files = [
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-
-[package.extras]
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-testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
-
 [[package]]
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@@ -1979,7 +1501,7 @@ test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "num
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-
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 [[package]]
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 version = "2.22"
@@ -4440,17 +3332,6 @@ files = [
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 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
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-optional = true
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 tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
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-
 [[package]]
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 version = "4.2.0"
@@ -4580,20 +3447,6 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
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-
 [[package]]
 name = "pytz"
 version = "2024.1"
@@ -4609,7 +3462,7 @@ files = [
 name = "pywin32"
 version = "306"
 description = "Python for Window Extensions"
-optional = true
+optional = false
 python-versions = "*"
 files = [
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@@ -4698,106 +3551,6 @@ files = [
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 jeepney = ">=0.6"
 
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-
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@@ -5791,24 +4380,6 @@ requests = ">=2.26.0"
 [package.extras]
 blobfile = ["blobfile (>=2)"]
 
-[[package]]
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-
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-    {file = "xxhash-3.4.1.tar.gz", hash = "sha256:0379d6cf1ff987cd421609a264ce025e74f346e3e145dd106c0cc2e3ec3f99a9"},
-]
-
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -6758,11 +5091,9 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 
 [extras]
 api-bot = ["fastapi", "pydantic", "requests", "uvicorn"]
-azure = ["pulumi", "pulumi-azure-native"]
-ft-notebooks = ["datasets", "faiss-cpu", "ipykernel"]
-llama-index-notebooks = ["bitsandbytes", "gradio", "ipykernel", "nbconvert"]
+azure = ["azure-storage-file-share", "pulumi", "pulumi-azure-native"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<3.12"
-content-hash = "3c2fb819c064f00c71ebc15edaee17905150a91adc0e2b2c2026e406f29a33b6"
+content-hash = "6c025f7263718fd83e17df5ff9f4fbacfdef3e41e3338d2567859b58e50d6050"
diff --git a/pyproject.toml b/pyproject.toml
index 617068bf..cfd99cf4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,23 +19,17 @@ readme = "README.md"
 [tool.poetry.dependencies]
 python = ">=3.11,<3.12"
 accelerate = "^0.31.0"
-bitsandbytes = { version="^0.41.1", optional=true }
-datasets = { version="^2.16.1", optional=true }
-faiss-cpu = { version="^1.7.4", optional=true }
 fastapi = { version="^0.110.1", optional=true }
 gitpython = "^3.1.43"
-gradio = { version="^4.36.1", optional=true }
 httpx = "^0.27.0"
-ipykernel = { version="^6.23.2", optional=true }
-langchain = "^0.2.3"
 llama-cpp-python = "^0.2.78"
 llama-index = "^0.10.43"
-nbconvert = { version="^7.8.0", optional=true }
 nest_asyncio = "^1.6.0"
 openai = "^1.33.0"
 pandas = "^2.2.2"
 pulumi = { version="^3.100.0", optional=true }
 pulumi-azure-native = { version="^2.24.0", optional=true }
+azure-storage-file-share = { version="^12.16.0", optional=true }
 pydantic = { version="^2.7.4", optional=true }
 requests = { version="^2.32.3", optional=true }
 safetensors = "^0.4.3"
@@ -44,19 +38,16 @@ sentence-transformers = "2.*"
 torch = "^2.3.1"
 transformers = "^4.41.2"
 uvicorn = { version="^0.23.2", optional=true }
-llama-index-readers-github = "^0.1.9"
 llama-index-llms-azure-openai = "^0.1.8"
 llama-index-llms-openai = "^0.1.22"
+llama-index-embeddings-huggingface = "^0.2.1"
 llama-index-llms-huggingface = "^0.2.3"
 llama-index-llms-ollama = "^0.1.5"
 llama-index-llms-llama-cpp = "^0.1.3"
 llama-index-readers-file = "^0.1.23"
-llama-index-embeddings-langchain = "^0.1.2"
-typer = "^0.12.3"
-langchain-community = "^0.2.4"
+llama-index-readers-github = "^0.1.9"
 tiktoken = "^0.7.0"
-llama-index-embeddings-huggingface = "^0.2.1"
-azure-storage-file-share = "^12.16.0"
+typer = "^0.12.3"
 rich = "^13.7.1"
 
 
@@ -79,17 +70,7 @@ api_bot = [
 azure = [
     "pulumi",
     "pulumi-azure-native",
-]
-ft_notebooks = [
-    "datasets",
-    "faiss-cpu",
-    "ipykernel",
-]
-llama_index_notebooks = [
-    "bitsandbytes",
-    "gradio",
-    "ipykernel",
-    "nbconvert"
+    "azure-storage-file-share",
 ]
 
 [tool.poetry.scripts]

From 0d2737b3396a8e68ce0eef403574bbf5206d07a1 Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 22:06:58 +0100
Subject: [PATCH 03/11] refactor so models and modules loaded only when
 necessary

---
 reginald/cli.py                               |   52 +-
 reginald/defaults.py                          |   22 +
 reginald/models/__init__.py                   |   75 ++
 reginald/models/app.py                        |   10 +
 reginald/models/{models => }/base.py          |    0
 reginald/models/chat_interact.py              |   44 +
 reginald/models/create_index.py               |    6 +-
 reginald/models/download_from_fileshare.py    |   65 +
 reginald/models/llama_index/__init__.py       |    0
 reginald/models/llama_index/base.py           |  367 ++++++
 .../models/llama_index/data_index_creator.py  |  396 ++++++
 .../llama_cpp_template.py}                    |    4 +-
 reginald/models/llama_index/llama_index.py    |  370 ++++++
 .../llama_index/llama_index_azure_openai.py   |   68 +
 reginald/models/llama_index/llama_index_hf.py |   55 +
 .../llama_index/llama_index_llama_cpp.py      |   71 ++
 .../models/llama_index/llama_index_ollama.py  |   47 +
 .../models/llama_index/llama_index_openai.py  |   50 +
 reginald/models/llama_index/llama_utils.py    |  119 ++
 reginald/models/models/__init__.py            |   38 -
 reginald/models/models/llama_index.py         | 1136 -----------------
 reginald/models/setup_llm.py                  |   25 +-
 reginald/models/simple/__init__.py            |    0
 .../{models => simple}/chat_completion.py     |    2 +-
 reginald/models/{models => simple}/hello.py   |    2 +-
 reginald/parser_utils.py                      |  226 ----
 reginald/run.py                               |  187 +--
 reginald/run_full_pipeline.py                 |   17 +
 reginald/slack_bot/bot.py                     |    2 +-
 reginald/slack_bot/run_bot.py                 |   26 +
 reginald/slack_bot/{setup_bot.py => utils.py} |   47 +-
 reginald/utils.py                             |    1 -
 tests/test_chat_interact.py                   |   36 +-
 33 files changed, 1939 insertions(+), 1627 deletions(-)
 create mode 100644 reginald/defaults.py
 rename reginald/models/{models => }/base.py (100%)
 create mode 100644 reginald/models/chat_interact.py
 create mode 100644 reginald/models/download_from_fileshare.py
 create mode 100644 reginald/models/llama_index/__init__.py
 create mode 100644 reginald/models/llama_index/base.py
 create mode 100644 reginald/models/llama_index/data_index_creator.py
 rename reginald/models/{models/llama_utils.py => llama_index/llama_cpp_template.py} (93%)
 create mode 100644 reginald/models/llama_index/llama_index.py
 create mode 100644 reginald/models/llama_index/llama_index_azure_openai.py
 create mode 100644 reginald/models/llama_index/llama_index_hf.py
 create mode 100644 reginald/models/llama_index/llama_index_llama_cpp.py
 create mode 100644 reginald/models/llama_index/llama_index_ollama.py
 create mode 100644 reginald/models/llama_index/llama_index_openai.py
 create mode 100644 reginald/models/llama_index/llama_utils.py
 delete mode 100644 reginald/models/models/__init__.py
 delete mode 100644 reginald/models/models/llama_index.py
 create mode 100644 reginald/models/simple/__init__.py
 rename reginald/models/{models => simple}/chat_completion.py (99%)
 rename reginald/models/{models => simple}/hello.py (92%)
 delete mode 100644 reginald/parser_utils.py
 create mode 100644 reginald/run_full_pipeline.py
 create mode 100644 reginald/slack_bot/run_bot.py
 rename reginald/slack_bot/{setup_bot.py => utils.py} (69%)

diff --git a/reginald/cli.py b/reginald/cli.py
index 74c97320..076f3c30 100644
--- a/reginald/cli.py
+++ b/reginald/cli.py
@@ -4,10 +4,14 @@
 
 import typer
 
-from reginald.models.setup_llm import DEFAULT_ARGS
-from reginald.run import EMOJI_DEFAULT, main
+from reginald.defaults import DEFAULT_ARGS, EMOJI_DEFAULT
+from reginald.run import main
 
-API_URL_PROPMPT = "No API URL was provided and REGINALD_API_URL not set. Please provide an API URL for the Reginald app"
+PROMPTS = {
+    "api_url": "No API URL was provided and REGINALD_API_URL not set. Please provide an API URL for the Reginald app",
+    "slack_app_token": "No Slack app token was provided and SLACK_APP_TOKEN not set. Please provide a Slack app token for the bot",
+    "slack_bot_token": "No Slack bot token was provided and SLACK_BOT_TOKEN not set. Please provide a Slack bot token for the bot",
+}
 HELP_TEXT = {
     "model": "Select which type of model to use..",
     "model_name": "Select which sub-model to use (within the main model selected).",
@@ -26,6 +30,8 @@
     "api_url": "API URL for the Reginald app.",
     "emoji": "Emoji to use for the bot.",
     "streaming": "Whether to use streaming for the chat interaction.",
+    "slack_app_token": "Slack app token for the bot.",
+    "slack_bot_token": "Slack bot token for the bot.",
 }
 
 cli = typer.Typer()
@@ -41,6 +47,22 @@ def set_up_logging_config(level: int = 20) -> None:
 
 @cli.command()
 def run_all(
+    slack_app_token: Annotated[
+        str,
+        typer.Option(
+            prompt=PROMPTS["slack_app_token"],
+            envvar="SLACK_APP_TOKEN",
+            help=HELP_TEXT["slack_app_token"],
+        ),
+    ],
+    slack_bot_token: Annotated[
+        str,
+        typer.Option(
+            prompt=PROMPTS["slack_bot_token"],
+            envvar="SLACK_BOT_TOKEN",
+            help=HELP_TEXT["slack_bot_token"],
+        ),
+    ],
     model: Annotated[
         str,
         typer.Option(
@@ -111,6 +133,8 @@ def run_all(
     set_up_logging_config(level=20)
     main(
         cli="run_all",
+        slack_app_token=slack_app_token,
+        slack_bot_token=slack_bot_token,
         model=model,
         model_name=model_name,
         mode=mode,
@@ -130,10 +154,28 @@ def run_all(
 
 @cli.command()
 def bot(
+    slack_app_token: Annotated[
+        str,
+        typer.Option(
+            prompt=PROMPTS["slack_app_token"],
+            envvar="SLACK_APP_TOKEN",
+            help=HELP_TEXT["slack_app_token"],
+        ),
+    ],
+    slack_bot_token: Annotated[
+        str,
+        typer.Option(
+            prompt=PROMPTS["slack_bot_token"],
+            envvar="SLACK_BOT_TOKEN",
+            help=HELP_TEXT["slack_bot_token"],
+        ),
+    ],
     api_url: Annotated[
         str,
         typer.Option(
-            prompt=API_URL_PROPMPT, envvar="REGINALD_API_URL", help=HELP_TEXT["api_url"]
+            prompt=PROMPTS["api_url"],
+            envvar="REGINALD_API_URL",
+            help=HELP_TEXT["api_url"],
         ),
     ],
     emoji: Annotated[
@@ -149,6 +191,8 @@ def bot(
     set_up_logging_config(level=20)
     main(
         cli="bot",
+        slack_app_token=slack_app_token,
+        slack_bot_token=slack_bot_token,
         api_url=api_url,
         emoji=emoji,
     )
diff --git a/reginald/defaults.py b/reginald/defaults.py
new file mode 100644
index 00000000..7aa705a4
--- /dev/null
+++ b/reginald/defaults.py
@@ -0,0 +1,22 @@
+import pathlib
+from typing import Final
+
+EMOJI_DEFAULT: Final[str] = "rocket"
+
+LLAMA_INDEX_DIR: Final[str] = "llama_index_indices"
+
+DEFAULT_ARGS = {
+    "model": "hello",
+    "mode": "chat",
+    "data_dir": pathlib.Path(__file__).parent.parent / "data",
+    "which_index": "reg",
+    "force_new_index": False,
+    "max_input_size": 4096,
+    "k": 3,
+    "chunk_size": 512,
+    "chunk_overlap_ratio": 0.1,
+    "num_output": 512,
+    "is_path": False,
+    "n_gpu_layers": 0,
+    "device": "auto",
+}
diff --git a/reginald/models/__init__.py b/reginald/models/__init__.py
index e69de29b..5988bc26 100644
--- a/reginald/models/__init__.py
+++ b/reginald/models/__init__.py
@@ -0,0 +1,75 @@
+class ModelMapper:
+    @staticmethod
+    def available_models():
+        return [
+            "chat-completion-azure",
+            "chat-completion-openai",
+            "hello",
+            "llama-index-ollama",
+            "llama-index-llama-cpp",
+            "llama-index-hf",
+            "llama-index-gpt-azure",
+            "llama-index-gpt-openai",
+        ]
+
+    @staticmethod
+    def get_model(model_name: str):
+        match model_name:
+            case "chat-completion-azure":
+                from reginald.models.simple.chat_completion import ChatCompletionAzure
+
+                return ChatCompletionAzure
+            case "chat-completion-openai":
+                from reginald.models.simple.chat_completion import ChatCompletionOpenAI
+
+                return ChatCompletionOpenAI
+            case "hello":
+                from reginald.models.simple.hello import Hello
+
+                return Hello
+            case "llama-index-ollama":
+                from reginald.models.llama_index.llama_index_ollama import (
+                    LlamaIndexOllama,
+                )
+
+                return LlamaIndexOllama
+            case "llama-index-llama-cpp":
+                from reginald.models.llama_index.llama_index_llama_cpp import (
+                    LlamaIndexLlamaCPP,
+                )
+
+                return LlamaIndexLlamaCPP
+            case "llama-index-hf":
+                from reginald.models.llama_index.llama_index_hf import LlamaIndexHF
+
+                return LlamaIndexHF
+            case "llama-index-gpt-azure":
+                from reginald.models.llama_index.llama_index_openai import (
+                    LlamaIndexGPTOpenAI,
+                )
+
+                return LlamaIndexGPTOpenAI
+            case "llama-index-gpt-openai":
+                from reginald.models.llama_index.llama_index_openai import (
+                    LlamaIndexGPTOpenAI,
+                )
+
+                return LlamaIndexGPTOpenAI
+            case _:
+                raise ValueError(
+                    f"Model {model_name} not found. Available models: {ModelMapper.available_models()}"
+                )
+
+
+DEFAULTS = {
+    "chat-completion-azure": "reginald-gpt4",
+    "chat-completion-openai": "gpt-3.5-turbo",
+    "hello": None,
+    "llama-index-ollama": "llama3",
+    "llama-index-llama-cpp": "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q6_K.gguf",
+    "llama-index-hf": "microsoft/phi-1_5",
+    "llama-index-gpt-azure": "reginald-gpt4",
+    "llama-index-gpt-openai": "gpt-3.5-turbo",
+}
+
+__all__ = ["MODELS", "DEFAULTS"]
diff --git a/reginald/models/app.py b/reginald/models/app.py
index c9c41e9c..04808e40 100644
--- a/reginald/models/app.py
+++ b/reginald/models/app.py
@@ -1,6 +1,9 @@
+import uvicorn
 from fastapi import FastAPI
 from pydantic import BaseModel
 
+from reginald.models.setup_llm import setup_llm
+
 
 class Query(BaseModel):
     message: str
@@ -38,3 +41,10 @@ async def channel_mention(query: Query):
         return response
 
     return app
+
+
+async def run_reginald_app(**kwargs) -> None:
+    # set up response model
+    response_model = setup_llm(**kwargs)
+    app: FastAPI = create_reginald_app(response_model)
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/reginald/models/models/base.py b/reginald/models/base.py
similarity index 100%
rename from reginald/models/models/base.py
rename to reginald/models/base.py
diff --git a/reginald/models/chat_interact.py b/reginald/models/chat_interact.py
new file mode 100644
index 00000000..391d17f6
--- /dev/null
+++ b/reginald/models/chat_interact.py
@@ -0,0 +1,44 @@
+import readline
+
+from reginald.models.base import ResponseModel
+from reginald.models.setup_llm import setup_llm
+
+art = """
+(`-') (`-')  _          _    <-. (`-')_(`-')  _        _(`-')
+<-.(OO ) ( OO).-/   .->   (_)      \( OO) (OO ).-/   <-. ( (OO ).->
+,------,(,------.,---(`-'),-(`-',--./ ,--// ,---.  ,--. ) \    .'_
+|   /`. '|  .---'  .-(OO )| ( OO|   \ |  || \ /`.\ |  (`-''`'-..__)
+|  |_.' (|  '--.|  | .-, \|  |  |  . '|  |'-'|_.' ||  |OO |  |  ' |
+|  .   .'|  .--'|  | '.(_(|  |_/|  |\    (|  .-.  (|  '__ |  |  / :
+|  |\  \ |  `---|  '-'  | |  |'-|  | \   ||  | |  ||     ||  '-'  /
+`--' '--'`------'`-----'  `--'  `--'  `--'`--' `--'`-----'`------'
+"""
+
+
+def run_chat_interact(streaming: bool = False, **kwargs) -> ResponseModel:
+    # set up response model
+    response_model = setup_llm(**kwargs)
+    user_id = "command_line_chat"
+    print(art)
+
+    while True:
+        message = input(">>> ")
+        if message in ["exit", "exit()", "quit()", "bye Reginald"]:
+            return response_model
+        if message in ["clear_history", "\clear_history"]:
+            if (
+                response_model.mode == "chat"
+                and response_model.chat_engine.get(user_id) is not None
+            ):
+                response_model.chat_engine[user_id].reset()
+                print("\nReginald: History cleared.")
+            else:
+                print("\nReginald: No history to clear.")
+            continue
+
+        if streaming:
+            response = response_model.stream_message(message=message, user_id=user_id)
+            print("")
+        else:
+            response = response_model.direct_message(message=message, user_id=user_id)
+            print(f"\nReginald: {response.message}")
diff --git a/reginald/models/create_index.py b/reginald/models/create_index.py
index 6b54888b..0f685cdf 100644
--- a/reginald/models/create_index.py
+++ b/reginald/models/create_index.py
@@ -10,12 +10,12 @@
 from llama_index.core.llms.callbacks import llm_completion_callback
 from llama_index.core.llms.custom import CustomLLM
 
-from reginald.models.models.llama_index import (
+from reginald.defaults import DEFAULT_ARGS
+from reginald.models.llama_index.base import (
     DataIndexCreator,
     compute_default_chunk_size,
-    setup_settings,
 )
-from reginald.models.setup_llm import DEFAULT_ARGS
+from reginald.models.llama_index.llama_utils import setup_settings
 
 
 class DummyLLM(CustomLLM):
diff --git a/reginald/models/download_from_fileshare.py b/reginald/models/download_from_fileshare.py
new file mode 100644
index 00000000..44d22dc2
--- /dev/null
+++ b/reginald/models/download_from_fileshare.py
@@ -0,0 +1,65 @@
+import logging
+import pathlib
+import sys
+
+from reginald.utils import create_folder
+
+
+def download_from_fileshare(
+    data_dir: pathlib.Path | str,
+    which_index: str,
+    azure_storage_key: str | None,
+    connection_str: str | None,
+) -> None:
+    from azure.storage.fileshare import ShareClient
+    from tqdm import tqdm
+
+    if azure_storage_key is None:
+        logging.error("azure_storage_key is not set.")
+        sys.exit(1)
+    if connection_str is None:
+        logging.error("connection_str is not set.")
+        sys.exit(1)
+
+    # set the file share name and directory
+    file_share_name = "llama-data"
+    file_share_directory = f"llama_index_indices/{which_index}"
+
+    # create a ShareClient object
+    share_client = ShareClient.from_connection_string(
+        conn_str=connection_str,
+        share_name=file_share_name,
+        credential=azure_storage_key,
+    )
+
+    # get a reference to the file share directory
+    file_share_directory_client = share_client.get_directory_client(
+        file_share_directory
+    )
+
+    # set the local download directory
+    local_download_directory = (
+        pathlib.Path(data_dir) / "llama_index_indices" / which_index
+    )
+
+    # create folder if does not exist
+    create_folder(local_download_directory)
+
+    # list all the files in the directory
+    files_list = file_share_directory_client.list_directories_and_files()
+
+    # check if the index exists
+    try:
+        files_list = list(files_list)
+    except:
+        logging.error(f"Index {which_index} does not exist in the file share")
+        sys.exit(1)
+
+    # iterate through each file in the list and download it
+    for file in tqdm(files_list):
+        if not file.is_directory:
+            file_client = file_share_directory_client.get_file_client(file.name)
+            download_path = local_download_directory / file.name
+            with open(download_path, "wb") as file_handle:
+                data = file_client.download_file()
+                data.readinto(file_handle)
diff --git a/reginald/models/llama_index/__init__.py b/reginald/models/llama_index/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reginald/models/llama_index/base.py b/reginald/models/llama_index/base.py
new file mode 100644
index 00000000..6eed3c94
--- /dev/null
+++ b/reginald/models/llama_index/base.py
@@ -0,0 +1,367 @@
+import logging
+import pathlib
+import re
+import sys
+from typing import Callable
+
+import nest_asyncio
+from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage
+from llama_index.core.base.llms.base import BaseLLM
+from llama_index.core.base.response.schema import RESPONSE_TYPE
+
+from reginald.defaults import LLAMA_INDEX_DIR
+from reginald.models.base import MessageResponse, ResponseModel
+from reginald.models.llama_index.data_index_creator import DataIndexCreator
+from reginald.models.llama_index.llama_utils import (
+    compute_default_chunk_size,
+    setup_settings,
+)
+from reginald.utils import stream_iter_progress_wrapper, stream_progress_wrapper
+
+nest_asyncio.apply()
+
+
+class LlamaIndex(ResponseModel):
+    def __init__(
+        self,
+        model_name: str,
+        max_input_size: int,
+        data_dir: pathlib.Path | str,
+        which_index: str,
+        mode: str = "chat",
+        k: int = 3,
+        chunk_size: int | None = None,
+        chunk_overlap_ratio: float = 0.1,
+        num_output: int = 512,
+        force_new_index: bool = False,
+        *args,
+        **kwargs,
+    ) -> None:
+        """
+        Base class for models using llama-index.
+        This class is not intended to be used directly, but rather subclassed
+        to implement the `_prep_llm` method which constructs the LLM to be used.
+
+        Parameters
+        ----------
+        model_name : str
+            Model name to specify which LLM to use.
+        max_input_size : int
+            Context window size for the LLM.
+        data_dir : pathlib.Path | str
+            Path to the data directory.
+        which_index : str
+            Which index to construct (if force_new_index is True) or use.
+            Options are "handbook", "wikis",  "public", "reg" or "all_data".
+        mode : Optional[str], optional
+            The type of engine to use when interacting with the data, options of "chat" or "query".
+            Default is "chat".
+        k : int, optional
+            `similarity_top_k` to use in chat or query engine, by default 3
+        chunk_size : int | None, optional
+            Maximum size of chunks to use, by default None.
+            If None, this is computed as `ceil(max_input_size / k)`.
+        chunk_overlap_ratio : float, optional
+            Chunk overlap as a ratio of chunk size, by default 0.1
+        num_output : int, optional
+            Number of outputs for the LLM, by default 512
+        force_new_index : bool, optional
+            Whether or not to recreate the index vector store,
+            by default False
+        """
+        super().__init__(*args, emoji="llama", **kwargs)
+        logging.info("Setting up Huggingface backend.")
+        if mode == "chat":
+            logging.info("Setting up chat engine.")
+        elif mode == "query":
+            logging.info("Setting up query engine.")
+        else:
+            logging.error("Mode must either be 'query' or 'chat'.")
+            sys.exit(1)
+
+        self.max_input_size = max_input_size
+        self.model_name = model_name
+        self.num_output = num_output
+        self.mode = mode
+        self.k = k
+        self.chunk_size = chunk_size or compute_default_chunk_size(
+            max_input_size=max_input_size, k=k
+        )
+        self.chunk_overlap_ratio = chunk_overlap_ratio
+        self.data_dir = pathlib.Path(data_dir)
+        self.which_index = which_index
+        self.documents = []
+
+        # set up LLM
+        llm = self._prep_llm()
+
+        # set up settings
+        settings = setup_settings(
+            llm=llm,
+            max_input_size=self.max_input_size,
+            num_output=self.num_output,
+            chunk_size=self.chunk_size,
+            chunk_overlap_ratio=self.chunk_overlap_ratio,
+            k=self.k,
+            tokenizer=self._prep_tokenizer(),
+        )
+
+        if force_new_index:
+            logging.info("Generating the index from scratch...")
+            data_creator = DataIndexCreator(
+                which_index=self.which_index,
+                data_dir=self.data_dir,
+                settings=settings,
+            )
+            self.index: VectorStoreIndex = stream_progress_wrapper(
+                data_creator.create_index,
+                task_str="Generating the index from scratch...",
+            )
+            stream_progress_wrapper(
+                data_creator.save_index,
+                task_str="Saving the index...",
+            )
+
+        else:
+            logging.info("Loading the storage context")
+            storage_context = stream_progress_wrapper(
+                StorageContext.from_defaults,
+                task_str="Loading the storage context...",
+                persist_dir=self.data_dir / LLAMA_INDEX_DIR / self.which_index,
+            )
+
+            logging.info("Loading the pre-processed index")
+            self.index = stream_progress_wrapper(
+                load_index_from_storage,
+                task_str="Loading the pre-processed index...",
+                storage_context=storage_context,
+                settings=settings,
+            )
+
+        self.response_mode = "simple_summarize"
+        if self.mode == "chat":
+            self.chat_engine = {}
+            logging.info("Done setting up Huggingface backend for chat engine.")
+        elif self.mode == "query":
+            self.query_engine = self.index.as_query_engine(
+                response_mode=self.response_mode,
+                similarity_top_k=k,
+            )
+            logging.info("Done setting up Huggingface backend for query engine.")
+
+        self.error_response_template = (
+            "Oh no! When I tried to get a response to your prompt, "
+            "I got the following error:"
+            "\n```\n{}\n```"
+        )
+
+    @staticmethod
+    def _format_sources(response: RESPONSE_TYPE) -> str:
+        """
+        Method to format the sources used to compose the response.
+
+        Parameters
+        ----------
+        response : RESPONSE_TYPE
+            response object from the query engine
+
+        Returns
+        -------
+        str
+            String containing the formatted sources that
+            were used to compose the response
+        """
+        texts = []
+        for source_node in response.source_nodes:
+            # obtain the URL for source
+            try:
+                node_url = source_node.node.extra_info["url"]
+            except KeyError:
+                node_url = source_node.node.extra_info["filename"]
+
+            # add its similarity score and append to texts
+            source_text = node_url + f" (similarity: {round(source_node.score, 2)})"
+            texts.append(source_text)
+
+        result = "I read the following documents to compose this answer:\n"
+        result += "\n\n".join(texts)
+
+        return result
+
+    def _prep_llm(self) -> BaseLLM:
+        """
+        Method to prepare the LLM to be used.
+
+        Returns
+        -------
+        BaseLLM
+            LLM to be used.
+
+        Raises
+        ------
+        NotImplemented
+            This must be implemented by a subclass of LlamaIndex.
+        """
+        raise NotImplementedError(
+            "_prep_llm needs to be implemented by a subclass of LlamaIndex."
+        )
+
+    def _prep_tokenizer(self) -> Callable[[str], int] | None:
+        """
+        Method to prepare the Tokenizer to be used.
+
+        Returns
+        -------
+        Callable[[str], int] | None
+            Tokenizer to use. A callable function on a string.
+            Can also be None if using the default set by LlamaIndex.
+
+        Raises
+        ------
+        NotImplemented
+        """
+        raise NotImplementedError(
+            "_prep_tokenizer needs to be implemented by a subclass of LlamaIndex."
+        )
+
+    def _get_response(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        try:
+            if self.mode == "chat":
+                # create chat engine for user if does not exist
+                if self.chat_engine.get(user_id) is None:
+                    self.chat_engine[user_id] = self.index.as_chat_engine(
+                        chat_mode="context",
+                        response_mode=self.response_mode,
+                        similarity_top_k=self.k,
+                    )
+
+                # obtain chat engine for particular user
+                chat_engine = self.chat_engine[user_id]
+                response = chat_engine.chat(message)
+            elif self.mode == "query":
+                self.query_engine._response_synthesizer._streaming = False
+                response = self.query_engine.query(message)
+
+            # concatenate the response with the resources that it used
+            formatted_response = (
+                response.response + "\n\n\n" + self._format_sources(response)
+            )
+        except Exception as e:  # ignore: broad-except
+            formatted_response = self.error_response_template.format(repr(e))
+
+        pattern = (
+            r"(?s)^Context information is"
+            r".*"
+            r"Given the context information and not prior knowledge, answer the question: "
+            rf"{message}"
+            r"\n(.*)"
+        )
+        m = re.search(pattern, formatted_response)
+
+        if m:
+            answer = m.group(1)
+        else:
+            logging.warning(
+                "Was expecting a backend response with a regular expression but couldn't find a match."
+            )
+            answer = formatted_response
+
+        return MessageResponse(answer)
+
+    def direct_message(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a direct message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        return self._get_response(message=message, user_id=user_id)
+
+    def channel_mention(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a channel mention in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        return self._get_response(message=message, user_id=user_id)
+
+    def stream_message(self, message: str, user_id: str) -> None:
+        """
+        Method to respond to a stream message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        try:
+            if self.mode == "chat":
+                # create chat engine for user if does not exist
+                if self.chat_engine.get(user_id) is None:
+                    self.chat_engine[user_id] = self.index.as_chat_engine(
+                        chat_mode="context",
+                        response_mode=self.response_mode,
+                        similarity_top_k=self.k,
+                        streaming=True,
+                    )
+
+                # obtain chat engine for particular user
+                chat_engine = self.chat_engine[user_id]
+                response_stream = chat_engine.stream_chat(message)
+            elif self.mode == "query":
+                self.query_engine._response_synthesizer._streaming = True
+                response_stream = self.query_engine.query(message)
+
+            for token in stream_iter_progress_wrapper(response_stream.response_gen):
+                print(token, end="", flush=True)
+
+            formatted_response = "\n\n\n" + self._format_sources(response_stream)
+
+            for token in re.split(r"(\s+)", formatted_response):
+                print(token, end="", flush=True)
+        except Exception as e:  # ignore: broad-except
+            for token in re.split(
+                r"(\s+)", self.error_response_template.format(repr(e))
+            ):
+                print(token, end="", flush=True)
diff --git a/reginald/models/llama_index/data_index_creator.py b/reginald/models/llama_index/data_index_creator.py
new file mode 100644
index 00000000..b2fdd483
--- /dev/null
+++ b/reginald/models/llama_index/data_index_creator.py
@@ -0,0 +1,396 @@
+import logging
+import os
+import pathlib
+from tempfile import TemporaryDirectory
+
+import pandas as pd
+from git import Repo
+from httpx import HTTPError
+from llama_index.core import Document, SimpleDirectoryReader, VectorStoreIndex
+from llama_index.core.settings import _Settings
+from llama_index.readers.github import (
+    GithubClient,
+    GitHubIssuesClient,
+    GitHubRepositoryIssuesReader,
+    GithubRepositoryReader,
+)
+
+from reginald.defaults import LLAMA_INDEX_DIR
+from reginald.utils import get_env_var
+
+
+class DataIndexCreator:
+    def __init__(
+        self,
+        data_dir: pathlib.Path | str,
+        which_index: str,
+        settings: _Settings,
+    ) -> None:
+        """
+        Class for creating the data index.
+
+        Parameters
+        ----------
+        data_dir : pathlib.Path | str
+            Path to the data directory.
+        which_index : str
+            Which index to construct (if force_new_index is True) or use.
+            Options are "handbook", "wikis",  "public", "reg" or "all_data".
+        settings : _Settings
+            llama_index.core.settings._Settings object to use to create the index.
+        """
+        self.data_dir: pathlib.Path = pathlib.Path(data_dir)
+        self.which_index: str = which_index
+        self.settings: _Settings = settings
+        self.documents: list[str] = []
+        self.index: VectorStoreIndex | None = None
+
+    def prep_documents(self) -> None:
+        """
+        Method to prepare the documents for the index vector store.
+        """
+        # prep the contextual documents
+        gh_token = get_env_var("GITHUB_TOKEN")
+
+        if gh_token is None:
+            raise ValueError(
+                "Please export your github personal access token as 'GITHUB_TOKEN'."
+            )
+
+        if self.which_index == "handbook":
+            logging.info("Regenerating index only for the handbook")
+
+            # load handbook from repo
+            self._load_handbook(gh_token)
+
+        elif self.which_index == "wikis":
+            logging.info("Regenerating index only for the wikis")
+
+            # load wikis
+            self._load_wikis(gh_token)
+
+        elif self.which_index == "public":
+            logging.info("Regenerating index for all PUBLIC. Will take a long time...")
+
+            # load in scraped turing.ac.uk website
+            self._load_turing_ac_uk()
+
+            # load public data from repos
+            self._load_handbook(gh_token)
+            self._load_rse_course(gh_token)
+            self._load_rds_course(gh_token)
+            self._load_turing_way(gh_token)
+
+        elif self.which_index == "reg":
+            logging.info("Regenerating index for REG. Will take a long time...")
+
+            # load in scraped turing.ac.uk website
+            self._load_turing_ac_uk()
+
+            # load public data from repos
+            self._load_handbook(gh_token)
+
+            # load hut23 data
+            self._load_hut23(gh_token)
+
+            # load wikis
+            self._load_wikis(gh_token)
+
+        elif self.which_index == "all_data":
+            logging.info("Regenerating index for ALL DATA. Will take a long time...")
+
+            # load in scraped turing.ac.uk website
+            self._load_turing_ac_uk()
+
+            # load public data from repos
+            self._load_handbook(gh_token)
+            self._load_rse_course(gh_token)
+            self._load_rds_course(gh_token)
+            self._load_turing_way(gh_token)
+
+            # load hut23 data
+            self._load_hut23(gh_token)
+
+            # load wikis
+            self._load_wikis(gh_token)
+
+        else:
+            logging.info("The which_index provided is unrecognized")
+
+    def _load_turing_ac_uk(self) -> None:
+        """
+        Load in the scraped turing.ac.uk website.
+
+        For 'public' index and 'all_data' index.
+        """
+        data_file = f"{self.data_dir}/public/turingacuk-no-boilerplate.csv"
+        turing_df = pd.read_csv(data_file)
+        turing_df = turing_df[~turing_df.loc[:, "body"].isna()]
+        self.documents += [
+            Document(text=row[1]["body"], extra_info={"url": row[1]["url"]})
+            for row in turing_df.iterrows()
+        ]
+
+    def _load_handbook(self, gh_token: str) -> None:
+        """
+        Load in the REG handbook.
+
+        For 'handbook' index, 'public' index, and 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the handbook repo.
+        """
+        owner = "alan-turing-institute"
+        repo = "REG-handbook"
+
+        handbook_loader = GithubRepositoryReader(
+            GithubClient(gh_token, fail_on_http_error=False),
+            owner=owner,
+            repo=repo,
+            verbose=False,
+            concurrent_requests=1,
+            timeout=60,
+            retries=3,
+            filter_file_extensions=(
+                [".md"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+            filter_directories=(
+                ["content"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+        )
+        self.documents.extend(handbook_loader.load_data(branch="main"))
+
+    def _load_rse_course(self, gh_token: str) -> None:
+        """
+        Load in the REG RSE course.
+
+        For 'public' index and 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the RSE course repo.
+        """
+        owner = "alan-turing-institute"
+        repo = "rse-course"
+
+        rse_course_loader = GithubRepositoryReader(
+            GithubClient(gh_token, fail_on_http_error=False),
+            owner=owner,
+            repo=repo,
+            verbose=False,
+            concurrent_requests=1,
+            timeout=60,
+            retries=3,
+            filter_file_extensions=(
+                [".md", ".ipynb"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+        )
+        self.documents.extend(rse_course_loader.load_data(branch="main"))
+
+    def _load_rds_course(self, gh_token: str) -> None:
+        """
+        Load in REG RDS course.
+
+        For 'public' index and 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the RDS course repo.
+        """
+        owner = "alan-turing-institute"
+        repo = "rds-course"
+
+        rds_course_loader = GithubRepositoryReader(
+            GithubClient(gh_token, fail_on_http_error=False),
+            owner=owner,
+            repo=repo,
+            verbose=False,
+            concurrent_requests=1,
+            timeout=60,
+            retries=3,
+            filter_file_extensions=(
+                [".md", ".ipynb"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+        )
+        self.documents.extend(rds_course_loader.load_data(branch="develop"))
+
+    def _load_turing_way(self, gh_token: str) -> None:
+        """
+        Load in the Turing Way.
+
+        For 'public' index and 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the Turing Way repo.
+        """
+        owner = "the-turing-way"
+        repo = "the-turing-way"
+
+        turing_way_loader = GithubRepositoryReader(
+            GithubClient(gh_token, fail_on_http_error=False),
+            owner=owner,
+            repo=repo,
+            verbose=False,
+            concurrent_requests=1,
+            timeout=60,
+            retries=3,
+            filter_file_extensions=(
+                [".md"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+        )
+        self.documents.extend(turing_way_loader.load_data(branch="main"))
+
+    def _load_hut23(self, gh_token: str) -> None:
+        """
+        Load in documents from the Hut23 repo.
+
+        For 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the Hut23 repo.
+        """
+        owner = "alan-turing-institute"
+        repo = "Hut23"
+
+        # load repo
+        hut23_repo_loader = GithubRepositoryReader(
+            GithubClient(gh_token, fail_on_http_error=False),
+            owner=owner,
+            repo=repo,
+            verbose=False,
+            concurrent_requests=1,
+            timeout=60,
+            retries=3,
+            filter_file_extensions=(
+                [".md", ".ipynb"],
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+            filter_directories=(
+                [
+                    "JDs",
+                    "development",
+                    "newsletters",
+                    "objectives",
+                    "project-appraisal",
+                    "rfc",
+                    "team-meetings",
+                ],  # we can adjust these
+                GithubRepositoryReader.FilterType.INCLUDE,
+            ),
+        )
+        self.documents.extend(hut23_repo_loader.load_data(branch="main"))
+
+        try:
+            # load_issues
+            hut23_issues_loader = GitHubRepositoryIssuesReader(
+                GitHubIssuesClient(gh_token),
+                owner=owner,
+                repo=repo,
+                verbose=True,
+            )
+
+            issue_docs = hut23_issues_loader.load_data()
+            for doc in issue_docs:
+                doc.metadata["api_url"] = str(doc.metadata["url"])
+                doc.metadata["url"] = doc.metadata["source"]
+            self.documents.extend(issue_docs)
+
+        except HTTPError as e:
+            logging.error(f"Failed to load Hut23 issues: {e}")
+
+        # load collaborators
+        # hut23_collaborators_loader = GitHubRepositoryCollaboratorsReader(
+        #     GitHubCollaboratorsClient(gh_token),
+        #     owner=owner,
+        #     repo=repo,
+        #     verbose=True,
+        # )
+        # self.documents.extend(hut23_collaborators_loader.load_data())
+
+    def _load_wikis(self, gh_token: str) -> None:
+        """
+        Load in documents from the wikis.
+
+        For 'wikis' index and 'all_data' index.
+
+        Parameters
+        ----------
+        gh_token : str
+            Github token to use to access the research-engineering-group
+            and Hut23 repo wikis.
+        """
+        wiki_urls = [
+            f"https://oauth2:{gh_token}@github.com/alan-turing-institute/research-engineering-group.wiki.git",
+            f"https://oauth2:{gh_token}@github.com/alan-turing-institute/Hut23.wiki.git",
+        ]
+
+        for url in wiki_urls:
+            temp_dir = TemporaryDirectory()
+            wiki_path = os.path.join(temp_dir.name, url.split("/")[-1])
+
+            _ = Repo.clone_from(url, wiki_path)
+
+            reader = SimpleDirectoryReader(
+                input_dir=wiki_path,
+                required_exts=[".md"],
+                recursive=True,
+                filename_as_id=True,
+            )
+
+            # get base url and file names
+            base_url = url.removesuffix(".wiki.git")
+            fnames = [str(file) for file in reader.input_files]
+
+            # get file urls and create dictionary to map fname to url
+            file_urls = [
+                os.path.join(base_url, "wiki", fname.split("/")[-1].removesuffix(".md"))
+                for fname in fnames
+            ]
+            file_urls_dict = {
+                fname: file_url for fname, file_url in zip(fnames, file_urls)
+            }
+
+            def get_urls(fname):
+                return {"url": file_urls_dict.get(fname)}
+
+            # add `get_urls` function to reader
+            reader.file_metadata = get_urls
+
+            self.documents.extend(reader.load_data())
+
+    def create_index(self) -> VectorStoreIndex:
+        """
+        Create the index vector store.
+        """
+        # obtain documents
+        logging.info(f"Preparing documents for {self.which_index} index...")
+        self.prep_documents()
+
+        # create index
+        logging.info("Creating index...")
+        self.index = VectorStoreIndex.from_documents(
+            self.documents, settings=self.settings
+        )
+
+        return self.index
+
+    def save_index(self, directory: pathlib.Path | None = None) -> None:
+        if directory is None:
+            directory = self.data_dir / LLAMA_INDEX_DIR / self.which_index
+
+        # save the settings and persist the index
+        logging.info(f"Saving the index in {directory}...")
+        self.index.storage_context.persist(persist_dir=directory)
diff --git a/reginald/models/models/llama_utils.py b/reginald/models/llama_index/llama_cpp_template.py
similarity index 93%
rename from reginald/models/models/llama_utils.py
rename to reginald/models/llama_index/llama_cpp_template.py
index 372f8856..8b00af53 100644
--- a/reginald/models/models/llama_utils.py
+++ b/reginald/models/llama_index/llama_cpp_template.py
@@ -2,7 +2,7 @@
 
 from llama_index.core.llms import ChatMessage, MessageRole
 
-BOS, EOS = "<s>", "</s>"
+BOS, EOS = "", ""  # not needed as LlamaCPP handles adding these
 B_INST, E_INST = "[INST]", "[/INST]"
 B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"  # use for Llama2
 # B_SYS, E_SYS = "", "\n\n" # use for Mistral
@@ -11,7 +11,7 @@
 Always answer as helpfully as possible and follow ALL given instructions. \
 Do not speculate or make up information. \
 Do not reference any given instructions or context. \
-If the content is not relevant, just ignore it and provide a helpful \
+If the content is not relevant to the question, just ignore it and provide a helpful \
 response without mentioning the context. \
 """
 
diff --git a/reginald/models/llama_index/llama_index.py b/reginald/models/llama_index/llama_index.py
new file mode 100644
index 00000000..7a6945e5
--- /dev/null
+++ b/reginald/models/llama_index/llama_index.py
@@ -0,0 +1,370 @@
+from __future__ import annotations
+
+import logging
+import pathlib
+import re
+import sys
+
+import nest_asyncio
+from llama_index.core import StorageContext, VectorStoreIndex, load_index_from_storage
+from llama_index.core.base.llms.base import BaseLLM
+from llama_index.core.base.response.schema import RESPONSE_TYPE
+
+from reginald.models.base import MessageResponse, ResponseModel
+from reginald.utils import stream_iter_progress_wrapper, stream_progress_wrapper
+
+nest_asyncio.apply()
+
+
+from reginald.defaults import LLAMA_INDEX_DIR
+from reginald.models.llama_index.data_index_creator import DataIndexCreator
+from reginald.models.llama_index.llama_utils import (
+    compute_default_chunk_size,
+    setup_settings,
+)
+
+
+class LlamaIndex(ResponseModel):
+    def __init__(
+        self,
+        model_name: str,
+        max_input_size: int,
+        data_dir: pathlib.Path | str,
+        which_index: str,
+        mode: str = "chat",
+        k: int = 3,
+        chunk_size: int | None = None,
+        chunk_overlap_ratio: float = 0.1,
+        num_output: int = 512,
+        force_new_index: bool = False,
+        *args,
+        **kwargs,
+    ) -> None:
+        """
+        Base class for models using llama-index.
+        This class is not intended to be used directly, but rather subclassed
+        to implement the `_prep_llm` method which constructs the LLM to be used.
+
+        Parameters
+        ----------
+        model_name : str
+            Model name to specify which LLM to use.
+        max_input_size : int
+            Context window size for the LLM.
+        data_dir : pathlib.Path | str
+            Path to the data directory.
+        which_index : str
+            Which index to construct (if force_new_index is True) or use.
+            Options are "handbook", "wikis",  "public", "reg" or "all_data".
+        mode : Optional[str], optional
+            The type of engine to use when interacting with the data, options of "chat" or "query".
+            Default is "chat".
+        k : int, optional
+            `similarity_top_k` to use in chat or query engine, by default 3
+        chunk_size : int | None, optional
+            Maximum size of chunks to use, by default None.
+            If None, this is computed as `ceil(max_input_size / k)`.
+        chunk_overlap_ratio : float, optional
+            Chunk overlap as a ratio of chunk size, by default 0.1
+        num_output : int, optional
+            Number of outputs for the LLM, by default 512
+        force_new_index : bool, optional
+            Whether or not to recreate the index vector store,
+            by default False
+        """
+        super().__init__(*args, emoji="llama", **kwargs)
+        logging.info("Setting up Huggingface backend.")
+        if mode == "chat":
+            logging.info("Setting up chat engine.")
+        elif mode == "query":
+            logging.info("Setting up query engine.")
+        else:
+            logging.error("Mode must either be 'query' or 'chat'.")
+            sys.exit(1)
+
+        self.max_input_size = max_input_size
+        self.model_name = model_name
+        self.num_output = num_output
+        self.mode = mode
+        self.k = k
+        self.chunk_size = chunk_size or compute_default_chunk_size(
+            max_input_size=max_input_size, k=k
+        )
+        self.chunk_overlap_ratio = chunk_overlap_ratio
+        self.data_dir = pathlib.Path(data_dir)
+        self.which_index = which_index
+        self.documents = []
+
+        # set up LLM
+        llm = self._prep_llm()
+
+        # set up settings
+        settings = setup_settings(
+            llm=llm,
+            max_input_size=self.max_input_size,
+            num_output=self.num_output,
+            chunk_size=self.chunk_size,
+            chunk_overlap_ratio=self.chunk_overlap_ratio,
+            k=self.k,
+            tokenizer=self._prep_tokenizer(),
+        )
+
+        if force_new_index:
+            logging.info("Generating the index from scratch...")
+            data_creator = DataIndexCreator(
+                which_index=self.which_index,
+                data_dir=self.data_dir,
+                settings=settings,
+            )
+            self.index: VectorStoreIndex = stream_progress_wrapper(
+                data_creator.create_index,
+                task_str="Generating the index from scratch...",
+            )
+            stream_progress_wrapper(
+                data_creator.save_index,
+                task_str="Saving the index...",
+            )
+
+        else:
+            logging.info("Loading the storage context")
+            storage_context = stream_progress_wrapper(
+                StorageContext.from_defaults,
+                task_str="Loading the storage context...",
+                persist_dir=self.data_dir / LLAMA_INDEX_DIR / self.which_index,
+            )
+
+            logging.info("Loading the pre-processed index")
+            self.index = stream_progress_wrapper(
+                load_index_from_storage,
+                task_str="Loading the pre-processed index...",
+                storage_context=storage_context,
+                settings=settings,
+            )
+
+        self.response_mode = "simple_summarize"
+        if self.mode == "chat":
+            self.chat_engine = {}
+            logging.info("Done setting up Huggingface backend for chat engine.")
+        elif self.mode == "query":
+            self.query_engine = self.index.as_query_engine(
+                response_mode=self.response_mode,
+                similarity_top_k=k,
+            )
+            logging.info("Done setting up Huggingface backend for query engine.")
+
+        self.error_response_template = (
+            "Oh no! When I tried to get a response to your prompt, "
+            "I got the following error:"
+            "\n```\n{}\n```"
+        )
+
+    @staticmethod
+    def _format_sources(response: RESPONSE_TYPE) -> str:
+        """
+        Method to format the sources used to compose the response.
+
+        Parameters
+        ----------
+        response : RESPONSE_TYPE
+            response object from the query engine
+
+        Returns
+        -------
+        str
+            String containing the formatted sources that
+            were used to compose the response
+        """
+        texts = []
+        for source_node in response.source_nodes:
+            # obtain the URL for source
+            try:
+                node_url = source_node.node.extra_info["url"]
+            except KeyError:
+                node_url = source_node.node.extra_info["filename"]
+
+            # add its similarity score and append to texts
+            source_text = node_url + f" (similarity: {round(source_node.score, 2)})"
+            texts.append(source_text)
+
+        result = "I read the following documents to compose this answer:\n"
+        result += "\n\n".join(texts)
+
+        return result
+
+    def _prep_llm(self) -> BaseLLM:
+        """
+        Method to prepare the LLM to be used.
+
+        Returns
+        -------
+        BaseLLM
+            LLM to be used.
+
+        Raises
+        ------
+        NotImplemented
+            This must be implemented by a subclass of LlamaIndex.
+        """
+        raise NotImplementedError(
+            "_prep_llm needs to be implemented by a subclass of LlamaIndex."
+        )
+
+    def _prep_tokenizer(self) -> callable[str] | None:
+        """
+        Method to prepare the Tokenizer to be used.
+
+        Returns
+        -------
+        callable[str] | None
+            Tokenizer to use. A callable function on a string.
+            Can also be None if using the default set by LlamaIndex.
+
+        Raises
+        ------
+        NotImplemented
+        """
+        raise NotImplementedError(
+            "_prep_tokenizer needs to be implemented by a subclass of LlamaIndex."
+        )
+
+    def _get_response(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        try:
+            if self.mode == "chat":
+                # create chat engine for user if does not exist
+                if self.chat_engine.get(user_id) is None:
+                    self.chat_engine[user_id] = self.index.as_chat_engine(
+                        chat_mode="context",
+                        response_mode=self.response_mode,
+                        similarity_top_k=self.k,
+                    )
+
+                # obtain chat engine for particular user
+                chat_engine = self.chat_engine[user_id]
+                response = chat_engine.chat(message)
+            elif self.mode == "query":
+                self.query_engine._response_synthesizer._streaming = False
+                response = self.query_engine.query(message)
+
+            # concatenate the response with the resources that it used
+            formatted_response = (
+                response.response + "\n\n\n" + self._format_sources(response)
+            )
+        except Exception as e:  # ignore: broad-except
+            formatted_response = self.error_response_template.format(repr(e))
+
+        pattern = (
+            r"(?s)^Context information is"
+            r".*"
+            r"Given the context information and not prior knowledge, answer the question: "
+            rf"{message}"
+            r"\n(.*)"
+        )
+        m = re.search(pattern, formatted_response)
+
+        if m:
+            answer = m.group(1)
+        else:
+            logging.warning(
+                "Was expecting a backend response with a regular expression but couldn't find a match."
+            )
+            answer = formatted_response
+
+        return MessageResponse(answer)
+
+    def direct_message(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a direct message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        return self._get_response(message=message, user_id=user_id)
+
+    def channel_mention(self, message: str, user_id: str) -> MessageResponse:
+        """
+        Method to respond to a channel mention in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        return self._get_response(message=message, user_id=user_id)
+
+    def stream_message(self, message: str, user_id: str) -> None:
+        """
+        Method to respond to a stream message in Slack.
+
+        Parameters
+        ----------
+        msg_in : str
+            Message from user
+        user_id : str
+            User ID
+
+        Returns
+        -------
+        MessageResponse
+            Response from the query engine.
+        """
+        try:
+            if self.mode == "chat":
+                # create chat engine for user if does not exist
+                if self.chat_engine.get(user_id) is None:
+                    self.chat_engine[user_id] = self.index.as_chat_engine(
+                        chat_mode="context",
+                        response_mode=self.response_mode,
+                        similarity_top_k=self.k,
+                        streaming=True,
+                    )
+
+                # obtain chat engine for particular user
+                chat_engine = self.chat_engine[user_id]
+                response_stream = chat_engine.stream_chat(message)
+            elif self.mode == "query":
+                self.query_engine._response_synthesizer._streaming = True
+                response_stream = self.query_engine.query(message)
+
+            for token in stream_iter_progress_wrapper(response_stream.response_gen):
+                print(token, end="", flush=True)
+
+            formatted_response = "\n\n\n" + self._format_sources(response_stream)
+
+            for token in re.split(r"(\s+)", formatted_response):
+                print(token, end="", flush=True)
+        except Exception as e:  # ignore: broad-except
+            for token in re.split(
+                r"(\s+)", self.error_response_template.format(repr(e))
+            ):
+                print(token, end="", flush=True)
diff --git a/reginald/models/llama_index/llama_index_azure_openai.py b/reginald/models/llama_index/llama_index_azure_openai.py
new file mode 100644
index 00000000..37c794a4
--- /dev/null
+++ b/reginald/models/llama_index/llama_index_azure_openai.py
@@ -0,0 +1,68 @@
+import logging
+from typing import Any
+
+from llama_index.core import set_global_tokenizer
+from llama_index.llms.azure_openai import AzureOpenAI
+
+from reginald.models.llama_index.base import LlamaIndex
+from reginald.utils import get_env_var
+
+
+class LlamaIndexGPTAzure(LlamaIndex):
+    def __init__(
+        self, model_name: str = "reginald-gpt4", *args: Any, **kwargs: Any
+    ) -> None:
+        """
+         `LlamaIndexGPTAzure` is a subclass of `LlamaIndex` that uses Azure's
+        instance of OpenAI's LLMs to implement the LLM.
+
+        Must have the following environment variables set:
+        - `OPENAI_API_BASE`: Azure endpoint which looks
+          like https://YOUR_RESOURCE_NAME.openai.azure.com/
+        - `OPENAI_API_KEY`: Azure API key
+
+        Parameters
+        ----------
+        model_name : str, optional
+            The deployment name of the model, by default "reginald-gpt4"
+        """
+        openai_azure_api_base = get_env_var("OPENAI_AZURE_API_BASE", secret_value=False)
+        if openai_azure_api_base is None:
+            raise ValueError(
+                "You must set OPENAI_AZURE_API_BASE to your Azure endpoint. "
+                "It should look like https://YOUR_RESOURCE_NAME.openai.azure.com/"
+            )
+
+        openai_azure_api_key = get_env_var("OPENAI_AZURE_API_KEY")
+        if openai_azure_api_key is None:
+            raise ValueError("You must set OPENAI_AZURE_API_KEY for Azure OpenAI.")
+
+        # deployment name can be found in the Azure AI Studio portal
+        self.deployment_name = model_name
+        self.openai_api_base = openai_azure_api_base
+        self.openai_api_key = openai_azure_api_key
+        self.openai_api_version = "2023-09-15-preview"
+        self.temperature = 0.7
+        super().__init__(*args, model_name="gpt-4", **kwargs)
+
+    def _prep_llm(self) -> AzureOpenAI:
+        logging.info(f"Setting up AzureOpenAI LLM (model {self.deployment_name})")
+        return AzureOpenAI(
+            model=self.model_name,
+            engine=self.deployment_name,
+            temperature=self.temperature,
+            max_tokens=self.num_output,
+            api_key=self.openai_api_key,
+            api_base=self.openai_api_base,
+            api_type="azure",
+            azure_endpoint=self.openai_api_base,
+            api_version=self.openai_api_version,
+        )
+
+    def _prep_tokenizer(self) -> None:
+        import tiktoken
+
+        logging.info(f"Setting up tiktoken tokenizer for model {self.model_name}")
+        tokenizer = tiktoken.encoding_for_model("gpt-4").encode
+        set_global_tokenizer(tokenizer)
+        return tokenizer
diff --git a/reginald/models/llama_index/llama_index_hf.py b/reginald/models/llama_index/llama_index_hf.py
new file mode 100644
index 00000000..a602f42e
--- /dev/null
+++ b/reginald/models/llama_index/llama_index_hf.py
@@ -0,0 +1,55 @@
+import logging
+from typing import Any, Callable
+
+from llama_index.core import set_global_tokenizer
+from llama_index.llms.huggingface import HuggingFaceLLM
+from transformers import AutoTokenizer
+
+from reginald.models.llama_index.base import LlamaIndex
+
+
+class LlamaIndexHF(LlamaIndex):
+    def __init__(
+        self,
+        model_name: str = "google/gemma-2b-it",
+        device: str = "auto",
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        `LlamaIndexHF` is a subclass of `LlamaIndex` that uses HuggingFace's
+        `transformers` library to implement the LLM.
+
+        Parameters
+        ----------
+        model_name : str, optional
+            Model name from Huggingface's model hub,
+            by default "google/gemma-2b-it".
+        device : str, optional
+            Device map to use for the LLM, by default "auto".
+        """
+        self.device = device
+        super().__init__(*args, model_name=model_name, **kwargs)
+
+    def _prep_llm(self) -> HuggingFaceLLM:
+        logging.info(
+            f"Setting up Huggingface LLM (model {self.model_name}) on device {self.device}"
+        )
+        logging.info(
+            f"HF-args: (context_window: {self.max_input_size}, num_output: {self.num_output})"
+        )
+
+        return HuggingFaceLLM(
+            context_window=self.max_input_size,
+            max_new_tokens=self.num_output,
+            generate_kwargs={"temperature": 0.1, "do_sample": False},
+            tokenizer_name=self.model_name,
+            model_name=self.model_name,
+            device_map=self.device or "auto",
+        )
+
+    def _prep_tokenizer(self) -> Callable[[str], int]:
+        logging.info(f"Setting up Huggingface tokenizer for model {self.model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name).encode
+        set_global_tokenizer(tokenizer)
+        return tokenizer
diff --git a/reginald/models/llama_index/llama_index_llama_cpp.py b/reginald/models/llama_index/llama_index_llama_cpp.py
new file mode 100644
index 00000000..c5f3d00d
--- /dev/null
+++ b/reginald/models/llama_index/llama_index_llama_cpp.py
@@ -0,0 +1,71 @@
+import logging
+from typing import Any, Callable
+
+from llama_index.core import set_global_tokenizer
+from llama_index.llms.llama_cpp import LlamaCPP
+from tiktoken import encoding_for_model
+
+from reginald.models.llama_index.base import LlamaIndex
+from reginald.models.llama_index.llama_cpp_template import (
+    completion_to_prompt,
+    messages_to_prompt,
+)
+
+
+class LlamaIndexLlamaCPP(LlamaIndex):
+    def __init__(
+        self,
+        model_name: str,
+        is_path: bool,
+        n_gpu_layers: int = 0,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        `LlamaIndexLlamaCPP` is a subclass of `LlamaIndex` that uses
+        llama-cpp to implement the LLM.
+
+        Parameters
+        ----------
+        model_name : str
+            Either the path to the model or the URL to download the model from
+        is_path : bool, optional
+            If True, model_name is used as a path to the model file,
+            otherwise it should be the URL to download the model
+        n_gpu_layers : int, optional
+            Number of layers to offload to GPU.
+            If -1, all layers are offloaded, by default 0
+        """
+        self.is_path = is_path
+        self.n_gpu_layers = n_gpu_layers
+        super().__init__(*args, model_name=model_name, **kwargs)
+
+    def _prep_llm(self) -> LlamaCPP:
+        logging.info(
+            f"Setting up LlamaCPP LLM (model {self.model_name}) on {self.n_gpu_layers} GPU layers"
+        )
+        logging.info(
+            f"LlamaCPP-args: (context_window: {self.max_input_size}, num_output: {self.num_output})"
+        )
+
+        return LlamaCPP(
+            model_url=self.model_name if not self.is_path else None,
+            model_path=self.model_name if self.is_path else None,
+            temperature=0.1,
+            max_new_tokens=self.num_output,
+            context_window=self.max_input_size,
+            # kwargs to pass to __call__()
+            generate_kwargs={},
+            # kwargs to pass to __init__()
+            model_kwargs={"n_gpu_layers": self.n_gpu_layers},
+            messages_to_prompt=messages_to_prompt,
+            completion_to_prompt=completion_to_prompt,
+            verbose=True,
+        )
+
+    def _prep_tokenizer(self) -> Callable[[str], int]:
+        # NOTE: this should depend on the model used, but hard coding tiktoken for now
+        logging.info("Setting up tiktoken gpt-4 tokenizer")
+        tokenizer = encoding_for_model("gpt-4").encode
+        set_global_tokenizer(tokenizer)
+        return tokenizer
diff --git a/reginald/models/llama_index/llama_index_ollama.py b/reginald/models/llama_index/llama_index_ollama.py
new file mode 100644
index 00000000..b06b4a8e
--- /dev/null
+++ b/reginald/models/llama_index/llama_index_ollama.py
@@ -0,0 +1,47 @@
+import logging
+from typing import Any, Callable
+
+from llama_index.core import set_global_tokenizer
+from llama_index.llms.ollama import Ollama
+from tiktoken import encoding_for_model
+
+from reginald.models.llama_index.base import LlamaIndex
+from reginald.utils import get_env_var
+
+
+class LlamaIndexOllama(LlamaIndex):
+    def __init__(
+        self,
+        model_name: str,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        `LlamaIndexOllama` is a subclass of `LlamaIndex` that uses
+        ollama to run inference on the LLM.
+
+        Parameters
+        ----------
+        model_name : str
+            The Ollama model to use
+        """
+        ollama_api_endpoint = get_env_var("OLLAMA_API_ENDPOINT")
+        if ollama_api_endpoint is None:
+            raise ValueError("You must set OLLAMA_API_ENDPOINT for Ollama.")
+        self.ollama_api_endpoint = ollama_api_endpoint
+        super().__init__(*args, model_name=model_name, **kwargs)
+
+    def _prep_llm(self) -> Ollama:
+        logging.info(f"Setting up Ollama (model {self.model_name})")
+        return Ollama(
+            base_url=self.ollama_api_endpoint,
+            model=self.model_name,
+            request_timeout=60,
+        )
+
+    def _prep_tokenizer(self) -> Callable[[str], int]:
+        # NOTE: this should depend on the model used, but hard coding tiktoken for now
+        logging.info("Setting up tiktoken gpt-4 tokenizer")
+        tokenizer = encoding_for_model("gpt-4").encode
+        set_global_tokenizer(tokenizer)
+        return tokenizer
diff --git a/reginald/models/llama_index/llama_index_openai.py b/reginald/models/llama_index/llama_index_openai.py
new file mode 100644
index 00000000..8633fff5
--- /dev/null
+++ b/reginald/models/llama_index/llama_index_openai.py
@@ -0,0 +1,50 @@
+import logging
+from typing import Any
+
+from llama_index.core import set_global_tokenizer
+from llama_index.llms.openai import OpenAI
+
+from reginald.models.llama_index.base import LlamaIndex
+from reginald.utils import get_env_var
+
+
+class LlamaIndexGPTOpenAI(LlamaIndex):
+    def __init__(
+        self, model_name: str = "gpt-3.5-turbo", *args: Any, **kwargs: Any
+    ) -> None:
+        """
+        `LlamaIndexGPTOpenAI` is a subclass of `LlamaIndex` that uses OpenAI's
+        API to implement the LLM.
+
+        Must have `OPENAI_API_KEY` set as an environment variable.
+
+        Parameters
+        ----------
+        model_name : str, optional
+            The model to use from the OpenAI API, by default "gpt-3.5-turbo"
+        """
+        openai_api_key = get_env_var("OPENAI_API_KEY")
+        if openai_api_key is None:
+            raise ValueError("You must set OPENAI_API_KEY for OpenAI.")
+
+        self.model_name = model_name
+        self.openai_api_key = openai_api_key
+        self.temperature = 0.7
+        super().__init__(*args, model_name=self.model_name, **kwargs)
+
+    def _prep_llm(self) -> OpenAI:
+        logging.info(f"Setting up OpenAI LLM (model {self.model_name})")
+        return OpenAI(
+            model=self.model_name,
+            temperature=self.temperature,
+            max_tokens=self.num_output,
+            api_key=self.openai_api_key,
+        )
+
+    def _prep_tokenizer(self) -> None:
+        import tiktoken
+
+        logging.info(f"Setting up tiktoken tokenizer for model {self.model_name}")
+        tokenizer = tiktoken.encoding_for_model(self.model_name).encode
+        set_global_tokenizer(tokenizer)
+        return tokenizer
diff --git a/reginald/models/llama_index/llama_utils.py b/reginald/models/llama_index/llama_utils.py
new file mode 100644
index 00000000..65e5a593
--- /dev/null
+++ b/reginald/models/llama_index/llama_utils.py
@@ -0,0 +1,119 @@
+import logging
+from math import ceil
+from typing import Callable
+
+from llama_index.core import PromptHelper, Settings
+from llama_index.core.base.llms.base import BaseLLM
+from llama_index.core.settings import _Settings
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+
+def compute_default_chunk_size(max_input_size: int, k: int) -> int:
+    """
+    Compute the default chunk size to use for the index vector store.
+
+    Parameters
+    ----------
+    max_input_size : int
+        Maximum input size for the LLM.
+    k : int
+        `similarity_top_k` to use in chat or query engine.
+
+    Returns
+    -------
+    int
+        Default chunk size to use for the index vector store.
+    """
+    return ceil(max_input_size / (k + 1))
+
+
+def setup_settings(
+    llm: BaseLLM,
+    max_input_size: int | str,
+    num_output: int | str,
+    chunk_overlap_ratio: float | str,
+    chunk_size: int | str | None = None,
+    k: int | str | None = None,
+    tokenizer: Callable[[str], int] | None = None,
+) -> _Settings:
+    """
+    Helper function to set up the settings.
+    Can pass in either chunk_size or k.
+    If chunk_size is not provided, it is computed as
+    `ceil(max_input_size / k)`.
+    If chunk_size is provided, k is ignored.
+
+    Parameters
+    ----------
+    llm : BaseLLM
+        LLM to use to create the index vectors.
+    max_input_size : int | str
+        Context window size for the LLM.
+    num_output : int, optional
+        Number of outputs for the LLM.
+    chunk_overlap_ratio : float, optional
+        Chunk overlap as a ratio of chunk size._
+    chunk_size : int | None, optional
+        Maximum size of chunks to use, by default None.
+        If None, this is computed as `ceil(max_input_size / k)`.
+    k : int | str | None, optional
+        `similarity_top_k` to use in chat or query engine,
+        by default None
+    tokenizer: Callable[[str], int] | None, optional
+        Tokenizer to use. A callable function on a string.
+        Can also be None if using the default set by LlamaIndex.
+
+    Returns
+    -------
+    Settings
+        _Settings object to use to create the index vectors.
+    """
+    if chunk_size is None and k is None:
+        raise ValueError("Either chunk_size or k must be provided.")
+
+    # convert to int or float if necessary
+    if isinstance(max_input_size, str):
+        max_input_size = int(max_input_size)
+    if isinstance(num_output, str):
+        num_output = int(num_output)
+    if isinstance(chunk_overlap_ratio, str):
+        chunk_overlap_ratio = float(chunk_overlap_ratio)
+    if isinstance(chunk_size, str):
+        chunk_size = int(chunk_size)
+    if isinstance(k, str):
+        k = int(k)
+
+    # if chunk_size is not provided, compute a default value
+    chunk_size = chunk_size or compute_default_chunk_size(
+        max_input_size=max_input_size, k=k
+    )
+
+    # initialise embedding model to use to create the index vectors
+    embed_model = HuggingFaceEmbedding(
+        model_name="sentence-transformers/all-mpnet-base-v2",
+        embed_batch_size=128,
+    )
+
+    # construct the prompt helper
+    prompt_helper = PromptHelper(
+        context_window=max_input_size,
+        num_output=num_output,
+        chunk_size_limit=chunk_size,
+        chunk_overlap_ratio=chunk_overlap_ratio,
+        tokenizer=tokenizer,
+    )
+
+    # construct the settings (and logging the settings set)
+    Settings.llm = llm
+    logging.info(f"Settings llm: {llm}")
+    Settings.embed_model = embed_model
+    logging.info(f"Settings embed_model: {embed_model}")
+    logging.info(f"Embedding model initialised on device {embed_model._device}")
+    Settings.prompt_helper = prompt_helper
+    logging.info(f"Settings prompt_helper: {prompt_helper}")
+    Settings.chunk_size = chunk_size
+    logging.info(f"Settings chunk_size: {chunk_size}")
+    Settings.tokenizer = tokenizer
+    logging.info(f"Settings tokenizer: {tokenizer}")
+
+    return Settings
diff --git a/reginald/models/models/__init__.py b/reginald/models/models/__init__.py
deleted file mode 100644
index d018a5c4..00000000
--- a/reginald/models/models/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from reginald.models.models.chat_completion import (
-    ChatCompletionAzure,
-    ChatCompletionOpenAI,
-)
-from reginald.models.models.hello import Hello
-from reginald.models.models.llama_index import (
-    LlamaIndexGPTAzure,
-    LlamaIndexGPTOpenAI,
-    LlamaIndexHF,
-    LlamaIndexLlamaCPP,
-    LlamaIndexOllama,
-)
-
-# Please ensure that any models needing OPENAI_API_KEY are named *openai*
-# Please ensure that any models needing OPENAI_AZURE_API_BASE and OPENAI_AZURE_API_KEY are named *azure*
-MODELS = {
-    "chat-completion-azure": ChatCompletionAzure,
-    "chat-completion-openai": ChatCompletionOpenAI,
-    "hello": Hello,
-    "llama-index-ollama": LlamaIndexOllama,
-    "llama-index-llama-cpp": LlamaIndexLlamaCPP,
-    "llama-index-hf": LlamaIndexHF,
-    "llama-index-gpt-azure": LlamaIndexGPTAzure,
-    "llama-index-gpt-openai": LlamaIndexGPTOpenAI,
-}
-
-DEFAULTS = {
-    "chat-completion-azure": "reginald-gpt4",
-    "chat-completion-openai": "gpt-3.5-turbo",
-    "hello": None,
-    "llama-index-ollama": "llama3",
-    "llama-index-llama-cpp": "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q6_K.gguf",
-    "llama-index-hf": "microsoft/phi-1_5",
-    "llama-index-gpt-azure": "reginald-gpt4",
-    "llama-index-gpt-openai": "gpt-3.5-turbo",
-}
-
-__all__ = ["MODELS", "DEFAULTS"]
diff --git a/reginald/models/models/llama_index.py b/reginald/models/models/llama_index.py
deleted file mode 100644
index 3d08355b..00000000
--- a/reginald/models/models/llama_index.py
+++ /dev/null
@@ -1,1136 +0,0 @@
-from __future__ import annotations
-
-import logging
-import os
-import pathlib
-import re
-import sys
-from math import ceil
-from tempfile import TemporaryDirectory
-from typing import Any
-
-import nest_asyncio
-import pandas as pd
-import tiktoken
-from git import Repo
-from httpx import HTTPError
-from llama_index.core import (
-    Document,
-    PromptHelper,
-    PromptTemplate,
-    Settings,
-    SimpleDirectoryReader,
-    StorageContext,
-    VectorStoreIndex,
-    load_index_from_storage,
-    set_global_tokenizer,
-)
-from llama_index.core.base.llms.base import BaseLLM
-from llama_index.core.base.response.schema import RESPONSE_TYPE
-from llama_index.core.settings import _Settings
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.llms.azure_openai import AzureOpenAI
-from llama_index.llms.huggingface import HuggingFaceLLM
-from llama_index.llms.llama_cpp import LlamaCPP
-from llama_index.llms.ollama import Ollama
-from llama_index.llms.openai import OpenAI
-from llama_index.readers.github import (
-    GithubClient,
-    GitHubIssuesClient,
-    GitHubRepositoryIssuesReader,
-    GithubRepositoryReader,
-)
-from transformers import AutoTokenizer
-
-from reginald.models.models.base import MessageResponse, ResponseModel
-from reginald.models.models.llama_utils import completion_to_prompt, messages_to_prompt
-from reginald.utils import (
-    get_env_var,
-    stream_iter_progress_wrapper,
-    stream_progress_wrapper,
-)
-
-nest_asyncio.apply()
-
-
-LLAMA_INDEX_DIR = "llama_index_indices"
-
-
-def compute_default_chunk_size(max_input_size: int, k: int) -> int:
-    """
-    Compute the default chunk size to use for the index vector store.
-
-    Parameters
-    ----------
-    max_input_size : int
-        Maximum input size for the LLM.
-    k : int
-        `similarity_top_k` to use in chat or query engine.
-
-    Returns
-    -------
-    int
-        Default chunk size to use for the index vector store.
-    """
-    return ceil(max_input_size / (k + 1))
-
-
-def setup_settings(
-    llm: BaseLLM,
-    max_input_size: int | str,
-    num_output: int | str,
-    chunk_overlap_ratio: float | str,
-    chunk_size: int | str | None = None,
-    k: int | str | None = None,
-    tokenizer: callable[str] | None = None,
-) -> _Settings:
-    """
-    Helper function to set up the settings.
-    Can pass in either chunk_size or k.
-    If chunk_size is not provided, it is computed as
-    `ceil(max_input_size / k)`.
-    If chunk_size is provided, k is ignored.
-
-    Parameters
-    ----------
-    llm : BaseLLM
-        LLM to use to create the index vectors.
-    max_input_size : int | str
-        Context window size for the LLM.
-    num_output : int, optional
-        Number of outputs for the LLM.
-    chunk_overlap_ratio : float, optional
-        Chunk overlap as a ratio of chunk size._
-    chunk_size : int | None, optional
-        Maximum size of chunks to use, by default None.
-        If None, this is computed as `ceil(max_input_size / k)`.
-    k : int | str | None, optional
-        `similarity_top_k` to use in chat or query engine,
-        by default None
-    tokenizer : callable[str] | None, optional
-        Tokenizer to use. A callable function on a string.
-        Can also be None if using the default set by LlamaIndex.
-
-    Returns
-    -------
-    Settings
-        _Settings object to use to create the index vectors.
-    """
-    if chunk_size is None and k is None:
-        raise ValueError("Either chunk_size or k must be provided.")
-
-    # convert to int or float if necessary
-    if isinstance(max_input_size, str):
-        max_input_size = int(max_input_size)
-    if isinstance(num_output, str):
-        num_output = int(num_output)
-    if isinstance(chunk_overlap_ratio, str):
-        chunk_overlap_ratio = float(chunk_overlap_ratio)
-    if isinstance(chunk_size, str):
-        chunk_size = int(chunk_size)
-    if isinstance(k, str):
-        k = int(k)
-
-    # if chunk_size is not provided, compute a default value
-    chunk_size = chunk_size or compute_default_chunk_size(
-        max_input_size=max_input_size, k=k
-    )
-
-    # initialise embedding model to use to create the index vectors
-    embed_model = HuggingFaceEmbedding(
-        model_name="sentence-transformers/all-mpnet-base-v2",
-        embed_batch_size=128,
-    )
-
-    # construct the prompt helper
-    prompt_helper = PromptHelper(
-        context_window=max_input_size,
-        num_output=num_output,
-        chunk_size_limit=chunk_size,
-        chunk_overlap_ratio=chunk_overlap_ratio,
-        tokenizer=tokenizer,
-    )
-
-    # construct the settings (and logging the settings set)
-    Settings.llm = llm
-    logging.info(f"Settings llm: {llm}")
-    Settings.embed_model = embed_model
-    logging.info(f"Settings embed_model: {embed_model}")
-    logging.info(f"Embedding model initialised on device {embed_model._device}")
-    Settings.prompt_helper = prompt_helper
-    logging.info(f"Settings prompt_helper: {prompt_helper}")
-    Settings.chunk_size = chunk_size
-    logging.info(f"Settings chunk_size: {chunk_size}")
-    Settings.tokenizer = tokenizer
-    logging.info(f"Settings tokenizer: {tokenizer}")
-
-    return Settings
-
-
-class DataIndexCreator:
-    def __init__(
-        self,
-        data_dir: pathlib.Path | str,
-        which_index: str,
-        settings: _Settings,
-    ) -> None:
-        """
-        Class for creating the data index.
-
-        Parameters
-        ----------
-        data_dir : pathlib.Path | str
-            Path to the data directory.
-        which_index : str
-            Which index to construct (if force_new_index is True) or use.
-            Options are "handbook", "wikis",  "public", "reg" or "all_data".
-        settings : _Settings
-            llama_index.core.settings._Settings object to use to create the index.
-        """
-        self.data_dir: pathlib.Path = pathlib.Path(data_dir)
-        self.which_index: str = which_index
-        self.settings: _Settings = settings
-        self.documents: list[str] = []
-        self.index: VectorStoreIndex | None = None
-
-    def prep_documents(self) -> None:
-        """
-        Method to prepare the documents for the index vector store.
-        """
-        # prep the contextual documents
-        gh_token = get_env_var("GITHUB_TOKEN")
-
-        if gh_token is None:
-            raise ValueError(
-                "Please export your github personal access token as 'GITHUB_TOKEN'."
-            )
-
-        if self.which_index == "handbook":
-            logging.info("Regenerating index only for the handbook")
-
-            # load handbook from repo
-            self._load_handbook(gh_token)
-
-        elif self.which_index == "wikis":
-            logging.info("Regenerating index only for the wikis")
-
-            # load wikis
-            self._load_wikis(gh_token)
-
-        elif self.which_index == "public":
-            logging.info("Regenerating index for all PUBLIC. Will take a long time...")
-
-            # load in scraped turing.ac.uk website
-            self._load_turing_ac_uk()
-
-            # load public data from repos
-            self._load_handbook(gh_token)
-            self._load_rse_course(gh_token)
-            self._load_rds_course(gh_token)
-            self._load_turing_way(gh_token)
-
-        elif self.which_index == "reg":
-            logging.info("Regenerating index for REG. Will take a long time...")
-
-            # load in scraped turing.ac.uk website
-            self._load_turing_ac_uk()
-
-            # load public data from repos
-            self._load_handbook(gh_token)
-
-            # load hut23 data
-            self._load_hut23(gh_token)
-
-            # load wikis
-            self._load_wikis(gh_token)
-
-        elif self.which_index == "all_data":
-            logging.info("Regenerating index for ALL DATA. Will take a long time...")
-
-            # load in scraped turing.ac.uk website
-            self._load_turing_ac_uk()
-
-            # load public data from repos
-            self._load_handbook(gh_token)
-            self._load_rse_course(gh_token)
-            self._load_rds_course(gh_token)
-            self._load_turing_way(gh_token)
-
-            # load hut23 data
-            self._load_hut23(gh_token)
-
-            # load wikis
-            self._load_wikis(gh_token)
-
-        else:
-            logging.info("The which_index provided is unrecognized")
-
-    def _load_turing_ac_uk(self) -> None:
-        """
-        Load in the scraped turing.ac.uk website.
-
-        For 'public' index and 'all_data' index.
-        """
-        data_file = f"{self.data_dir}/public/turingacuk-no-boilerplate.csv"
-        turing_df = pd.read_csv(data_file)
-        turing_df = turing_df[~turing_df.loc[:, "body"].isna()]
-        self.documents += [
-            Document(text=row[1]["body"], extra_info={"url": row[1]["url"]})
-            for row in turing_df.iterrows()
-        ]
-
-    def _load_handbook(self, gh_token: str) -> None:
-        """
-        Load in the REG handbook.
-
-        For 'handbook' index, 'public' index, and 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the handbook repo.
-        """
-        owner = "alan-turing-institute"
-        repo = "REG-handbook"
-
-        handbook_loader = GithubRepositoryReader(
-            GithubClient(gh_token, fail_on_http_error=False),
-            owner=owner,
-            repo=repo,
-            verbose=False,
-            concurrent_requests=1,
-            timeout=60,
-            retries=3,
-            filter_file_extensions=(
-                [".md"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-            filter_directories=(
-                ["content"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-        )
-        self.documents.extend(handbook_loader.load_data(branch="main"))
-
-    def _load_rse_course(self, gh_token: str) -> None:
-        """
-        Load in the REG RSE course.
-
-        For 'public' index and 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the RSE course repo.
-        """
-        owner = "alan-turing-institute"
-        repo = "rse-course"
-
-        rse_course_loader = GithubRepositoryReader(
-            GithubClient(gh_token, fail_on_http_error=False),
-            owner=owner,
-            repo=repo,
-            verbose=False,
-            concurrent_requests=1,
-            timeout=60,
-            retries=3,
-            filter_file_extensions=(
-                [".md", ".ipynb"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-        )
-        self.documents.extend(rse_course_loader.load_data(branch="main"))
-
-    def _load_rds_course(self, gh_token: str) -> None:
-        """
-        Load in REG RDS course.
-
-        For 'public' index and 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the RDS course repo.
-        """
-        owner = "alan-turing-institute"
-        repo = "rds-course"
-
-        rds_course_loader = GithubRepositoryReader(
-            GithubClient(gh_token, fail_on_http_error=False),
-            owner=owner,
-            repo=repo,
-            verbose=False,
-            concurrent_requests=1,
-            timeout=60,
-            retries=3,
-            filter_file_extensions=(
-                [".md", ".ipynb"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-        )
-        self.documents.extend(rds_course_loader.load_data(branch="develop"))
-
-    def _load_turing_way(self, gh_token: str) -> None:
-        """
-        Load in the Turing Way.
-
-        For 'public' index and 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the Turing Way repo.
-        """
-        owner = "the-turing-way"
-        repo = "the-turing-way"
-
-        turing_way_loader = GithubRepositoryReader(
-            GithubClient(gh_token, fail_on_http_error=False),
-            owner=owner,
-            repo=repo,
-            verbose=False,
-            concurrent_requests=1,
-            timeout=60,
-            retries=3,
-            filter_file_extensions=(
-                [".md"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-        )
-        self.documents.extend(turing_way_loader.load_data(branch="main"))
-
-    def _load_hut23(self, gh_token: str) -> None:
-        """
-        Load in documents from the Hut23 repo.
-
-        For 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the Hut23 repo.
-        """
-        owner = "alan-turing-institute"
-        repo = "Hut23"
-
-        # load repo
-        hut23_repo_loader = GithubRepositoryReader(
-            GithubClient(gh_token, fail_on_http_error=False),
-            owner=owner,
-            repo=repo,
-            verbose=False,
-            concurrent_requests=1,
-            timeout=60,
-            retries=3,
-            filter_file_extensions=(
-                [".md", ".ipynb"],
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-            filter_directories=(
-                [
-                    "JDs",
-                    "development",
-                    "newsletters",
-                    "objectives",
-                    "project-appraisal",
-                    "rfc",
-                    "team-meetings",
-                ],  # we can adjust these
-                GithubRepositoryReader.FilterType.INCLUDE,
-            ),
-        )
-        self.documents.extend(hut23_repo_loader.load_data(branch="main"))
-
-        try:
-            # load_issues
-            hut23_issues_loader = GitHubRepositoryIssuesReader(
-                GitHubIssuesClient(gh_token),
-                owner=owner,
-                repo=repo,
-                verbose=True,
-            )
-
-            issue_docs = hut23_issues_loader.load_data()
-            for doc in issue_docs:
-                doc.metadata["api_url"] = str(doc.metadata["url"])
-                doc.metadata["url"] = doc.metadata["source"]
-            self.documents.extend(issue_docs)
-
-        except HTTPError as e:
-            logging.error(f"Failed to load Hut23 issues: {e}")
-
-        # load collaborators
-        # hut23_collaborators_loader = GitHubRepositoryCollaboratorsReader(
-        #     GitHubCollaboratorsClient(gh_token),
-        #     owner=owner,
-        #     repo=repo,
-        #     verbose=True,
-        # )
-        # self.documents.extend(hut23_collaborators_loader.load_data())
-
-    def _load_wikis(self, gh_token: str) -> None:
-        """
-        Load in documents from the wikis.
-
-        For 'wikis' index and 'all_data' index.
-
-        Parameters
-        ----------
-        gh_token : str
-            Github token to use to access the research-engineering-group
-            and Hut23 repo wikis.
-        """
-        wiki_urls = [
-            f"https://oauth2:{gh_token}@github.com/alan-turing-institute/research-engineering-group.wiki.git",
-            f"https://oauth2:{gh_token}@github.com/alan-turing-institute/Hut23.wiki.git",
-        ]
-
-        for url in wiki_urls:
-            temp_dir = TemporaryDirectory()
-            wiki_path = os.path.join(temp_dir.name, url.split("/")[-1])
-
-            _ = Repo.clone_from(url, wiki_path)
-
-            reader = SimpleDirectoryReader(
-                input_dir=wiki_path,
-                required_exts=[".md"],
-                recursive=True,
-                filename_as_id=True,
-            )
-
-            # get base url and file names
-            base_url = url.removesuffix(".wiki.git")
-            fnames = [str(file) for file in reader.input_files]
-
-            # get file urls and create dictionary to map fname to url
-            file_urls = [
-                os.path.join(base_url, "wiki", fname.split("/")[-1].removesuffix(".md"))
-                for fname in fnames
-            ]
-            file_urls_dict = {
-                fname: file_url for fname, file_url in zip(fnames, file_urls)
-            }
-
-            def get_urls(fname):
-                return {"url": file_urls_dict.get(fname)}
-
-            # add `get_urls` function to reader
-            reader.file_metadata = get_urls
-
-            self.documents.extend(reader.load_data())
-
-    def create_index(self) -> VectorStoreIndex:
-        """
-        Create the index vector store.
-        """
-        # obtain documents
-        logging.info(f"Preparing documents for {self.which_index} index...")
-        self.prep_documents()
-
-        # create index
-        logging.info("Creating index...")
-        self.index = VectorStoreIndex.from_documents(
-            self.documents, settings=self.settings
-        )
-
-        return self.index
-
-    def save_index(self, directory: pathlib.Path | None = None) -> None:
-        if directory is None:
-            directory = self.data_dir / LLAMA_INDEX_DIR / self.which_index
-
-        # save the settings and persist the index
-        logging.info(f"Saving the index in {directory}...")
-        self.index.storage_context.persist(persist_dir=directory)
-
-
-class LlamaIndex(ResponseModel):
-    def __init__(
-        self,
-        model_name: str,
-        max_input_size: int,
-        data_dir: pathlib.Path | str,
-        which_index: str,
-        mode: str = "chat",
-        k: int = 3,
-        chunk_size: int | None = None,
-        chunk_overlap_ratio: float = 0.1,
-        num_output: int = 512,
-        force_new_index: bool = False,
-        *args,
-        **kwargs,
-    ) -> None:
-        """
-        Base class for models using llama-index.
-        This class is not intended to be used directly, but rather subclassed
-        to implement the `_prep_llm` method which constructs the LLM to be used.
-
-        Parameters
-        ----------
-        model_name : str
-            Model name to specify which LLM to use.
-        max_input_size : int
-            Context window size for the LLM.
-        data_dir : pathlib.Path | str
-            Path to the data directory.
-        which_index : str
-            Which index to construct (if force_new_index is True) or use.
-            Options are "handbook", "wikis",  "public", "reg" or "all_data".
-        mode : Optional[str], optional
-            The type of engine to use when interacting with the data, options of "chat" or "query".
-            Default is "chat".
-        k : int, optional
-            `similarity_top_k` to use in chat or query engine, by default 3
-        chunk_size : int | None, optional
-            Maximum size of chunks to use, by default None.
-            If None, this is computed as `ceil(max_input_size / k)`.
-        chunk_overlap_ratio : float, optional
-            Chunk overlap as a ratio of chunk size, by default 0.1
-        num_output : int, optional
-            Number of outputs for the LLM, by default 512
-        force_new_index : bool, optional
-            Whether or not to recreate the index vector store,
-            by default False
-        """
-        super().__init__(*args, emoji="llama", **kwargs)
-        logging.info("Setting up Huggingface backend.")
-        if mode == "chat":
-            logging.info("Setting up chat engine.")
-        elif mode == "query":
-            logging.info("Setting up query engine.")
-        else:
-            logging.error("Mode must either be 'query' or 'chat'.")
-            sys.exit(1)
-
-        self.max_input_size = max_input_size
-        self.model_name = model_name
-        self.num_output = num_output
-        self.mode = mode
-        self.k = k
-        self.chunk_size = chunk_size or compute_default_chunk_size(
-            max_input_size=max_input_size, k=k
-        )
-        self.chunk_overlap_ratio = chunk_overlap_ratio
-        self.data_dir = pathlib.Path(data_dir)
-        self.which_index = which_index
-        self.documents = []
-
-        # set up LLM
-        llm = self._prep_llm()
-
-        # set up settings
-        settings = setup_settings(
-            llm=llm,
-            max_input_size=self.max_input_size,
-            num_output=self.num_output,
-            chunk_size=self.chunk_size,
-            chunk_overlap_ratio=self.chunk_overlap_ratio,
-            k=self.k,
-            tokenizer=self._prep_tokenizer(),
-        )
-
-        if force_new_index:
-            logging.info("Generating the index from scratch...")
-            data_creator = DataIndexCreator(
-                which_index=self.which_index,
-                data_dir=self.data_dir,
-                settings=settings,
-            )
-            self.index = stream_progress_wrapper(
-                data_creator.create_index,
-                task_str="Generating the index from scratch...",
-            )
-            stream_progress_wrapper(
-                data_creator.save_index,
-                task_str="Saving the index...",
-            )
-
-        else:
-            logging.info("Loading the storage context")
-            storage_context = stream_progress_wrapper(
-                StorageContext.from_defaults,
-                task_str="Loading the storage context...",
-                persist_dir=self.data_dir / LLAMA_INDEX_DIR / self.which_index,
-            )
-
-            logging.info("Loading the pre-processed index")
-            self.index = stream_progress_wrapper(
-                load_index_from_storage,
-                task_str="Loading the pre-processed index...",
-                storage_context=storage_context,
-                settings=settings,
-            )
-
-        self.response_mode = "simple_summarize"
-        if self.mode == "chat":
-            self.chat_engine = {}
-            logging.info("Done setting up Huggingface backend for chat engine.")
-        elif self.mode == "query":
-            self.query_engine = self.index.as_query_engine(
-                response_mode=self.response_mode,
-                similarity_top_k=k,
-            )
-            logging.info("Done setting up Huggingface backend for query engine.")
-
-        self.error_response_template = (
-            "Oh no! When I tried to get a response to your prompt, "
-            "I got the following error:"
-            "\n```\n{}\n```"
-        )
-
-    @staticmethod
-    def _format_sources(response: RESPONSE_TYPE) -> str:
-        """
-        Method to format the sources used to compose the response.
-
-        Parameters
-        ----------
-        response : RESPONSE_TYPE
-            response object from the query engine
-
-        Returns
-        -------
-        str
-            String containing the formatted sources that
-            were used to compose the response
-        """
-        texts = []
-        for source_node in response.source_nodes:
-            # obtain the URL for source
-            try:
-                node_url = source_node.node.extra_info["url"]
-            except KeyError:
-                node_url = source_node.node.extra_info["filename"]
-
-            # add its similarity score and append to texts
-            source_text = node_url + f" (similarity: {round(source_node.score, 2)})"
-            texts.append(source_text)
-
-        result = "I read the following documents to compose this answer:\n"
-        result += "\n\n".join(texts)
-
-        return result
-
-    def _prep_llm(self) -> BaseLLM:
-        """
-        Method to prepare the LLM to be used.
-
-        Returns
-        -------
-        BaseLLM
-            LLM to be used.
-
-        Raises
-        ------
-        NotImplemented
-            This must be implemented by a subclass of LlamaIndex.
-        """
-        raise NotImplementedError(
-            "_prep_llm needs to be implemented by a subclass of LlamaIndex."
-        )
-
-    def _prep_tokenizer(self) -> callable[str] | None:
-        """
-        Method to prepare the Tokenizer to be used.
-
-        Returns
-        -------
-        callable[str] | None
-            Tokenizer to use. A callable function on a string.
-            Can also be None if using the default set by LlamaIndex.
-
-        Raises
-        ------
-        NotImplemented
-        """
-        raise NotImplementedError(
-            "_prep_tokenizer needs to be implemented by a subclass of LlamaIndex."
-        )
-
-    def _get_response(self, message: str, user_id: str) -> MessageResponse:
-        """
-        Method to respond to a message in Slack.
-
-        Parameters
-        ----------
-        msg_in : str
-            Message from user
-        user_id : str
-            User ID
-
-        Returns
-        -------
-        MessageResponse
-            Response from the query engine.
-        """
-        try:
-            if self.mode == "chat":
-                # create chat engine for user if does not exist
-                if self.chat_engine.get(user_id) is None:
-                    self.chat_engine[user_id] = self.index.as_chat_engine(
-                        chat_mode="condense_plus_context",
-                        response_mode=self.response_mode,
-                        similarity_top_k=self.k,
-                    )
-
-                # obtain chat engine for particular user
-                chat_engine = self.chat_engine[user_id]
-                response = chat_engine.chat(message)
-            elif self.mode == "query":
-                self.query_engine._response_synthesizer._streaming = False
-                response = self.query_engine.query(message)
-
-            # concatenate the response with the resources that it used
-            formatted_response = (
-                response.response + "\n\n\n" + self._format_sources(response)
-            )
-        except Exception as e:  # ignore: broad-except
-            formatted_response = self.error_response_template.format(repr(e))
-
-        pattern = (
-            r"(?s)^Context information is"
-            r".*"
-            r"Given the context information and not prior knowledge, answer the question: "
-            rf"{message}"
-            r"\n(.*)"
-        )
-        m = re.search(pattern, formatted_response)
-
-        if m:
-            answer = m.group(1)
-        else:
-            logging.warning(
-                "Was expecting a backend response with a regular expression but couldn't find a match."
-            )
-            answer = formatted_response
-
-        return MessageResponse(answer)
-
-    def direct_message(self, message: str, user_id: str) -> MessageResponse:
-        """
-        Method to respond to a direct message in Slack.
-
-        Parameters
-        ----------
-        msg_in : str
-            Message from user
-        user_id : str
-            User ID
-
-        Returns
-        -------
-        MessageResponse
-            Response from the query engine.
-        """
-        return self._get_response(message=message, user_id=user_id)
-
-    def channel_mention(self, message: str, user_id: str) -> MessageResponse:
-        """
-        Method to respond to a channel mention in Slack.
-
-        Parameters
-        ----------
-        msg_in : str
-            Message from user
-        user_id : str
-            User ID
-
-        Returns
-        -------
-        MessageResponse
-            Response from the query engine.
-        """
-        return self._get_response(message=message, user_id=user_id)
-
-    def stream_message(self, message: str, user_id: str) -> None:
-        """
-        Method to respond to a stream message in Slack.
-
-        Parameters
-        ----------
-        msg_in : str
-            Message from user
-        user_id : str
-            User ID
-
-        Returns
-        -------
-        MessageResponse
-            Response from the query engine.
-        """
-        try:
-            if self.mode == "chat":
-                # create chat engine for user if does not exist
-                if self.chat_engine.get(user_id) is None:
-                    self.chat_engine[user_id] = self.index.as_chat_engine(
-                        chat_mode="condense_plus_context",
-                        response_mode=self.response_mode,
-                        similarity_top_k=self.k,
-                        streaming=True,
-                    )
-
-                # obtain chat engine for particular user
-                chat_engine = self.chat_engine[user_id]
-                response_stream = chat_engine.stream_chat(message)
-            elif self.mode == "query":
-                self.query_engine._response_synthesizer._streaming = True
-                response_stream = self.query_engine.query(message)
-
-            for token in stream_iter_progress_wrapper(response_stream.response_gen):
-                print(token, end="", flush=True)
-
-            formatted_response = "\n\n\n" + self._format_sources(response_stream)
-
-            for token in re.split(r"(\s+)", formatted_response):
-                print(token, end="", flush=True)
-        except Exception as e:  # ignore: broad-except
-            for token in re.split(
-                r"(\s+)", self.error_response_template.format(repr(e))
-            ):
-                print(token, end="", flush=True)
-
-
-class LlamaIndexOllama(LlamaIndex):
-    def __init__(
-        self,
-        model_name: str,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
-        """
-        `LlamaIndexOllama` is a subclass of `LlamaIndex` that uses
-        ollama to run inference on the LLM.
-
-        Parameters
-        ----------
-        model_name : str
-            The Ollama model to use
-        """
-        ollama_api_endpoint = get_env_var("OLLAMA_API_ENDPOINT")
-        if ollama_api_endpoint is None:
-            raise ValueError("You must set OLLAMA_API_ENDPOINT for Ollama.")
-        self.ollama_api_endpoint = ollama_api_endpoint
-        super().__init__(*args, model_name=model_name, **kwargs)
-
-    def _prep_llm(self) -> Ollama:
-        logging.info(f"Setting up Ollama (model {self.model_name})")
-        return Ollama(
-            base_url=self.ollama_api_endpoint,
-            model=self.model_name,
-            request_timeout=60,
-        )
-
-    def _prep_tokenizer(self) -> callable[str]:
-        # NOTE: this should depend on the model used, but hard coding tiktoken for now
-        logging.info("Setting up tiktoken gpt-4 tokenizer")
-        tokenizer = tiktoken.encoding_for_model("gpt-4").encode
-        set_global_tokenizer(tokenizer)
-        return tokenizer
-
-
-class LlamaIndexLlamaCPP(LlamaIndex):
-    def __init__(
-        self,
-        model_name: str,
-        is_path: bool,
-        n_gpu_layers: int = 0,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
-        """
-        `LlamaIndexLlamaCPP` is a subclass of `LlamaIndex` that uses
-        llama-cpp to implement the LLM.
-
-        Parameters
-        ----------
-        model_name : str
-            Either the path to the model or the URL to download the model from
-        is_path : bool, optional
-            If True, model_name is used as a path to the model file,
-            otherwise it should be the URL to download the model
-        n_gpu_layers : int, optional
-            Number of layers to offload to GPU.
-            If -1, all layers are offloaded, by default 0
-        """
-        self.is_path = is_path
-        self.n_gpu_layers = n_gpu_layers
-        super().__init__(*args, model_name=model_name, **kwargs)
-
-    def _prep_llm(self) -> LlamaCPP:
-        logging.info(
-            f"Setting up LlamaCPP LLM (model {self.model_name}) on {self.n_gpu_layers} GPU layers"
-        )
-        logging.info(
-            f"LlamaCPP-args: (context_window: {self.max_input_size}, num_output: {self.num_output})"
-        )
-
-        return LlamaCPP(
-            model_url=self.model_name if not self.is_path else None,
-            model_path=self.model_name if self.is_path else None,
-            temperature=0.1,
-            max_new_tokens=self.num_output,
-            context_window=self.max_input_size,
-            # kwargs to pass to __call__()
-            generate_kwargs={},
-            # kwargs to pass to __init__()
-            model_kwargs={"n_gpu_layers": self.n_gpu_layers},
-            messages_to_prompt=messages_to_prompt,
-            completion_to_prompt=completion_to_prompt,
-            verbose=True,
-        )
-
-    def _prep_tokenizer(self) -> callable[str]:
-        # NOTE: this should depend on the model used, but hard coding tiktoken for now
-        logging.info("Setting up tiktoken gpt-4 tokenizer")
-        tokenizer = tiktoken.encoding_for_model("gpt-4").encode
-        set_global_tokenizer(tokenizer)
-        return tokenizer
-
-
-class LlamaIndexHF(LlamaIndex):
-    def __init__(
-        self,
-        model_name: str = "google/gemma-2b-it",
-        device: str = "auto",
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
-        """
-        `LlamaIndexHF` is a subclass of `LlamaIndex` that uses HuggingFace's
-        `transformers` library to implement the LLM.
-
-        Parameters
-        ----------
-        model_name : str, optional
-            Model name from Huggingface's model hub,
-            by default "google/gemma-2b-it".
-        device : str, optional
-            Device map to use for the LLM, by default "auto".
-        """
-        self.device = device
-        super().__init__(*args, model_name=model_name, **kwargs)
-
-    def _prep_llm(self) -> HuggingFaceLLM:
-        logging.info(
-            f"Setting up Huggingface LLM (model {self.model_name}) on device {self.device}"
-        )
-        logging.info(
-            f"HF-args: (context_window: {self.max_input_size}, num_output: {self.num_output})"
-        )
-
-        return HuggingFaceLLM(
-            context_window=self.max_input_size,
-            max_new_tokens=self.num_output,
-            generate_kwargs={"temperature": 0.1, "do_sample": False},
-            tokenizer_name=self.model_name,
-            model_name=self.model_name,
-            device_map=self.device or "auto",
-        )
-
-    def _prep_tokenizer(self) -> callable[str]:
-        logging.info(f"Setting up Huggingface tokenizer for model {self.model_name}")
-        tokenizer = AutoTokenizer.from_pretrained(self.model_name).encode
-        set_global_tokenizer(tokenizer)
-        return tokenizer
-
-
-class LlamaIndexGPTOpenAI(LlamaIndex):
-    def __init__(
-        self, model_name: str = "gpt-3.5-turbo", *args: Any, **kwargs: Any
-    ) -> None:
-        """
-        `LlamaIndexGPTOpenAI` is a subclass of `LlamaIndex` that uses OpenAI's
-        API to implement the LLM.
-
-        Must have `OPENAI_API_KEY` set as an environment variable.
-
-        Parameters
-        ----------
-        model_name : str, optional
-            The model to use from the OpenAI API, by default "gpt-3.5-turbo"
-        """
-        openai_api_key = get_env_var("OPENAI_API_KEY")
-        if openai_api_key is None:
-            raise ValueError("You must set OPENAI_API_KEY for OpenAI.")
-
-        self.model_name = model_name
-        self.openai_api_key = openai_api_key
-        self.temperature = 0.7
-        super().__init__(*args, model_name=self.model_name, **kwargs)
-
-    def _prep_llm(self) -> OpenAI:
-        logging.info(f"Setting up OpenAI LLM (model {self.model_name})")
-        return OpenAI(
-            model=self.model_name,
-            temperature=self.temperature,
-            max_tokens=self.num_output,
-            api_key=self.openai_api_key,
-        )
-
-    def _prep_tokenizer(self) -> None:
-        import tiktoken
-
-        logging.info(f"Setting up tiktoken tokenizer for model {self.model_name}")
-        tokenizer = tiktoken.encoding_for_model(self.model_name).encode
-        set_global_tokenizer(tokenizer)
-        return tokenizer
-
-
-class LlamaIndexGPTAzure(LlamaIndex):
-    def __init__(
-        self, model_name: str = "reginald-gpt4", *args: Any, **kwargs: Any
-    ) -> None:
-        """
-         `LlamaIndexGPTAzure` is a subclass of `LlamaIndex` that uses Azure's
-        instance of OpenAI's LLMs to implement the LLM.
-
-        Must have the following environment variables set:
-        - `OPENAI_API_BASE`: Azure endpoint which looks
-          like https://YOUR_RESOURCE_NAME.openai.azure.com/
-        - `OPENAI_API_KEY`: Azure API key
-
-        Parameters
-        ----------
-        model_name : str, optional
-            The deployment name of the model, by default "reginald-gpt4"
-        """
-        openai_azure_api_base = get_env_var("OPENAI_AZURE_API_BASE", secret_value=False)
-        if openai_azure_api_base is None:
-            raise ValueError(
-                "You must set OPENAI_AZURE_API_BASE to your Azure endpoint. "
-                "It should look like https://YOUR_RESOURCE_NAME.openai.azure.com/"
-            )
-
-        openai_azure_api_key = get_env_var("OPENAI_AZURE_API_KEY")
-        if openai_azure_api_key is None:
-            raise ValueError("You must set OPENAI_AZURE_API_KEY for Azure OpenAI.")
-
-        # deployment name can be found in the Azure AI Studio portal
-        self.deployment_name = model_name
-        self.openai_api_base = openai_azure_api_base
-        self.openai_api_key = openai_azure_api_key
-        self.openai_api_version = "2023-09-15-preview"
-        self.temperature = 0.7
-        super().__init__(*args, model_name="gpt-4", **kwargs)
-
-    def _prep_llm(self) -> AzureOpenAI:
-        logging.info(f"Setting up AzureOpenAI LLM (model {self.deployment_name})")
-        return AzureOpenAI(
-            model=self.model_name,
-            engine=self.deployment_name,
-            temperature=self.temperature,
-            max_tokens=self.num_output,
-            api_key=self.openai_api_key,
-            api_base=self.openai_api_base,
-            api_type="azure",
-            azure_endpoint=self.openai_api_base,
-            api_version=self.openai_api_version,
-        )
-
-    def _prep_tokenizer(self) -> None:
-        import tiktoken
-
-        logging.info(f"Setting up tiktoken tokenizer for model {self.model_name}")
-        tokenizer = tiktoken.encoding_for_model("gpt-4").encode
-        set_global_tokenizer(tokenizer)
-        return tokenizer
diff --git a/reginald/models/setup_llm.py b/reginald/models/setup_llm.py
index ea464631..4841fc05 100644
--- a/reginald/models/setup_llm.py
+++ b/reginald/models/setup_llm.py
@@ -2,24 +2,9 @@
 import pathlib
 import sys
 
-from reginald.models.models import DEFAULTS, MODELS
-from reginald.models.models.base import ResponseModel
-
-DEFAULT_ARGS = {
-    "model": "hello",
-    "mode": "chat",
-    "data_dir": pathlib.Path(__file__).parent.parent.parent / "data",
-    "which_index": "reg",
-    "force_new_index": False,
-    "max_input_size": 4096,
-    "k": 3,
-    "chunk_size": 512,
-    "chunk_overlap_ratio": 0.1,
-    "num_output": 512,
-    "is_path": False,
-    "n_gpu_layers": 0,
-    "device": "auto",
-}
+from reginald.defaults import DEFAULT_ARGS
+from reginald.models import DEFAULTS, ModelMapper
+from reginald.models.base import ResponseModel
 
 
 def setup_llm(
@@ -114,7 +99,7 @@ def setup_llm(
     if model is None:
         model = DEFAULT_ARGS["model"]
     model = model.lower()
-    if model not in MODELS.keys():
+    if model not in ModelMapper.available_models():
         logging.error(f"Model '{model}' not recognised.")
         sys.exit(1)
     logging.info(f"Setting up '{model}' model.")
@@ -189,7 +174,7 @@ def setup_llm(
         device = DEFAULT_ARGS["device"]
 
     # set up response model
-    model = MODELS[model]
+    model = ModelMapper.get_model(model)
     response_model = model(
         model_name=model_name,
         max_input_size=max_input_size,
diff --git a/reginald/models/simple/__init__.py b/reginald/models/simple/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reginald/models/models/chat_completion.py b/reginald/models/simple/chat_completion.py
similarity index 99%
rename from reginald/models/models/chat_completion.py
rename to reginald/models/simple/chat_completion.py
index fb0c0bcc..e9aad242 100644
--- a/reginald/models/models/chat_completion.py
+++ b/reginald/models/simple/chat_completion.py
@@ -5,7 +5,7 @@
 import openai
 from openai import AzureOpenAI, OpenAI
 
-from reginald.models.models.base import MessageResponse, ResponseModel
+from reginald.models.base import MessageResponse, ResponseModel
 from reginald.utils import get_env_var, stream_iter_progress_wrapper
 
 
diff --git a/reginald/models/models/hello.py b/reginald/models/simple/hello.py
similarity index 92%
rename from reginald/models/models/hello.py
rename to reginald/models/simple/hello.py
index 01ba40a3..accbf193 100644
--- a/reginald/models/models/hello.py
+++ b/reginald/models/simple/hello.py
@@ -1,6 +1,6 @@
 from typing import Any
 
-from reginald.models.models.base import MessageResponse, ResponseModel
+from reginald.models.base import MessageResponse, ResponseModel
 from reginald.utils import stream_iter_progress_wrapper
 
 
diff --git a/reginald/parser_utils.py b/reginald/parser_utils.py
deleted file mode 100644
index 2b2677b7..00000000
--- a/reginald/parser_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import argparse
-import pathlib
-
-from reginald.models.models import MODELS
-from reginald.utils import get_env_var
-
-
-class Parser(argparse.ArgumentParser):
-    def __init__(self, create_index_only: bool = False, *args, **kwargs):
-        """
-        Parser for command line arguments for Reginald.
-
-        Note that the default values for the arguments are set to a lambda function
-        to obtain the relevant environment variables (if they exist). They are
-        lambda functions so that the environment variables are only obtained if
-        the argument is actually used.
-
-        See `get_args` for parsing the arguments and obtaining any of the
-        environment variables if using default values.
-
-        Parameters
-        ----------
-        create_index_only : bool, optional
-            Whether or not to only include arguments related to data index creation,
-            by default False
-        """
-        super().__init__(*args, **kwargs)
-        if not create_index_only:
-            # model arguments
-            self.add_argument(
-                "--model",
-                "-m",
-                type=str,
-                help=("Select which type of model to use. Default is 'hello'."),
-                default=lambda: get_env_var("REGINALD_MODEL", secret_value=False),
-                choices=MODELS,
-            )
-            self.add_argument(
-                "--model-name",
-                "-n",
-                type=str,
-                help=(
-                    "Select which sub-model to use (within the main model selected)."
-                    "For llama-index-llama-cpp and llama-index-hf models, this specifies"
-                    "the LLM (or path to that model) which we would like to use."
-                    "For chat-completion-azure and llama-index-gpt-azure, this refers"
-                    "to the deployment name on Azure."
-                    "For chat-completion-azure and llama-index-gpt-openai, this refers"
-                    "to the model name on OpenAI."
-                    "(ignored if using 'hello' model types)."
-                ),
-                default=lambda: get_env_var("REGINALD_MODEL_NAME", secret_value=False),
-            )
-            self.add_argument(
-                "--mode",
-                type=str,
-                help=(
-                    "Select which mode to use "
-                    "(ignored if not using llama-index). "
-                    "Default is 'chat'."
-                ),
-                default=lambda: get_env_var("LLAMA_INDEX_MODE", secret_value=False),
-                choices=["chat", "query"],
-            )
-            self.add_argument(
-                "--force-new-index",
-                "-f",
-                help=(
-                    "Recreate the index vector store or not "
-                    "(ignored if not using llama-index). "
-                    "Default is False."
-                ),
-                action=argparse.BooleanOptionalAction,
-                default=lambda: get_env_var(
-                    "LLAMA_INDEX_FORCE_NEW_INDEX", secret_value=False
-                ),
-            )
-            self.add_argument(
-                "--is-path",
-                "-p",
-                help=(
-                    "Whether or not the model_name passed is a path to the model "
-                    "(ignored if not using llama-index-llama-cpp). "
-                    "Default is False."
-                ),
-                action=argparse.BooleanOptionalAction,
-                default=lambda: get_env_var("LLAMA_INDEX_IS_PATH", secret_value=False),
-            )
-            self.add_argument(
-                "--n-gpu-layers",
-                "-ngl",
-                type=int,
-                help=(
-                    "Select number of GPU layers for LlamaCPP model "
-                    "(ignored if not using llama-index-llama-cpp). "
-                    "Default is 0."
-                ),
-                default=lambda: get_env_var(
-                    "LLAMA_INDEX_N_GPU_LAYERS", secret_value=False
-                ),
-            )
-            self.add_argument(
-                "--device",
-                "-dev",
-                type=str,
-                help=(
-                    "Select device for HuggingFace model "
-                    "(ignored if not using llama-index-hf model). "
-                    "Default is 'auto'."
-                ),
-                default=lambda: get_env_var("LLAMA_INDEX_DEVICE", secret_value=False),
-            )
-
-        # data index arguments
-        self.add_argument(
-            "--data-dir",
-            "-d",
-            type=pathlib.Path,
-            help=(
-                "Location for data (ignored if not using llama-index). "
-                "Default is 'data' in the root of the repo."
-            ),
-            default=lambda: get_env_var("LLAMA_INDEX_DATA_DIR", secret_value=False)
-            or (pathlib.Path(__file__).parent.parent / "data").resolve(),
-        )
-        self.add_argument(
-            "--which-index",
-            "-w",
-            type=str,
-            help=(
-                "Specifies the directory name for looking up/writing indices "
-                "(ignored if not using llama-index). "
-                "Currently supports 'handbook', 'wikis', 'public', or 'all_data'. "
-                "Default is 'all_data'."
-            ),
-            default=lambda: get_env_var("LLAMA_INDEX_WHICH_INDEX", secret_value=False),
-            choices=["handbook", "wikis", "public", "reg", "all_data"],
-        )
-        self.add_argument(
-            "--max-input-size",
-            "-max",
-            type=int,
-            help=(
-                "Select maximum input size for LlamaCPP or HuggingFace model "
-                "(ignored if not using llama-index). "
-                "Default is 4096."
-            ),
-            default=lambda: get_env_var(
-                "LLAMA_INDEX_MAX_INPUT_SIZE", secret_value=False
-            ),
-        )
-        self.add_argument(
-            "--k",
-            type=int,
-            help=(
-                "`similarity_top_k` to use in chat or query engine, "
-                "(ignored if not using llama-index). "
-                "Default is 3."
-            ),
-            default=lambda: get_env_var("LLAMA_INDEX_K", secret_value=False),
-        )
-        self.add_argument(
-            "--chunk-size",
-            "-cs",
-            type=int,
-            help=(
-                "Select chunk size for LlamaIndex model "
-                "(ignored if not using llama-index). "
-                "Default is computed by ceil(max_input_size / k)."
-            ),
-            default=lambda: get_env_var("LLAMA_INDEX_CHUNK_SIZE", secret_value=False),
-        )
-        self.add_argument(
-            "--chunk-overlap-ratio",
-            "-cor",
-            type=float,
-            help=(
-                "Select chunk overlap ratio for LlamaIndex model "
-                "(ignored if not using llama-index). "
-                "Default is 0.1."
-            ),
-            default=lambda: get_env_var(
-                "LLAMA_INDEX_CHUNK_OVERLAP_RATIO", secret_value=False
-            ),
-        )
-        self.add_argument(
-            "--num-output",
-            "-no",
-            type=int,
-            help=(
-                "Select number of outputs for LlamaIndex LLM model "
-                "(ignored if not using llama-index). "
-                "Default is 512."
-            ),
-            default=lambda: get_env_var("LLAMA_INDEX_NUM_OUTPUT", secret_value=False),
-        )
-
-
-def get_args(parser: Parser) -> argparse.Namespace:
-    """
-    Helper function to parse command line arguments and obtain any of the
-    environment variables if using default values.
-
-    Parameters
-    ----------
-    parser : Parser
-        Parser for command line arguments for Reginald
-
-    Returns
-    -------
-    argparse.Namespace
-        Namespace of parsed arguments
-    """
-    # parse command line arguments
-    args = parser.parse_args()
-
-    # call any lambda functions in the args
-    for arg_name in vars(args):
-        arg_value = getattr(args, arg_name)
-        if callable(arg_value):
-            # call the lambda function to get its value
-            arg_value = arg_value()
-            # update the argument value in args
-            setattr(args, arg_name, arg_value)
-
-    return args
diff --git a/reginald/run.py b/reginald/run.py
index 6ca70950..26bb0993 100644
--- a/reginald/run.py
+++ b/reginald/run.py
@@ -1,176 +1,59 @@
 import asyncio
 import logging
-import pathlib
-import sys
-from typing import Final
-
-import uvicorn
-from fastapi import FastAPI
-from slack_sdk.socket_mode.aiohttp import SocketModeClient
-
-from reginald.models.app import create_reginald_app
-from reginald.models.create_index import create_index
-from reginald.models.models.base import ResponseModel
-from reginald.models.setup_llm import setup_llm
-from reginald.slack_bot.setup_bot import (
-    EMOJI_DEFAULT,
-    setup_api_slack_bot,
-    setup_slack_bot,
-    setup_slack_client,
-)
-from reginald.utils import create_folder
-
-LISTENING_MSG: Final[str] = "Listening for requests..."
-
-
-async def run_bot(api_url: str | None, emoji: str) -> None:
-    if api_url is None:
-        logging.error("api_url is not set.")
-        sys.exit(1)
-
-    # set up slack bot
-    bot = setup_api_slack_bot(api_url=api_url, emoji=emoji)
-
-    # set up slack client
-    client = setup_slack_client(slack_bot=bot)
-    await connect_client(client)
-
-
-async def run_reginald_app(**kwargs) -> None:
-    # set up response model
-    response_model = setup_llm(**kwargs)
-    app: FastAPI = create_reginald_app(response_model)
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-
-
-async def run_full_pipeline(**kwargs) -> None:
-    # set up response model
-    response_model = setup_llm(**kwargs)
-    bot = setup_slack_bot(response_model)
-    # set up slack client
-    client = setup_slack_client(bot)
-    await connect_client(client)
-
-
-def run_chat_interact(streaming: bool = False, **kwargs) -> ResponseModel:
-    # set up response model
-    response_model = setup_llm(**kwargs)
-    user_id = "command_line_chat"
-
-    while True:
-        message = input(">>> ")
-        if message in ["exit", "exit()", "quit()", "bye Reginald"]:
-            return response_model
-        if message == "":
-            continue
-        if message in ["clear_history", "\clear_history"]:
-            if (
-                response_model.mode == "chat"
-                and response_model.chat_engine.get(user_id) is not None
-            ):
-                response_model.chat_engine[user_id].reset()
-                print("\nReginald: History cleared.")
-            else:
-                print("\nReginald: No history to clear.")
-            continue
-
-        if streaming:
-            response = response_model.stream_message(message=message, user_id=user_id)
-            print("")
-        else:
-            response = response_model.direct_message(message=message, user_id=user_id)
-            print(f"\nReginald: {response.message}")
-
-
-async def connect_client(client: SocketModeClient) -> None:
-    await client.connect()
-    # listen for events
-    logging.info(LISTENING_MSG)
-    # TODO: Assess whether this is best to use
-    await asyncio.sleep(float("inf"))
-
-
-def download_from_fileshare(
-    data_dir: pathlib.Path | str,
-    which_index: str,
-    azure_storage_key: str | None,
-    connection_str: str | None,
-) -> None:
-    from azure.storage.fileshare import ShareClient
-    from tqdm import tqdm
-
-    if azure_storage_key is None:
-        logging.error("azure_storage_key is not set.")
-        sys.exit(1)
-    if connection_str is None:
-        logging.error("connection_str is not set.")
-        sys.exit(1)
-
-    # set the file share name and directory
-    file_share_name = "llama-data"
-    file_share_directory = f"llama_index_indices/{which_index}"
-
-    # create a ShareClient object
-    share_client = ShareClient.from_connection_string(
-        conn_str=connection_str,
-        share_name=file_share_name,
-        credential=azure_storage_key,
-    )
-
-    # get a reference to the file share directory
-    file_share_directory_client = share_client.get_directory_client(
-        file_share_directory
-    )
-
-    # set the local download directory
-    local_download_directory = (
-        pathlib.Path(data_dir) / "llama_index_indices" / which_index
-    )
-
-    # create folder if does not exist
-    create_folder(local_download_directory)
-
-    # list all the files in the directory
-    files_list = file_share_directory_client.list_directories_and_files()
-
-    # check if the index exists
-    try:
-        files_list = list(files_list)
-    except:
-        logging.error(f"Index {which_index} does not exist in the file share")
-        sys.exit(1)
-
-    # iterate through each file in the list and download it
-    for file in tqdm(files_list):
-        if not file.is_directory:
-            file_client = file_share_directory_client.get_file_client(file.name)
-            download_path = local_download_directory / file.name
-            with open(download_path, "wb") as file_handle:
-                data = file_client.download_file()
-                data.readinto(file_handle)
 
 
 def main(
     cli: str,
     api_url: str | None = None,
-    emoji: str = EMOJI_DEFAULT,
+    emoji: str | None = None,
     streaming: bool = False,
     data_dir: str | None = None,
     which_index: str | None = None,
+    slack_app_token: str | None = None,
+    slack_bot_token: str | None = None,
     **kwargs,
 ):
     # initialise logging
     if cli == "run_all":
-        asyncio.run(run_full_pipeline(**kwargs))
+        from reginald.run_full_pipeline import run_full_pipeline
+
+        asyncio.run(
+            run_full_pipeline(
+                slack_app_token=slack_app_token,
+                slack_bot_token=slack_bot_token,
+                **kwargs,
+            )
+        )
     elif cli == "bot":
-        asyncio.run(run_bot(api_url=api_url, emoji=emoji))
+        from reginald.slack_bot.run_bot import run_bot
+
+        asyncio.run(
+            run_bot(
+                slack_app_token=slack_app_token,
+                slack_bot_token=slack_bot_token,
+                api_url=api_url,
+                emoji=emoji,
+            )
+        )
     elif cli == "app":
+        from reginald.models.app import run_reginald_app
+
         asyncio.run(run_reginald_app(**kwargs))
     elif cli == "chat":
+        import warnings
+
+        warnings.filterwarnings("ignore")
+
+        from reginald.models.chat_interact import run_chat_interact
+
         run_chat_interact(streaming=streaming, **kwargs)
     elif cli == "create_index":
-        create_index(**kwargs)
+        from reginald.models.create_index import create_index
+
+        create_index(data_dir=data_dir, which_index=which_index, **kwargs)
     elif cli == "download":
+        from reginald.models.download_from_fileshare import download_from_fileshare
+
         download_from_fileshare(data_dir=data_dir, which_index=which_index, **kwargs)
     else:
         logging.info("No run options selected.")
diff --git a/reginald/run_full_pipeline.py b/reginald/run_full_pipeline.py
new file mode 100644
index 00000000..4c669960
--- /dev/null
+++ b/reginald/run_full_pipeline.py
@@ -0,0 +1,17 @@
+from reginald.models.setup_llm import setup_llm
+from reginald.slack_bot.utils import connect_client, setup_slack_bot, setup_slack_client
+
+
+async def run_full_pipeline(
+    slack_app_token: str, slack_bot_token: str, **kwargs
+) -> None:
+    # set up response model
+    response_model = setup_llm(**kwargs)
+    bot = setup_slack_bot(response_model)
+
+    # set up slack client
+    client = setup_slack_client(
+        slack_bot=bot, slack_app_token=slack_app_token, slack_bot_token=slack_bot_token
+    )
+
+    await connect_client(client)
diff --git a/reginald/slack_bot/bot.py b/reginald/slack_bot/bot.py
index 978c080a..85f9716f 100644
--- a/reginald/slack_bot/bot.py
+++ b/reginald/slack_bot/bot.py
@@ -7,7 +7,7 @@
 from slack_sdk.socket_mode.request import SocketModeRequest
 from slack_sdk.socket_mode.response import SocketModeResponse
 
-from reginald.models.models.base import ResponseModel
+from reginald.models.base import ResponseModel
 
 
 class Bot(AsyncSocketModeRequestListener):
diff --git a/reginald/slack_bot/run_bot.py b/reginald/slack_bot/run_bot.py
new file mode 100644
index 00000000..1366979b
--- /dev/null
+++ b/reginald/slack_bot/run_bot.py
@@ -0,0 +1,26 @@
+import logging
+import sys
+
+from reginald.slack_bot.utils import (
+    connect_client,
+    setup_api_slack_bot,
+    setup_slack_client,
+)
+
+
+async def run_bot(
+    slack_app_token: str, slack_bot_token: str, api_url: str | None, emoji: str | None
+) -> None:
+    if api_url is None:
+        logging.error("api_url is not set.")
+        sys.exit(1)
+
+    # set up slack bot
+    bot = setup_api_slack_bot(api_url=api_url, emoji=emoji)
+
+    # set up slack client
+    client = setup_slack_client(
+        slack_bot=bot, slack_app_token=slack_app_token, slack_bot_token=slack_bot_token
+    )
+
+    await connect_client(client)
diff --git a/reginald/slack_bot/setup_bot.py b/reginald/slack_bot/utils.py
similarity index 69%
rename from reginald/slack_bot/setup_bot.py
rename to reginald/slack_bot/utils.py
index 505b2559..296510c4 100755
--- a/reginald/slack_bot/setup_bot.py
+++ b/reginald/slack_bot/utils.py
@@ -1,19 +1,15 @@
-import argparse
 import asyncio
 import logging
-import os
-import sys
 from typing import Final
 
 from slack_sdk.socket_mode.aiohttp import SocketModeClient
 from slack_sdk.web.async_client import AsyncWebClient
 
-from reginald.models.models.base import ResponseModel
-from reginald.parser_utils import get_args
+from reginald.defaults import EMOJI_DEFAULT
+from reginald.models.base import ResponseModel
 from reginald.slack_bot.bot import ApiBot, Bot
-from reginald.utils import get_env_var
 
-EMOJI_DEFAULT: Final[str] = "rocket"
+LISTENING_MSG: Final[str] = "Listening for requests..."
 
 
 def setup_slack_bot(model: ResponseModel) -> Bot:
@@ -34,15 +30,10 @@ def setup_slack_bot(model: ResponseModel) -> Bot:
 
     slack_bot = Bot(model=model)
 
-    logging.info("Connecting to Slack...")
-    if get_env_var("SLACK_APP_TOKEN", log=False) is None:
-        logging.error("SLACK_APP_TOKEN is not set")
-        sys.exit(1)
-
     return slack_bot
 
 
-def setup_api_slack_bot(api_url: str | None, emoji: str) -> ApiBot:
+def setup_api_slack_bot(api_url: str, emoji: str | None) -> ApiBot:
     """
     Initialise `ApiBot` with response model.
 
@@ -57,20 +48,20 @@ def setup_api_slack_bot(api_url: str | None, emoji: str) -> ApiBot:
         Bot which uses an API for responding to messages
     """
     logging.info(f"Initalising bot at {api_url}")
+
+    if emoji is None:
+        emoji = EMOJI_DEFAULT
     logging.info(f"Initalising bot with {emoji} emoji")
 
     # set up bot with the api_url and emoji
     slack_bot = ApiBot(api_url=api_url, emoji=emoji)
 
-    logging.info("Connecting to Slack...")
-    if get_env_var("SLACK_APP_TOKEN", log=False) is None:
-        logging.error("SLACK_APP_TOKEN is not set")
-        sys.exit(1)
-
     return slack_bot
 
 
-def setup_slack_client(slack_bot: ApiBot | Bot) -> SocketModeClient:
+def setup_slack_client(
+    slack_bot: ApiBot | Bot, slack_app_token: str, slack_bot_token: str
+) -> SocketModeClient:
     """
     Initialise Slack client with bot.
 
@@ -88,15 +79,7 @@ def setup_slack_client(slack_bot: ApiBot | Bot) -> SocketModeClient:
     SocketModeClient
         Slack client with bot
     """
-    slack_app_token = get_env_var("SLACK_APP_TOKEN")
-    if slack_app_token is None:
-        logging.error("SLACK_APP_TOKEN is not set")
-        sys.exit(1)
-
-    slack_bot_token = get_env_var("SLACK_BOT_TOKEN")
-    if slack_bot_token is None:
-        logging.error("SLACK_BOT_TOKEN is not set")
-        sys.exit(1)
+    logging.info("Connecting to Slack...")
 
     # initialize SocketModeClient with an app-level token + AsyncWebClient
     client = SocketModeClient(
@@ -112,3 +95,11 @@ def setup_slack_client(slack_bot: ApiBot | Bot) -> SocketModeClient:
     client.socket_mode_request_listeners.append(slack_bot)
 
     return client
+
+
+async def connect_client(client: SocketModeClient) -> None:
+    await client.connect()
+    # listen for events
+    logging.info(LISTENING_MSG)
+    # TODO: Assess whether this is best to use
+    await asyncio.sleep(float("inf"))
diff --git a/reginald/utils.py b/reginald/utils.py
index 673fe0e5..720e5206 100644
--- a/reginald/utils.py
+++ b/reginald/utils.py
@@ -1,7 +1,6 @@
 import logging
 import os
 from itertools import chain
-from time import sleep
 from typing import Any, Callable, Final, Generator, Iterable
 
 from rich.progress import Progress, SpinnerColumn, TextColumn
diff --git a/tests/test_chat_interact.py b/tests/test_chat_interact.py
index 2379372c..1cb68a0f 100644
--- a/tests/test_chat_interact.py
+++ b/tests/test_chat_interact.py
@@ -5,19 +5,21 @@
 from typer.testing import CliRunner
 
 from reginald.cli import cli
-from reginald.models.models.hello import Hello
-from reginald.run import run_chat_interact
+from reginald.models.chat_interact import art, run_chat_interact
+from reginald.models.simple.hello import Hello
 
 runner = CliRunner()
+art_split = art.splitlines()
 
 
 def test_chat_cli():
     """Test sending an input `str` via `cli` and then exiting."""
     result = runner.invoke(cli, ["chat"], input="What's up dock?\nexit\n")
     term_stdout_lines: list[str] = result.stdout.split("\n")
-    assert term_stdout_lines[0] == ">>> "
-    assert term_stdout_lines[1] == "Reginald: Hello! How are you?"
-    assert term_stdout_lines[2] == ">>> "
+    assert term_stdout_lines[: len(art_split)] == art_split
+    assert term_stdout_lines[len(art_split) + 1] == ">>> "
+    assert term_stdout_lines[len(art_split) + 2] == "Reginald: Hello! How are you?"
+    assert term_stdout_lines[len(art_split) + 3] == ">>> "
 
 
 def test_chat_cli_no_stream():
@@ -26,9 +28,13 @@ def test_chat_cli_no_stream():
         cli, ["chat", "--no-streaming"], input="What's up dock?\nexit\n"
     )
     term_stdout_lines: list[str] = result.stdout.split("\n")
-    assert term_stdout_lines[0] == ">>> "
-    assert term_stdout_lines[1] == "Reginald: Let's discuss this in a channel!"
-    assert term_stdout_lines[2] == ">>> "
+    assert term_stdout_lines[: len(art_split)] == art_split
+    assert term_stdout_lines[len(art_split) + 1] == ">>> "
+    assert (
+        term_stdout_lines[len(art_split) + 2]
+        == "Reginald: Let's discuss this in a channel!"
+    )
+    assert term_stdout_lines[len(art_split) + 3] == ">>> "
 
 
 def test_chat_interact_exit():
@@ -58,14 +64,16 @@ def test_chat_interact_bye():
 def test_chat_interact_clear_history():
     result = runner.invoke(cli, ["chat"], input="clear_history\n")
     term_stdout_lines: list[str] = result.stdout.split("\n")
-    assert term_stdout_lines[0] == ">>> "
-    assert term_stdout_lines[1] == "Reginald: No history to clear."
-    assert term_stdout_lines[2] == ">>> "
+    assert term_stdout_lines[: len(art_split)] == art_split
+    assert term_stdout_lines[len(art_split) + 1] == ">>> "
+    assert term_stdout_lines[len(art_split) + 2] == "Reginald: No history to clear."
+    assert term_stdout_lines[len(art_split) + 3] == ">>> "
 
 
 def test_chat_interact_slash_clear_history():
     result = runner.invoke(cli, ["chat"], input="\clear_history\n")
     term_stdout_lines: list[str] = result.stdout.split("\n")
-    assert term_stdout_lines[0] == ">>> "
-    assert term_stdout_lines[1] == "Reginald: No history to clear."
-    assert term_stdout_lines[2] == ">>> "
+    assert term_stdout_lines[: len(art_split)] == art_split
+    assert term_stdout_lines[len(art_split) + 1] == ">>> "
+    assert term_stdout_lines[len(art_split) + 2] == "Reginald: No history to clear."
+    assert term_stdout_lines[len(art_split) + 3] == ">>> "

From 20a3eb5a8d35bc9ae1a33574a57eb1c5dd1ccf2f Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 22:48:11 +0100
Subject: [PATCH 04/11] fix error in llama-index-azure-gpt model getter

---
 poetry.lock                      | 2 +-
 pyproject.toml                   | 2 +-
 reginald/models/__init__.py      | 4 ++--
 reginald/models/chat_interact.py | 2 ++
 4 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index a5c2ccf1..a5c8cf7c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -5096,4 +5096,4 @@ azure = ["azure-storage-file-share", "pulumi", "pulumi-azure-native"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<3.12"
-content-hash = "6c025f7263718fd83e17df5ff9f4fbacfdef3e41e3338d2567859b58e50d6050"
+content-hash = "4d7330aee8ed44e7b403e2a21fc304c310e2ae5f9fba6a72079257869506a085"
diff --git a/pyproject.toml b/pyproject.toml
index cfd99cf4..11e98a96 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,7 +25,7 @@ httpx = "^0.27.0"
 llama-cpp-python = "^0.2.78"
 llama-index = "^0.10.43"
 nest_asyncio = "^1.6.0"
-openai = "^1.33.0"
+openai = "^1.34.0"
 pandas = "^2.2.2"
 pulumi = { version="^3.100.0", optional=true }
 pulumi-azure-native = { version="^2.24.0", optional=true }
diff --git a/reginald/models/__init__.py b/reginald/models/__init__.py
index 5988bc26..5e4684d7 100644
--- a/reginald/models/__init__.py
+++ b/reginald/models/__init__.py
@@ -44,8 +44,8 @@ def get_model(model_name: str):
 
                 return LlamaIndexHF
             case "llama-index-gpt-azure":
-                from reginald.models.llama_index.llama_index_openai import (
-                    LlamaIndexGPTOpenAI,
+                from reginald.models.llama_index.llama_index_azure_openai import (
+                    LlamaIndexGPTAzure,
                 )
 
                 return LlamaIndexGPTOpenAI
diff --git a/reginald/models/chat_interact.py b/reginald/models/chat_interact.py
index 391d17f6..1bc5cfaa 100644
--- a/reginald/models/chat_interact.py
+++ b/reginald/models/chat_interact.py
@@ -25,6 +25,8 @@ def run_chat_interact(streaming: bool = False, **kwargs) -> ResponseModel:
         message = input(">>> ")
         if message in ["exit", "exit()", "quit()", "bye Reginald"]:
             return response_model
+        if message == "":
+            continue
         if message in ["clear_history", "\clear_history"]:
             if (
                 response_model.mode == "chat"

From cfc52404caefaf6a11a7e144301620ba0d68531c Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 22:50:55 +0100
Subject: [PATCH 05/11] update handbook indices

---
 data/llama_index_indices/handbook/default__vector_store.json | 1 +
 data/llama_index_indices/handbook/docstore.json              | 2 +-
 data/llama_index_indices/handbook/image__vector_store.json   | 1 +
 data/llama_index_indices/handbook/index_store.json           | 2 +-
 4 files changed, 4 insertions(+), 2 deletions(-)
 create mode 100644 data/llama_index_indices/handbook/default__vector_store.json
 create mode 100644 data/llama_index_indices/handbook/image__vector_store.json

diff --git a/data/llama_index_indices/handbook/default__vector_store.json b/data/llama_index_indices/handbook/default__vector_store.json
new file mode 100644
index 00000000..0ebe2473
--- /dev/null
+++ b/data/llama_index_indices/handbook/default__vector_store.json
@@ -0,0 +1 @@
+{"embedding_dict": {"75cf5ad8-bf0e-4b67-b3dd-ed3272881b09": [0.013197319582104683, 0.011916624382138252, -0.05129290744662285, 0.0015251581789925694, -0.059926245361566544, -0.0037623224779963493, 0.04194905608892441, 0.01710866577923298, -0.0034507750533521175, 0.0021076216362416744, 0.027662940323352814, 0.01799507811665535, 0.005806008819490671, 0.09388380497694016, 0.022276535630226135, -0.042984429746866226, 0.03962317481637001, -0.012565327808260918, -0.03009520284831524, -0.0126412408426404, 0.009960923343896866, -0.003316539339721203, -0.02211957424879074, 0.022943269461393356, 0.03884167596697807, -0.03142911568284035, -0.007834000512957573, 0.006806707940995693, 0.05822954326868057, 0.029071465134620667, 0.011245799250900745, 0.03667028620839119, -0.024421261623501778, 0.06730309128761292, 2.666602767931181e-06, -0.02253924123942852, -0.0255073681473732, 0.0651547759771347, -0.00035738974111154675, 0.0071036722511053085, 0.05586494505405426, 0.06463126093149185, 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It is not a comprehensive guide to Hugo or any of the other tools used. Instead it is aimed to be a user-focused guide explaining how add to or edit the handbook without dwelling on the details of the underlying technologies. For those interesting in learning more, links will be liberally placed throughout for further reading. ", "doc_id": "fcd6893a-2299-4252-a491-58ea3ab2027d", "embedding": null, "doc_hash": "15b5c30a51cb56cc0f8aef9a21d5b3fe9540cc61decc853eb191120dd1c1ed39", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/"}, "node_info": {"start": 0, "end": 406, "_node_type": "1"}, "relationships": {"1": "e8ff93ab-6b4c-46a4-9bea-e54d3508987a"}}, "__type__": "1"}, "0136ae18-ab5b-403a-8e88-2246e8600382": {"__data__": {"text": "Advanced Using Data Through using templates Hugo can build page content from datafiles . This is particularly useful for when you want to display structured data in a page and when it would be easier to maintain a datafile rather than a Markdown or HTML document. Creating Shortcodes It is possible to create your own shortcodes . These should be placed in layouts/shortcodes/\n. If you feel the need to use HTML or want to create page content from a datafile (like a YAML file or csv) then a shortcode is probably the right answer. You should refer to Hugo\u2019s templates and functions documentation for resources to help writing a shortcode. Partial Templates Whole page templates are built from of a number of smaller partial templates . This approach reduces repeated code in templates and help keep making changes simple. The theme defines a number of dummy partial templates for us to overwrite. For example, the Creative Commons notice at the bottom of each page was added by editing layouts/partial/docs/inject/footer.html\n. ", "doc_id": "0136ae18-ab5b-403a-8e88-2246e8600382", "embedding": null, "doc_hash": "bb160b351072984821329e3dccc01108ffd0d52e320a2e34a79366f4380ff3e4", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/advanced/"}, "node_info": {"start": 0, "end": 1029, "_node_type": "1"}, "relationships": {"1": "cfb5a5b5-d72d-40da-bd3c-a26cf598c742"}}, "__type__": "1"}, "2918ad37-92c2-4140-93ff-faef72f2d9a8": {"__data__": {"text": "Recognising Contributions We aim to recognise all contributions to the handbook, not just those made through commits. For that purpose we use All Contributors to help manage a record of who has contributed to the book and how. Use the \u2018add\u2019 command in an issue or pull-request comment to ask @all-contributors to add a contributor @all-contributors please add @<username> for <contributions> See the bot usage documentation for full details and more examples. A list of contribution types (and the related emoji) can be seen here . ", "doc_id": "2918ad37-92c2-4140-93ff-faef72f2d9a8", "embedding": null, "doc_hash": "acf72fec2b10597c7ebd19741dc641af71346b6f7c0f6f16e2fea379cca8aa5c", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/recognising_contributions/"}, "node_info": {"start": 0, "end": 532, "_node_type": "1"}, "relationships": {"1": "818f387d-a522-4212-8a2d-3fa821940cc2"}}, "__type__": "1"}, "574bcc25-d760-4690-9e9c-734ca1a2faca": {"__data__": {"text": "Editing a Page If you followed the instructions in the Getting Started section to checkout the repository and serve the handbook locally you can edit a page locally. However, as you may have noticed, at the bottom of each page is a link to edit the page in the GitHub web editor if you would prefer. This may be easy for making small changes. Pages Each page is a Markdown file with YAML front matter followed by the page contents in Markdown. Front Matter The front matter is used to define various pieces of metadata related to a page. The front matter appears at the top of a content file. In the handbook we format front matter as YAML, preceded and followed by three hyphens. --- title : \"Example Page\"\nweight : 1\n--- The full YAML specification is long and comprehensive. The most important thing to understand here is that the front matter YAML consists of keys and values separated by a hyphen. For example, in the expression weight: 1 , weight is the key with a value of 1 . If you created a page using hugo new then some boilerplate front matter with explanatory comments should already be present. If you are editing an existing page there should already be front matter. Most of the time, the only keys you will need to consider are title and weight . title The title of a page as it appears in the navigation menu weight Determines the order of pages in the navigation menu. Smaller numbers appear first The Hugo documentation details a set of predefined front matter keys . Other valid fields related to the handbook theme are documented in the theme\u2019s README . Content After the front matter is the content section. --- title: \"Example Page\" weight: 1 ---\n| content section | The content section is formatted in Markdown. Markdown Guide outlines the basic and extended . Not all of the extended Markdown syntax may be implemented by Hugo\u2019s Markdown renderer Shortcodes Shortcodes\nare templates (which can be parametrised) and included the content section. They can be particularly useful for including more complex features than Markdown\u2019s simple syntax will allow. Using a shortcode is preferable to including raw HTML in a content file. Shortcodes can called in Markdown content files the following way {{% shortcode parameter %}} Some shortcodes may have an opening and closing tag, similar to HTML {{% shortcode %}} content to process {{% /shortcode %}} Hugo Shortcodes Hugo has a set of useful built-in shortcodes . In particular figure gist highlight param ref and relref Theme Shortcodes The theme has a number of shortcodes which may be helpful. In particular hints expand KaTeX tabs Repository Shortcodes Shortcodes can be included in the handbooks repository. For more information see Creating Shortcodes repo_link Create a link to a file or directory in the handbook\u2019s repository. {{% repo_link path=\"README.md\" text=\"README\" %}} path Path to the file or directory relative to the root of the repository text Text of the link (can include Markdown formatting) ", "doc_id": "574bcc25-d760-4690-9e9c-734ca1a2faca", "embedding": null, "doc_hash": "8b91442b19b0c04b06abfad82264ca93377090496db7ecaa9e0fea3a7ce87b49", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/editing_a_page/"}, "node_info": {"start": 0, "end": 2986, "_node_type": "1"}, "relationships": {"1": "7bc907be-6161-46a0-ae00-0279ce879d4f"}}, "__type__": "1"}, "d9b75cc2-4b54-4de9-834d-ac8d43513af6": {"__data__": {"text": "Reviewing Changes The review process helps to ensure high quality and catch problems in contributions. When acting as a reviewer, you should see your position as sharing your knowledge and working with the contributors to achieve the best possible result. Reviewing should not be an adversarial process. Recognising Contributions When reviewing a pull request you should try to make sure everyone\u2019s work is recognised. See the Recognising Contributions page for how to do this. Code Quality As a reviewer, one of your jobs is to ensure the quality of the codebase remains high. The CI process will help to assess pull requests by subjecting each commit to a series of tests. Some tests are required to pass and will block merging until they do. Other tests are allowed to fail. This is because these tests check external hyperlinks which can fail for reasons out of our control. For example, a website being offline or a rate limiting API. However, you should always aim to have all tests passing and investigate why any test fails. In particular, a genuinely incorrect link should not be ignored. You should always clone the branch, build the handbook locally (using hugo server --minify ) and inspect the changes using your browser. Not all bugs will be caught by CI and not all changes will be obvious in the source files. Reviewing the Pull Request Use the GitHub review system\nto check the diffs of all source files. You should make use of line comments\nwhere you have comments or questions about particular lines of sections. This gives context so that everyone knows where the problem is or what the question refers to. Line comments can also be used to suggest changes (using the \u00b1 button). You should do this when you have a simple solution. This is an excellent way to share knowledge. When you are finished, submit your review\nmaking sure to choose \u201ccomment\u201d, \u201capprove\u201d or \u201crequest changes\u201d as appropriate. Changes If you request changes, the pull request will enter an iterative process where the contributors make adjustments you repeat the review process. The contributors may accept your proposed changes, make their own changes or push back against changes. All of these may be appropriate. It is important to work together with the contributors to resolve any conversations. All conversations must be resolved before merging. Merging Update your review status to \u201capprove\u201d when you are happy with the state of the pull request. When all reviewers are satisfied, merge the pull request. ", "doc_id": "d9b75cc2-4b54-4de9-834d-ac8d43513af6", "embedding": null, "doc_hash": "22dfb1589aba2ea8422191f9cf01d15b35593cb541c5cc5028448d88fa94b2b6", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/reviewing_changes/"}, "node_info": {"start": 0, "end": 2503, "_node_type": "1"}, "relationships": {"1": "082e5499-fe3f-4bbb-8c20-036125497c60"}}, "__type__": "1"}, "62bb3d89-c999-4a68-9f10-63b27f8fe51f": {"__data__": {"text": "Adding New Content How Hugo Arranges Content Pages are build from content files in the content/\ndirectory. Hugo automatically gives pages a URL based on the organisation of files in the content/ directory. In Hugo, the first directory after content/ is significant and defines the content type . All handbook pages should be nested in the content/docs/ directory. Sections may be created in content/docs/ (to any depth) by creating a directory containing a content file called _index.md . It is important that the index file exists for Hugo to correctly assign pages to sections and for the theme to arrange pages in the tree-like menu. Creating a New Page To create a new handbook page use the hugo new command. hugo new content/docs/<path/to/page>.md If you wanted to create this page, you would type hugo new content/docs/contributing/creating_a_page.md Hugo will create a new Markdown document at the path you specified. Hugo provides a convenient way to generate new pages from templates called Archetypes . You can see the archetypes in the archetypes/ directory\n. Because your document is in the docs directory, Hugo will look for an archetype called docs.md\nto use as a template. Now you can edit your page with your favourite text editor. For example vim content/docs/contributing/creating_a_page.md Creating a Section We can also use the hugo new command to create a new section, both the directory and index file. For example hugo new content/docs/new_section/_index.md vim content/docs/new_section/_index.md If you only want a section to organise some pages and not to have a page of its own, you can simply leave the content section of _index.md (after the YAML front matter) empty. ", "doc_id": "62bb3d89-c999-4a68-9f10-63b27f8fe51f", "embedding": null, "doc_hash": "7a1f282fa82b5cfca49ec86fd7d1a60ffba39b470edef33a2ec71552b9e4a803", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/creating_a_page/"}, "node_info": {"start": 0, "end": 1696, "_node_type": "1"}, "relationships": {"1": "0b40b2c9-ab5d-43eb-8e72-87de4c8439cf"}}, "__type__": "1"}, "62ad20de-4bd1-4ce4-b188-02ee3afa832c": {"__data__": {"text": "Contributing Changes Once you have made changes, you will need to contribute them back to the remote repository. This page explains the process of pushing your changes back to the remote, and how your contribution will be checked in the CI and review process. The handbook users the GitHub flow workflow. In short create a branch make changes open pull request review merge to main This workflow should help changes be deployed quickly. When making commits, it is a good idea to follow Tim Pope\u2019s recommendations\nfor good commit messages. Creating a Branch You should make your changes on a new branch, and not on main . This will make it easier to merge changes from many contributors. Try to pick a branch name which is short and describes the change you are making. The changes on one branch or pull request should address a single issue and be self-contained. Don\u2019t try to solve more than one unrelated problem at once. Sticking to this practice will help ensure pull requests are small and easy to review. Otherwise, you might end up having to make many changes during review, have difficultly keeping your branch up to date or upset the reviewers. Opening a Pull Request When you think your changes are ready to be merged, open a pull request. If you don\u2019t want your changes to be merged yet but would like some feedback, you can open a draft pull request. When you open a pull request you will be presented with a template. Complete the sections and answer the questions as best you can. In particular, make sure to reference any issues your work closes or is related to. Continuous Integration A series of tests will be automatically conducted on each commit and pull request. The tests are defined in ci.yaml\n. The following tests are required to pass before merging a pull request A Markdown linter A Hugo build There are other test that are not required before merging, although you should aim to pass all tests. Those are A Markdown link checker A HTML proofer (including link checking) on the output of Hugo Keeping Your Branch up to Date The branch protection rules applied to main will prevent merging out-of-date branches. This means if your branch doesn\u2019t include the changes from the HEAD\nof origin/main it won\u2019t be possible to merge it. If your branch isn\u2019t up to date, you can fix it with a merge or rebase. Merging Merging\nthe changes from origin/main is probably the simpler way to get your branch up to date. It will leave the Git history a bit messier which can bother some people. Ensure you have the latest changes on origin/main in your local repository git fetch Merge origin/main into your branch. If your branch was called my_branch you would run git switch my_branch git merge origin/main Git will notify you of any conflicts. If there are conflicts you should fix them before running git merge --continue to commit. After you are done you can push your changes. Rebasing Rebasing your branch on top of origin/main can make conflicts more difficult or confusing to deal with. Ensure you have the latest changes on origin/main in your local repository git fetch Apply your commits on top of origin/main . If your branch was called my_branch you would run git switch my_branch git rebase origin/main If you want to tidy up your commits or messages you might want to use git rebase -i . Git will notify you of any conflicts. If there are conflicts you will need to fix them iteratively, running git rebase --continue to apply each commit. When the rebase is done, you will need to force push your changes as you have rewritten the history of your branch. git push -f Rewriting history is considered impolite on a branch where you are working collaboratively. It can cause problems when the state of a remote changes unexpectedly. The Review Process Currently reviews are not required and pull requests may be merged by anyone with write access once all required CI checks pass. If you like you can enable auto-merge\nso that the merge happens automatically when this is the case. However, if you\u2019d like your changes reviewed you can suggest reviewers when, or after, opening your pull request. If a reviewer then requests any changes or makes any comments, these should be resolved before the pull request is merged. ", "doc_id": "62ad20de-4bd1-4ce4-b188-02ee3afa832c", "embedding": null, "doc_hash": "377af2447f73132bee0e9289429e46ef1378e5788e5d79bcf66eab654604770e", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/contributing_changes/"}, "node_info": {"start": 0, "end": 4244, "_node_type": "1"}, "relationships": {"1": "520c5124-2147-4dab-bf3e-4cb029c7f436"}}, "__type__": "1"}, "75867960-b912-4f6e-9246-f0037fe39a38": {"__data__": {"text": "Discussions and Issues Contributing does not only mean adding code or writing pages. Being involved in reporting issues and discussing ideas are important and valuable aspects to contributing. The handbook uses both issues and discussions on GitHub. Discussions The handbook Discussions\nare the best place for informal talk about the handbook. You should feel welcome to create a discussion on any relevant topic, without the formality of an issue. Good examples of discussions are Any questions Possible bugs (does anyone else have this problem?) Chapter suggestions Looking for collaborators Community support Any other on-topic talk Issues The issue tracker is best used for development work. This is because issues integrate well with GitHub development tools like projects, pull requests, assignments and so on. Each issue should ideally represent a well-defined, self-contained piece of work suitable to become a single pull request. Good examples of issues are Bug reports with technical detail Developed chapter proposals Feature requests (such as new shortcodes) Specific ideas for changes When opening an issue, pick a suitable template (if any) to make the process easier. ", "doc_id": "75867960-b912-4f6e-9246-f0037fe39a38", "embedding": null, "doc_hash": "bec05483d06bf19213ddab5fcc8b5b5e9b3c7423d717733be4fe0aa1dff4e621", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/discussions_and_issues/"}, "node_info": {"start": 0, "end": 1184, "_node_type": "1"}, "relationships": {"1": "dd808e38-620c-4cdb-abc4-75ad9a42b39c"}}, "__type__": "1"}, "18541b6e-100d-427b-9e3e-efe271e15ab8": {"__data__": {"text": "Style Guide Semantic Line Breaks It is encouraged to use Semantic Line Breaks when writing for the handbook. This improves the readability of source files and make diffs clearer. The Semantic Line Breaks specification explains the rules and reasoning. The most important rules are, A semantic line break MUST occur after a sentence, as punctuated by a period (.), exclamation mark (!), or question mark (?). A semantic line break SHOULD occur after an independent clause as punctuated by a comma (,), semicolon (;), colon (:), or em dash (\u2014). A semantic line break MAY occur after a dependent clause in order to clarify grammatical structure or satisfy line length constraints. Markdown Whenever possible, the handbook\u2019s prose should be written in Markdown rather than HTML. However, it is completely reasonable use HTML when it is needed. When it is advantageous, data should be stored in data files and processed using shortcodes rather than presented in raw Markdown. Style Enforcement Markdown styling is enforced by markdownlint using the configuration .mdl_style.rb\n. An explanation of the rules can be found here . You can optionally install markdownlint locally to check your changes before submitting a pull request. Make sure you install the Ruby version ( mdl ) and not the Node.js version ( markdownlint-cli ) contained in the homebrew repositories, since the configuration is only good for the former. Once installed and assuming you\u2019re in the repository root, you can lint a specific file you\u2019ve made changes to like this: mdl -s .mdl_style.rb <filename> Alternatively run it over the entire handbook content like this: mdl -s .mdl_style.rb ./content ", "doc_id": "18541b6e-100d-427b-9e3e-efe271e15ab8", "embedding": null, "doc_hash": "62bdba179f5a5de7e2af6f54114f7815f27fd2a04a147510214999fe823ab176", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/style_guide/"}, "node_info": {"start": 0, "end": 1665, "_node_type": "1"}, "relationships": {"1": "4672ee2d-fc36-403b-a2f4-0d7b47e54c90"}}, "__type__": "1"}, "c56de485-bf90-4d6c-92ad-bee5e4906542": {"__data__": {"text": "Getting Started Install dependencies To contribute to the handbook you will need Git Hugo (You will need the extended version with Sass/SCSS support) Your favourite text editor Optionally markdownlint ; see the Style Guide\nfor more details. Clone the repository Clone the repository with one of the following options (depending on how you authenticate with GitHub):\nSSH authentication git clone --recurse-submodules git@github.com:alan-turing-institute/REG-handbook.git cd REG-handbook\nHTTPS authentication git clone --recurse-submodules https://github.com/alan-turing-institute/REG-handbook.git cd REG-handbook The --recurse-submodules flag is important as the handbook\u2019s theme is included as a Git submodule Serve the handbook locally While working on the handbook, it will be useful to preview your changes locally. To do this use Hugo\u2019s server command hugo server --minify In the output of this command will be a line similar to Web Server is available at http://localhost:1313/REG-handbook/ ( bind address 127.0.0.1 )\nwhich contains the URL of the locally served site. Navigate to this address in your browser to preview the handbook. The Hugo server will monitor the files in the repository and automatically rebuild the site whenever you make changes. This is a useful tool to immediately view your changes. ", "doc_id": "c56de485-bf90-4d6c-92ad-bee5e4906542", "embedding": null, "doc_hash": "72955dd9a83694e754e2286e4e7ec7adb9ced870d795b3a5db92a14082de4d8d", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributing/getting_started/"}, "node_info": {"start": 0, "end": 1315, "_node_type": "1"}, "relationships": {"1": "3e2c2087-2447-4036-89c6-c3b476ba1980"}}, "__type__": "1"}, "7e68274e-9ab6-471e-9185-c944c59cfaa8": {"__data__": {"text": "Projects This section describes how we coordinate our project work. It does not go into details of how individual projects are managed, but how REG coordinates its commitments to the range of projects it\u2019s involved in. ", "doc_id": "7e68274e-9ab6-471e-9185-c944c59cfaa8", "embedding": null, "doc_hash": "289dca7f46b829a4c7112a67257adee1c0233cf9f3d97659fc1aa298cdad82c0", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/projects/"}, "node_info": {"start": 0, "end": 219, "_node_type": "1"}, "relationships": {"1": "992e554d-6658-4f10-89c8-5de9396afd51"}}, "__type__": "1"}, "33eadcb9-33ef-4e1c-bee7-03bfbc864821": {"__data__": {"text": "Project Tracking This page describes some of the tools we use for tracking projects and other tasks within the team. Hut23 GitHub Repo The Hut23 repo is the central hub for most team matters and admin. Project Tracker : Board shows all upcoming, current and completed projects. There\u2019s an issue for each project (see below). Depending on the project status, it will be in one column or another. In order to assign REG team members to projects, we use emojis to vote for the projects that we would be interested in taking part. You should react to all projects on the \u201cFinding people\u201d column, but also on the \u201cAwaiting go/no-go\u201d column. The voting system is as follows: \ud83d\udc4d Would be okay working on this project \ud83d\udc4e Would rather not work on this project \ud83d\ude04 Would really like to work on this project Issues : Each project has an issue following a common template, including a general description of a project and its current status, for example. There are also issues for service areas and other activities/organisational issues for the team. Project Repos All repos in the Turing organisation that REG have worked on should have the hut23 tag. So if you\u2019re looking for something from a previous project searching for these tags may be a good place to start. Additionally, they should have a tag of the format hut23-123 , where 123 is the issue number for that project in the Hut23 repo. Forecast & Wimbledon We use Forecast to track everyone\u2019s allocations to projects. You can see which projects & service areas everyone in the team is currently involved in, as well as what they were allocated to in the past and will be allocated to in the future. \u201c Wimbledon planner \u201d is an online version of a poster we previously had in the office to display the information in Forecast. It\u2019s easier to see allocations for a longer period, and you can click project names to take you directly to the issue describing that project in the Hut23 repo. ", "doc_id": "33eadcb9-33ef-4e1c-bee7-03bfbc864821", "embedding": null, "doc_hash": "4035f8253070bc0c00bdb78a5316e20af0e21c2508cb6619a6aeb5413f6733a0", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/projects/project_tracking/"}, "node_info": {"start": 0, "end": 1932, "_node_type": "1"}, "relationships": {"1": "439c3118-d6dd-4617-82d9-97ad4cf51bbd"}}, "__type__": "1"}, "64015259-4712-473b-83aa-101859b847e3": {"__data__": {"text": "Regular Events Coffee Chats Coffee Chats REG Events Lunchtime tech talks Lightning talks Reading groups Outward-facing Events Drop-in Sessions ", "doc_id": "64015259-4712-473b-83aa-101859b847e3", "embedding": null, "doc_hash": "26bae2c2963aafecfa270625ba66c915cf7fcf55c465282c0598f9020396f2a6", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/"}, "node_info": {"start": 0, "end": 143, "_node_type": "1"}, "relationships": {"1": "71c71cdb-ad88-4a6c-91a5-3604f6c5df22"}}, "__type__": "1"}, "9755dfce-542c-4fd4-9159-be398121c20a": {"__data__": {"text": "Coffee Chats The team has two optional, daily coffee chats at 08:45 and 13:45. The chats usually happen in Gather but there is also a Zoom room in case Gather is unavailable. Details for both rooms can be found here . ", "doc_id": "9755dfce-542c-4fd4-9159-be398121c20a", "embedding": null, "doc_hash": "6d4d80ba889adac7dbbf4b084df960a442e7333356db9e62a2aff25ca03fd614", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/coffee_chats/"}, "node_info": {"start": 0, "end": 218, "_node_type": "1"}, "relationships": {"1": "d9440e1a-d8f3-45c1-ac9b-8d14f09b0870"}}, "__type__": "1"}, "3d62184d-17c2-489c-a1d6-6a328080a6b1": {"__data__": {"text": "Drop-in Sessions The group runs regular, informal, community-led sessions for students and researchers. At the sessions they can ask the group about specific technical problems, or seek general technical advice. The sessions offer a friendly, welcoming, non-judgemental environment to receive advice or assistance without concern about being judged on technical knowledge, expertise or understanding. As our organisers and helpers know, many of us working with software in the research community have been trained in domains where computing has not traditionally been a key element. There can be lots to learn in terms of new jargon and technical skills and we all have to start somewhere. We can help with: General programming problems \u2014 including helping to identify and fix bugs. Cloud computing questions \u2014 including how to get started on Azure. Software sustainability \u2014 best practices for testing, managing and packaging your code. Reproducible research \u2014 ensuring software is developed to support long-term reproducibility, including techniques for packaging and archiving code. High Performance Computing (HPC) \u2014 support and advice on the HPC resources available at the Turing. Joining sessions Our weekly sessions run for one hour from 13:30-14:30 every Tuesday and are hosted on Zoom . Everyone is welcome to drop-in at any time. There is no need to register in advance. ", "doc_id": "3d62184d-17c2-489c-a1d6-6a328080a6b1", "embedding": null, "doc_hash": "14a6ae432a9e870eda8bdac6066cec1b1aebfdc112b26b24e56b04faa57feb61", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/drop-in_sessions/"}, "node_info": {"start": 0, "end": 1381, "_node_type": "1"}, "relationships": {"1": "a96a9396-e221-4b06-a82d-dc66c66148ef"}}, "__type__": "1"}, "2c432e8c-1357-4d82-bb70-f06d795ffb78": {"__data__": {"text": "Lightning Talks Lightning Talks are a meeting series within the tech talk slots in which people give short 10 minute talks. The main goals are to: Regularly share what we\u2019re working on. Find overlaps between projects or problems someone else in the team could help with. Generate ideas for future tech talks. Do all this without a big time commitment, both for presenters and attendees. The talks can be on any topic, including but not limited to: A project (including one that hasn\u2019t started yet). A tool or technique you use. Something you\u2019d like help with. Informal talks or discussions without slides or much preparation are encouraged and very welcome! Talk Format Each slot will be 10 minutes with a few minutes after for questions. We will be fairly strict with time to make sure everyone on the agenda gets to present. You can choose whatever format you like (slides, markdown, notebook, freestyle\u2026), but remember that it\u2019s a short slot which lends itself to concise, high-level overviews of a topic. For example, if your talk is on a project you may wish to cover the following: Background: The domain area, context, problem etc. . Goals: What are the aims of the project? Status: What\u2019s been done so far, example results (if any). Tools & Techniques: What you\u2019re using, what you\u2019ve learnt, what you\u2019d like help with. Summary: who\u2019s working on the project, dates, GitHub link etc. . If you\u2019d like to prepare slides, there\u2019s a PowerPoint template and examples from previous talks under here that can help you get started. Sign Up To sign up to present a Lightning Talk, please add yourself to the signup list Previous Talks You can find a list of previous talks and their slides here . ", "doc_id": "2c432e8c-1357-4d82-bb70-f06d795ffb78", "embedding": null, "doc_hash": "bbd732561186a55870828de158dca4eaacbeb1133781abc310c870cca0e8206b", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/lightning_talks/"}, "node_info": {"start": 0, "end": 1694, "_node_type": "1"}, "relationships": {"1": "7f100d62-022a-451a-a1f4-967e62be7b04"}}, "__type__": "1"}, "112a5e73-1b8e-4e1c-8d4d-d86c9fffc995": {"__data__": {"text": "Reading Groups As part of projects or for general interest, members of the team create reading groups to learn about a topic. Reading groups may meet on weekly basis to discuss a chapter from a book, a paper, or to work together on implementing something. A list of current reading groups and information about them can be found on the Data Science Skills wiki . Material for the reading groups is stored in the Data Science Skills repository There are also Turing Interest Groups and many other informal groups around the Turing. ", "doc_id": "112a5e73-1b8e-4e1c-8d4d-d86c9fffc995", "embedding": null, "doc_hash": "c0daed071aec0c243993d208301d990d818dc6dc374f9afc40321aacbb58b36a", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/reading_groups/"}, "node_info": {"start": 0, "end": 531, "_node_type": "1"}, "relationships": {"1": "95a412e4-efb9-4a7a-8bdb-696965069c19"}}, "__type__": "1"}, "a6ae574c-ce44-46ea-8bcd-51daeadd4e33": {"__data__": {"text": "Lunchtime Tech Talks The Research Engineering team meets for Lunchtime Tech Talks most Tuesdays over lunch. The talks start at 12:30. At a tech talk someone (usually someone in REG) presents or discusses something, such as about a project or data science/computer science/software engineering topic. They can also be opportunities to look for help and input. We encourage talks at the beginning of projects (before it\u2019s certain what exactly you\u2019ll be doing) or to discuss a problem you\u2019re unsure how to solve. We also have shorter format lightning talks , where several people give a shorter talk. The Data Science Skills wiki contains the talks schedule, calendar invitation instructions and information on signing up for a talk. ", "doc_id": "a6ae574c-ce44-46ea-8bcd-51daeadd4e33", "embedding": null, "doc_hash": "b0cf17f86d4011ee201f97965f34d447124617ae835293faa1c07c11df99dfde", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/regular_events/lunchtime_tech_talks/"}, "node_info": {"start": 0, "end": 731, "_node_type": "1"}, "relationships": {"1": "d8d61bab-9e58-4ade-9a14-a63b787c2bc3"}}, "__type__": "1"}, "e08d3482-69bf-47bb-baf8-38dfe358b0c3": {"__data__": {"text": "Contributors In this table we try to capture everyone who has contributed to the project and how they have contributed. The table is generated and maintained using All Contributors . Andy Smith \ud83d\udd8b  \ud83d\udc40  \ud83e\udd14  \ud83d\udcbb  \ud83c\udfa8 Christina Last \ud83d\udd8b David Beavan \ud83e\udd14  \ud83d\udc40 David Llewellyn-Jones \ud83d\udd8b  \ud83e\udd14  \ud83d\udc1b  \ud83d\udcd6  \ud83d\udc40 David Salvador Jasin \ud83d\udd8b Federico Nanni \ud83d\udd8b  \ud83e\udd14 Gabriel \ud83d\udd8b  \ud83e\udd14 Isabel Fenton \ud83d\udc40 Jack Roberts \ud83d\udd8b James Robinson \ud83d\udd8b Jim Madge \ud83d\udcbb  \ud83d\udd8b  \ud83d\udcd6  \ud83d\udc40  \ud83e\udd14  \ud83d\ude87  \ud83d\udc1b Jonathan Yong \ud83d\udd8b  \ud83e\udd14 Katriona Goldmann \ud83d\udd8b  \ud83d\udc40 Levan Bokeria \ud83d\udc1b  \ud83d\udd8b Lydia France \ud83d\udd8b Malvika Sharan \ud83e\udd14 Markus Hauru \ud83d\udd8b  \ud83e\udd14 Martin O'Reilly \ud83e\udd14 Oscar T Giles \ud83e\udd14 Radka Jersakova \ud83d\udd8b Tim Hobson \ud83d\udd8b helenashowell \ud83d\udd8b  \ud83e\udd14 martin \ud83d\udd8b nbarlowATI \ud83e\udd14 ", "doc_id": "e08d3482-69bf-47bb-baf8-38dfe358b0c3", "embedding": null, "doc_hash": "ad368dd9aae70755fbdd7bf2c7c2ce72dc571c3a91c3503366407e23e1aa66d3", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/contributors/"}, "node_info": {"start": 0, "end": 630, "_node_type": "1"}, "relationships": {"1": "dbfd79ac-e69a-4052-8034-864e7fe1d39f"}}, "__type__": "1"}, "2bc907a2-81b3-4f00-996a-0d1337ddd0f4": {"__data__": {"text": "How We Work ", "doc_id": "2bc907a2-81b3-4f00-996a-0d1337ddd0f4", "embedding": null, "doc_hash": "0e514c0081bc3d3defc4fd22e544b2fa0c8160848610b4232f3bdd62a588dab5", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/how_we_work/"}, "node_info": {"start": 0, "end": 12, "_node_type": "1"}, "relationships": {"1": "3c9873c6-3747-431b-9944-534e0ddcd516"}}, "__type__": "1"}, "129527e9-ec4e-4f68-95fd-03240a7507b2": {"__data__": {"text": "Meeting Records When a synchronous meeting is unavoidable, it is critical to produce a clear and thorough record. The meeting record is important as a reference for what happened at the meeting. In particular, any decisions made or actions assigned need to be clearly recorded for future reference. A good record will also be invaluable to anyone who was not able to attend an in person meeting. Meeting Record Template Below is a suggested template for a meeting record document. The template uses Pandoc\nMarkdown . Parts of the template are already filled with explanations of how they should be used. --- title: Short meeting title date: yyyy-mm-dd author:  - List of authors of the this record abstract: | Summary of meeting purpose. Should help readers decide whether they need to read the record. keywords: [] # Pandoc LaTeX variables papersize: a4 fontfamily: fourier fontsize: 12pt colorlinks: true ---\n# Meeting Record\n## Agenda\n1. Itemised Agenda 1. Should be agreed in advance\n## Present\n- List of people present\n## Notes\n- Summary of discussion points - Use subsections if appropriate\n## Decisions\n- Record any decisions made during the meeting\n## Actions\n| Owner | Action | |-------|--------| |       |        | For printing or wider distribution, a meeting record following the template may be converted to pdf using Pandoc, pandoc meeting_record.md -o meeting_record.pdf A Pandoc YAML metadata block at the top of the template contains metadata about the meeting and helps the record render into a good looking pdf document. How To Use Create the meeting record document before the meeting. Use a collaborative text editor like HackMD so that all attendees may edit the document. Circulate the document to allow others to add and agree on agenda items. Distribute the record or store in a location visible to everyone who may need to read it. The template may also be used to avoid a meeting entirely. The document can be completed collaboratively and asynchronously while achieving the same result. ", "doc_id": "129527e9-ec4e-4f68-95fd-03240a7507b2", "embedding": null, "doc_hash": "5be3e44ceda78dbe4de244dd3611d47dd929fb129c0006fdd98188765ea1cc4b", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/how_we_work/meeting_record/"}, "node_info": {"start": 0, "end": 2015, "_node_type": "1"}, "relationships": {"1": "f57c0ea5-b736-4da2-aec2-a68e49a4c0dc"}}, "__type__": "1"}, "d1108f8f-6c82-40ca-bf1d-eba84831d72a": {"__data__": {"text": "Repo Audits As required we conduct audits of GitHub repositories created during projects. The aim is to check that the documentation is clear, installation is easy etc. Audits should be conducted by people who have not worked on the project. Past experience These comments from running the first round of these in June 2021: Installation is a big barrier for software with complex build requirements. We tried looking at two web app projects (Counterfactual Covid 19 and CROP) and despite Docker images being available we weren\u2019t able to get them to build correctly. We might have had more success if all we needed to do was a pip or CRAN install. First lesson: installation is ALWAYS harder than you think, and we need to get more fresh eyes on build instructions if we want our software to be easily usable. Second Lesson: Not having someone on hand from the project can be a big barrier, as we suspect there may have just been some small tweaks or changes that would fix our problem. One suggestion was to have a mix of unsupervised hacking and more knowledgeable feedback, maybe by having the person working on the project show up an hour in. If we can\u2019t get someone who knows the project well to attend, at least have them available on Slack in case we get stuck. At the very least, we were able to document our issues on the repo, and in at least one case the project was able to make fixes. One suggestion is to encourage \u201cGood First Issue\u201d tags or \u201cHacktoberfest\u201d-type tags that would help people know what simple things could be tackled in a few hours if they succeed in installing the software. I have found that it isn\u2019t always straightforward to come up with such issues (I tried thinking of some for a previous iteration of the activity, but always got stuck when I realized I didn\u2019t have a clue what to do and explaining the problem was hard enough), and in projects that are currently running it isn\u2019t always easy to leave these uncompleted when you have more time to work on a project. One idea was to have a HackMD document, similar to the one that suggests project meetings for new joiners to shadow, that lists all repos with such issues in one place, making it easier to find something fruitful to work on if these audits continue to be spontaneous. An alternative is to make these non-spontaneous and schedule them in advance. We\u2019ve kicked around the thought of scheduling lightning talks for all projects \u2014 perhaps we could better leverage people\u2019s brains if we gave a lightning talk intro on Tuesday and then guided people through installing and hacking on the software the next day? New joiners to projects inevitably spend a bunch of time doing exactly what we were trying to do in these sessions. Can we leverage the time investment of the new joiner by doubling up other team members to do this? Should new joiners to the team be encouraged to do this for a couple of weeks before being assigned to a project? I had a few new joiners on one of the audit sessions, and they said it was a good way to learn more about the team and our projects. ", "doc_id": "d1108f8f-6c82-40ca-bf1d-eba84831d72a", "embedding": null, "doc_hash": "e50e42f3558137686a1843ae30f3477d7961cd980d53eba113fd03fefd847535", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/how_we_work/code_audits/"}, "node_info": {"start": 0, "end": 3070, "_node_type": "1"}, "relationships": {"1": "5451e9d2-0048-4479-a6c9-7f48daf2a749"}}, "__type__": "1"}, "db89096e-87c7-4a7c-ac4d-67f67d6012a3": {"__data__": {"text": "Defining Project Doneness This working document was produced as part of Accelerating AI in the Arts and Humanities (AAAH). As a Research Software Engineer, we often join a collaborative project when a code base already exists. Defining the aims of a collaboration can be challenging, and when there is limited time it is important to apply attention in the right places. The following document is designed to help: Showcase what is important to us as RSEs Assessing the current status of an existing repo Define clear objectives Determine where to put effort Provide a common framework and language for collaborators Clearly define when the collaboration is done! The framework includes objectives that focus on improving the impact and legacy of the software, expanding communities of practice, and providing a confident codebase for future research. For each project discuss with collaborators which \u201cLevel\u201d in each of the following categories is a MINIMUM aim for defining done. Remember, time is a resource and so consider where efforts should be prioritised. Reach and Robustness Level 01. Internal users : Identify how users already use the tool and its current issues. Level 02. Best practices : Address minimum code best practices with view to robustness and accessibility Level 03. External users : Identify how similar external users could use the tool and current barriers Level 04. Generalise : Identify how the tool could be made general and current barriers to that Level 05. Proof of concept : Demonstrate its use outside its original intended purpose/audience. Functionality Level 01. Standard use : Install the tool, try out a standard use case, write a demo if one doesn\u2019t exist Level 02. Known Limitations : Understand and report. Level 03. Advanced use : Try out normal range but more advanced use cases, with edge cases. Level 04. New features : Identifying features for new functionality. Level 05. Enhancements : Fix functionality bugs and add features. Documentation Level 01. Follow docs : Follow the existing starting point of documentation (no help!) Level 02. What\u2019s missing : Identify what documentation is missing: e.g. installation, optional arguments, licensing, diagrams/screenshots, future work, etc. Level 03. Write docs : Write the documentation in markdown files Level 04. Published : Release the documentation on GitPages etc Level 05. Walkthrough : Write walkthroughs/demos (also gifs or videos) Testing Level 01. Continous Integration Start the automated infrastructure Level 02. Defining Positive/Negative Cases Conversation about what are the expected outcomes/how the tool works Level 03. Automated User Tests End-to-end tests that check expected outcomes from above, black box Level 04. Fine-grain Tests/Unit Tests Focus on critical/pain points, remove brittleness, modularise and reuseable components Level 05. Error Reporting Focus on negative cases, informative error communication for external collaboration Open Release Level 01. Find barriers : Identify minimum criteria that need to be solved before public release e.g. authorship, sensitivity, licensing, publicity, timings Level 02. Solve barriers : Address the minimum criteria and milestone efforts Level 03. Release : Make the repo public. Level 04. Distribute : Release the repo on PiPY or CRAN or equivalent, include DOI Level 05. Publisise : Publish the repo with JOSS, and/or publicly present the work. ", "doc_id": "db89096e-87c7-4a7c-ac4d-67f67d6012a3", "embedding": null, "doc_hash": "174d9ead8bc2b06133fe0b66c34278af157ae3211b93fb4ba893eea81f7a9fcc", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/how_we_work/defining-done/"}, "node_info": {"start": 0, "end": 3413, "_node_type": "1"}, "relationships": {"1": "f2a907fb-94af-401e-8dcd-33a3d4eec19e"}}, "__type__": "1"}, "718228f9-61dc-44bb-997e-c9d8c401bf75": {"__data__": {"text": "Knowledge Sharing This page lists the tools, resources and activities that REG has to promote general communication as well as sharing and gaining knowledge across the team (and the Turing as a whole), and some thoughts on what we could try to improve this. If you\u2019re looking to learn something new, get help with a problem, share something cool you\u2019ve been doing, or find out what others in the team are doing, the list below may give an idea of where to look/what to try. If you\u2019d like to organise something the best options are usually to post to Slack and the Hut23 mailing list (see below). Daily Github Hut23 Repo We use GitHub (and a couple of related tools) extensively for tracking projects and other tasks within the team. There is a separate page describing how we use them . Coffee Breaks We have coffee breaks on Gather twice daily. See the Regular Events page for details. If the times aren\u2019t convenient and you fancy a break then try posting on Slack and the chances are that there\u2019ll be others that fancy a break too. Slack Slack is our main tool for informal messaging and announcements (there\u2019s some info on how to join on here ). It\u2019s also a good place to ask for help. Channels of interest include: #hut23 : Our channel for the REG team (ask someone to add you if you don\u2019t have access) #tps : Channel for the tools, practices and systems (TPS) community at the Turing #askaway : Place to ask general questions ( e.g. that don\u2019t fit in a specific channel) #interesting-events , #general , #social , #random : General chat and announcement about what\u2019s happening around the Turing. We use Geekbot , which is a Slack App. We use this in two different ways (both are voluntary): A virtual daily standup: Geekbot asks what you\u2019re planning to do each day, what you\u2019ve achieved and if there is anything you are stuck on. This can be useful for your own planning, but also helps give an overview of the range of other work and skills within the team. Ice breaker-style questions a few times a week. To join in with the use of Geekbot, you will need to first add the Geekbot app to Slack and then join these slack channels #hut23standup and #hut23standup-tasks . Weekly Tech Talks See here . Reading Groups See here . Approximately Monthly REG Team Meetings Roughly once a month we have an all-REG meeting where we welcome new joiners and a few people briefly present news from around REG or the Turing. This is usually followed by a discussion on a topic of interest for the wider team. If you don\u2019t have the calendar invites (new joiners might not initially) ask someone else in the team to forward them to you. Turing Events The main focus of this page is what we do in REG, but there are many things happening around the Turing that REG members are also encouraged to attend, including: Turing Catchups & Town Halls \u2014 Regular meetings open to all staff with updates from around the institute. New starters may not have the calendar invites initially \u2014 ask others in the team to forward the details if you\u2019re unsure. Turing Events - often public talks & workshops. REG Newsletter We have a monthly REG newsletter which is usually sent around about a week before the monthly team meeting. It contains short project updates and other news/updates from around the team and the institute. It comes to the Hut23 mailing list (see above). If you have something you\u2019d like to be included contact the newsletter owner . Hut23 Mailing List You should automatically be added to the Hut23@turing.ac.uk mailing list when you join REG (if not ask your line manager about it). This is often the best way to contact everyone in the team when organising events, or for other news/communication that should be a bit more permanent/formal than Slack and doesn\u2019t fit in an issue in the Hut23 repo. Less frequently or ad-hoc Data study Groups Data study groups (DSGs) are week-long hack sessions on different projects that happen a few times a year. Participants are drawn from across the Turing and REG people have previously joined both as team members and project PIs. Turing courses and training There are professional and academic training opportunities around the Turing. There\u2019s some info on Mathison . Additionally, REG also runs a yearly research software engineering course . Remote Co-working Occasionally a few REG members get together on Zoom to Gather to do our work (not necessarily to work on the same thing, but to see each other and have a quick chat whilst we\u2019re working). During the COVID lockdowns, we organised these sessions regularly. Now they only occur on an ad-hoc basis. If you\u2019d like to work in this way, feel free to ask on Slack. Some project teams also choose to work in this way. Hack Sessions & REG Collaborative Time If there\u2019s something you\u2019d like to work on collaboratively feel free to organise a hack session (typically these would be longer but less frequent than a reading group, and perhaps more focused on implementing rather than reading). In the past several of us have got together to work on contributions for Hacktoberfest . Repo Audits A few of us plan to get together to help check whether documentation, usage and installation instructions are clear for a project (for example), or to review the code. See this page for more details. Project retrospective - Snark Hunts A \u201cSnark Hunt\u201d is one way in which we\u2019ve previously organised project retrospectives. We\u2019re open to trying other ideas too. Snark hunts are facilitated discussions after a project has finished where the project team, together with others from REG that didn\u2019t work on the project, answer/ask questions relating to the experience of working on it \u2014 what went well, what could have been better etc. . We don\u2019t do it for every project, but they can be a good opportunity to reflect and to get ideas for what we should do differently/do more of in the future. For more info, see here: Snark Hunt concept Snark Hunt template and example questions Previous Snark Hunts Working on Other Projects Working on different projects with different people causes a natural sharing of knowledge around the team. Beyond the usual project allocation process, there may be other ways to get involved with another project for a short time (some of these are speculative and not currently common practice in REG): Rotating between projects: Where a team member has been on a project for a long time, they may wish to change. Team members should ask to rotate to a different project in the next set of project allocations. Gaps between projects: Occasionally there are short gaps between one project finishing and another starting. These can be opportunities to get involved in something different for a while. One example could be to try a project with the ARC team (though I don\u2019t think anyone in REG has done this yet). Projects may also want to get into the habit of creating \u201cHacktoberfest\u201d style issues that newcomers could quickly get up to speed with and get involved if they have a gap in allocations. Shadowing projects: Especially for new starters, we\u2019ve started to encourage the idea of shadowing projects for a short while, to get an idea of how we work. ", "doc_id": "718228f9-61dc-44bb-997e-c9d8c401bf75", "embedding": null, "doc_hash": "79a4ba262d41746f6d604aeb06389780e75f99bb266ef41fc702586dd27b5163", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/how_we_work/knowledge_sharing/"}, "node_info": {"start": 0, "end": 7161, "_node_type": "1"}, "relationships": {"1": "0079133c-3da0-4592-8734-17c3465013d6"}}, "__type__": "1"}, "046ac25e-3976-4a1d-9914-e2b860979f2a": {"__data__": {"text": "Common Technical Practices This section contains a section of common technical practices within REG. For many of the topics in this section, there may be many possible ways to achieve the same goal \u2014 which are debated at length elsewhere. The intention of this section is not to claim that any of the methods here are the \u201cbest\u201d (however defined). Merely that they are good enough to allow the reader to focus on other, more interesting, unique or important, aspects of their project. ", "doc_id": "046ac25e-3976-4a1d-9914-e2b860979f2a", "embedding": null, "doc_hash": "f41b05448951acb22787ced25cc32c1388108bf230a40b6c296f79a6d8738799", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/technical_practices/"}, "node_info": {"start": 0, "end": 485, "_node_type": "1"}, "relationships": {"1": "7fb7e47d-1abf-4d13-9103-3ceafd2848bc"}}, "__type__": "1"}, "56594030-d0ed-4a20-9a89-3ffcf1198824": {"__data__": {"text": "Python Tools and Practices Python is a widely used language within REG. Below are some useful tools to help you configure your repo (with links to examples). Some of the topics discussed below are not exclusive to Python. As more content is added to the manual they should be separated out into their own pages. Autoformatters / Linters There are many widely used autoformatter and linters for Python, which can be used as standalone tools or call from .pre-commit , continuous integration tools etc. : isort Sorts your import statements correctly. The order isort \u201csorts\u201d imports in the following order: standard library imports ( e.g.  import os ). related third party imports ( e.g.  import pandas ). local application/library specific imports ( e.g.  from .my_python_file import MyClass ). You should put a blank line between each group of imports. the configuration of isort can be specified in a pyproject.toml file, where isort is using the [tool.isort] section. black A highly opinionated code formatter, which enforces control of minutiae details of your code. The configuration of black can be specified in a pyproject.toml file, where black is using the [tool.black] section. flake8 A wrapper for three other tools: PyFlakes \u2014 checks syntax, without checking the style. pycodestyle \u2014 checks compliance with PEP8 Ned Batchelder\u2019s McCabe script \u2014 checks the cyclomatic complexity of code. Configuration can be specified in a .flake8 configuration file in the root directory of your project. It is possible to use all of these in combination. The ARC group have a template repo with suggested configuration files which you can copy/adapt as required. Type Checkers Type checking is optional in Python but is generally recommended. There are several tools which can perform type checking: mypy \u2014 This type-checker has the distinction of including Guido van Rossum in its core development team. pyright \u2014 By Microsoft. pytype \u2014 By Google. Automation (Pre-Commit & Actions) A convenient way to incorporate many of these tools and checks into your workflow is to use pre-commit . Below is an example. You could adapt this to your needs, or create your own from the many hooks described in the documentation . # See https://pre-commit.com for more information\n# See https://pre-commit.com/hooks.html for more hooks\nrepos : - repo : https://github.com/pre-commit/pre-commit-hooks\n rev : v4.2.0\n hooks : - id : trailing-whitespace\n- id : end-of-file-fixer\n- id : check-yaml\n- id : check-xml\n- id : check-json\n- id : pretty-format-json\n args : [-- no - ensure-ascii, --no-sort-keys, --autofix]\n files : \".+json\"\n- id : check-added-large-files\n- repo : https://github.com/psf/black\n rev : 22.3.0\n hooks : - id : black\n#        files: \"*.py\"\n exclude : \".+/ignore_this_legacy_file.py\"\n- repo : https://github.com/pre-commit/mirrors-autopep8\n rev : 'v1.6.0'  # Use the sha / tag you want to point at\n hooks : - id : autopep8\n- repo : https://github.com/pre-commit/mirrors-mypy\n rev : 'v0.960'  # Use the sha / tag you want to point at\n hooks : - id : mypy\n exclude : \".+/ignore_this_legacy_file.py\"\n- repo : https://github.com/pre-commit/mirrors-isort\n rev : v5.10.1\n hooks : - id : isort\nThis example checks: There is no trailing whitespace at the end of each line. There is exactly one blank line at the end of the file. Any yaml files in the repo are formatted correctly. Any xml files in the repo are formatted correctly. Any json files in the repo are formatted correctly and \u201cpretty printed\u201d. If any large files have been added to the repo without using git-lfs Any python code (except the file named \u201cignore_this_legacy_file.py\u201d), is formatted according to \u201c black\u2019s \u201d uncompromising standard. Any python code (except the file named \u201cignore_this_legacy_file.py\u201d), is formatted according to \u201c pep8\u2019s \u201d standard. Any python code is tested for type safeness (That is declaring types is optional, but if types are declared they must be correct). The import statements in any python code are correctly ordered. If any of these tests fail or need to alter any files, the commit will fail. This prevents you from accidentally forgetting to check your changes before you commit them. If you wish to run the checks in pre-commit without attempting to commit your changes: pre-commit run --all-files There are many other possible checks available with pre-commit , which you can incorporate according to the needs of your project. The pre-commit documentation provides much more detail. Testing tools Some suggested testing tools: A test framework pytest or the less fashionable unittest . Coverage reporting pytest-cov (a plugin for pytest ) or coverage . A tool for testing code examples in your documentation doctest . This list of tools does not include a discussion on developing a testing strategy . Developing a testing strategy is a separate discussion, though it is not possible to make optimal use of these tools without a strategy. ", "doc_id": "56594030-d0ed-4a20-9a89-3ffcf1198824", "embedding": null, "doc_hash": "ca022c443def50fd9891dd9ac6bc57117cfbed7d6ffa6fb884034dcb56053e81", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/technical_practices/python/"}, "node_info": {"start": 0, "end": 4937, "_node_type": "1"}, "relationships": {"1": "2bbccd4a-6b45-45d0-a32c-c265b6acddcc"}}, "__type__": "1"}, "9a70dfba-f343-4b28-84c1-b30fe537b95d": {"__data__": {"text": "Configuring Your Editor Many people will configure their editor to suit their own needs and preferences. It is possible to do this so that there are common standards between collaborators in the code base, while allowing for differences in individuals\u2019 configurations. Some of the topics discussed below are heavily focused on python development. In due course we should generalise this to cover other languages. General Some common, particular formatting/encoding options can be configured for a range of editors/IDEs by using EditorConfig in your repo. A .editorconfig needs to be placed in the root of your repo. Many (but not all) editors support EditorConfig files allowing different members of your project team to use their own favourite editor while maintaining common coding standards. Some editors support EditorConfig natively; others require a plugin to support EditorConfig. There is some overlap in functionality between EditorConfig and Flake8. Crudely EditorConfig applies standards to files as they are created/edited, while Flake8 checks all files (irrespective of origin) on demand. Both tools need to be set up en ensure that they are mutually compatible. However, since the configurations for both Flake8 and EditorConfig rarely change after the initial setup of the project, this is not a problem in practice. VS code VS Code Python documentation ARC suggested config (Turing members only) Extensions Focusing on Python only, useful extensions include: Python \u2014 adds core functionality for linting/navigating/debugging/etc. Pylance Jupyter Python Indent \u2014 improves editor behaviour for matching indentation across lines autoDocstring \u2014 auto-generate template docstrings IntelliCode \u2014 improved auto-completion/code suggestions Black formatter (pre-release) \u2014 auto-format code with black isort (pre-release) \u2014 auto-format code with isort Python Type Hint \u2014 helps with completing type-hints The black and isort pre-release packages are currently designed to work with the Python pre-release version. This is likely to be consolidated/improved in the future. Settings VS Code Settings are stored in: Global user settings: ~/Library/Application Support/Code/User/settings.json . Project-specific (Workspace) settings: <PROJECT_ROOT>/.vscode/settings.json . You can also customise most settings via the Code -> Preferences -> Settings menu. For Python specifically note the Extensions -> Python section. Here\u2019s an example config for Python (reliant on installing the extensions above): {  \"python.linting.flake8Enabled\" : true ,  \"python.linting.mypyEnabled\" : true ,  \"python.testing.pytestEnabled\" : true ,  \"python.linting.pylintEnabled\" : true ,  \"autoDocstring.docstringFormat\" : \"numpy\" ,  \"[python]\" : {  \"editor.rulers\" : [ 88 ],  \"editor.defaultFormatter\" : \"ms-python.black-formatter\" ,  \"editor.formatOnSave\" : true ,  \"editor.codeActionsOnSave\" : {  \"source.organizeImports\" : true\n}, }, } It does the following: Activates linting with flake8 , mypy and pylint . We suggest using flake8 at a minimum, but you may not want all three activated as it gets quite noisy and there\u2019s some overlap between them. Sets pytest to be the default test framework. Sets numpy to be the default format for docstrings generated with autoDocstring. Sets black to be the default formatter, and automatically runs black and isort when saving a file. Adds a vertical ruler to the editor at 88 characters (default line length used by black ) for Python files only. Note that: It\u2019s possible to specify arguments for the linters/formatters in settings.json . We suggest using each tool\u2019s own configuration files ( e.g.  .flake8 ), as these will then be picked up by other tools and editors as well. ( e.g. Should configure flake8 and isort to be compatible with black ). If enabled tools are not installed in your Python environment VS Code should prompt you to install them. You are likely to have some non-Python specific configuration as well, so add the settings above to your settings.json file rather than overwriting it completely. Selecting the Python Interpreter VSC Python Interpreters Docs For each workspace VS Code can be configured to use a specific Python interpreter (environment). That interpreter is then used by default when doing anything with your code (running scripts, linting, tests etc. ) To set the interpreter: Open a .py file. Click on the details of the currently used environment on the status bar (bottom right). Select an interpreter from the list that appears (or write the path to it). Or from the command palette: Cmd-Shift-P -> Search for \u201cPython: Select Interpreter\u201d. Linting VSC Linting Docs If linters are activated (see Settings) they will be run after saving a file, and any problems the enabled tools encounter will be highlighted in-line in the editor, and be listed in the \u201cProblems\u201d tab of the panel at the bottom (or select View -> Problems from the menu). Hovering over a piece of code with a problem in the editor will give you more details about it, and may have a \u201cQuick Fix\u201d available, which will edit the code for you to fix it. Formatting VSC Formatting Docs VS code can use a few different file formatters (e.g. black , yapf , autopep8 \u2014 we recommend black ), which can be configured to run automatically when saving files (see settings above) or with the following shortcuts: Option-Shift-F. Save the file (if auto-formatting is configured). Right-click in a Python editor -> Format Document. Cmd-Shift-P (Opens Command Palette) -> Search for \u201cformat\u201d -> \u201cFormat Document\u201d. Running Tests VSC Testing Docs If you have enabled pytest (or another framework, e.g. unittest ), tests will be discovered and appear in the Testing menu (lab bottle on the left side menu). From here you can run all/individual tests. You can also search for \u201cTests\u201d in the command palette (Cmd-Shift-P) to find options for running tests. If a test fails, clicking on it will show the error message. You can also debug tests (see debugging). If you have a tests file open in an editor the latest pass/fail status of a test will be shown (or a play button if the test hasn\u2019t run before) \u2014 running tests can be triggered from there too. Debugging VSC Debugging Docs Other Tips and Tricks Auto-import: If you start typing a function name that hasn\u2019t been imported, VS Code will pop up a list of suggested functions and \u201cAuto-Import\u201d them (add an import statement to the top of the script) if you select one of them. Rename symbol: Right-clicking on a (variable/function/class/etc.) name in a file, selecting \u201cRename symbol\u201d, and entering a new name will change the name of all instances of that across your codebase (but respecting the scope of the renamed entity). Jupyter: The Python extension comes with a Jupyter extension so you can run and edit notebooks in VS Code. Personally, I prefer the native Jupyter notebook/lab interface to the VS Code one. AutoDocstring: If the AutoDocstring extension is installed, typing \"\"\" then hitting enter will generate a template docstring for you containing placeholders for all arguments (including their types if type hints are used in the function definition). Go to definition: Right-clicking on a (variable/function/class/etc.) name and selecting \u201cGo to definition\u201d will take you to where that thing is defined in the codebase. Git: The editor highlights modifications to the code (by the line numbers), and you can also commit etc. from VS Code using the Source Control menu on the left. Docker: There are extensions ( 1 , 2 ) for running code in containers/attaching a VS code window to a container. ", "doc_id": "9a70dfba-f343-4b28-84c1-b30fe537b95d", "embedding": null, "doc_hash": "23a784c90a2d8da2bc758468979bd2729b75ae50e5842e9a1b84cbb341edcaa8", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/technical_practices/configuring_editors/"}, "node_info": {"start": 0, "end": 7578, "_node_type": "1"}, "relationships": {"1": "e9557c23-59b2-468d-b8ae-b75b5199b39a"}}, "__type__": "1"}, "caaf5123-07a6-4538-9dc0-fa7c6e01133a": {"__data__": {"text": "Changelogs For any non-trivial code, it is worth maintaining a Changelog. This is valuable as it is a record of changes from the perspective of users (rather than git commit messages which are often only meaningful to the developers involved). A recommended format for Changelog files is given at Keep a changelog . All notable changes to a project should be documented in this file. Semantic Versioning A related concept is semantic versioning . In most cases, semantic versioning is recommended. You should state explicitly whether or not you are using semantic versioning in both your project\u2019s README and CHANGELOG files. ", "doc_id": "caaf5123-07a6-4538-9dc0-fa7c6e01133a", "embedding": null, "doc_hash": "edfe439e98900c023be3ac00c114ffec9bbbd2b4263e480745efa728e5af410e", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/technical_practices/change_logs/"}, "node_info": {"start": 0, "end": 626, "_node_type": "1"}, "relationships": {"1": "99e45a31-eb27-4ffe-98b0-60fd18b1a669"}}, "__type__": "1"}, "5adbe930-f5b1-45f1-9d06-982bd4fcc751": {"__data__": {"text": "Software Development Best Practices Status: this document is a draft proposal. RSE projects There should be a Github issue for each thing we do. It\u2019s ok for issues to evolve and my preference would be that the top description box always has the most up to date, clearest description of the issue and its definition of done. Feel free to make liberal use of the comments for communication and tracking investigative / exploratory work. The main description box should contain a clear definition of done / acceptance criteria sufficient for the following to be done in principle by different people from the team who are involved in the project. Implement the functionality Write tests to validate the implementation meets the definition of done Review the implementation for correctness All code should have automated tests, with all tests runnable by a single command by someone who has checked out a fresh copy of the repo. All dependencies, installation, compilation etc should be scripted such that these can be triggered by a single command by someone who has checked out a fresh copy of the repo. Dependencies required only for testing can require a second command or few. Each new issue should be developed on a short-lived feature branch split from \u201cmain\u201d. These branches ideally only live around 0.5-2 days before being merged back into main. Merge process should be: merge main into feature branch and resolve any conflicts. Push latest feature branch to Github. Open PR to merge into main after review + continuous integration (CI) tests. Code should be committed regularly in small chunks. You should generally be committing multiple times per day. Commits on feature branches can have broken functionality or tests, but all merges into main should only occur at commits where the code is correct and has passing tests. All code commits should be accompanied by tests verifying it behaves as expected. At a minimum code submitted as a PR should have tests that show it meets the definition of done defined in the related issue(s). However, code should have additional unit / regression / integration tests where appropriate (it\u2019s almost always appropriate). Writing code then tests is a significantly lower quality assurance measure than writing tests first / in iteration with the code, so you should do the latter. All code should have had two eyeballs on it before it is merged into main, either through pair programming or code review within the PR. Please note on the PR who two eyes were. The second pair of eyes can be anyone on the project (not just a coder), anyone in our team or someone from a related team (e.g. the UCL research programming). Whether as pair programming or a code review, the final PR code should be validated against the definition of done detailed in the related issue(s) by the two pairs of eyes. The Github repository should be set up to run all tests on all pushes to all branches and on all PRs. Currently this can be done for public repos via GitHub Actions. If CI is set up this way, commits to main should be blocked unless all tests pass. Data Science projects TBD ", "doc_id": "5adbe930-f5b1-45f1-9d06-982bd4fcc751", "embedding": null, "doc_hash": "7815b228021dfe6287b08371c4e5e39c43b642fc4c4a0c8aecdb5557f875feae", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/technical_practices/software_dev_best_practice/"}, "node_info": {"start": 0, "end": 3115, "_node_type": "1"}, "relationships": {"1": "0db2f055-dd75-4980-92b5-9ee77654d811"}}, "__type__": "1"}, "d22f3c5b-84ac-45ca-901e-1d2530796bb6": {"__data__": {"text": "Onboarding ", "doc_id": "d22f3c5b-84ac-45ca-901e-1d2530796bb6", "embedding": null, "doc_hash": "8de19a3a3de64a5ec335f8cf9751ea13ec2280a39b167593dc39ff342a3eb837", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/"}, "node_info": {"start": 0, "end": 11, "_node_type": "1"}, "relationships": {"1": "dd11122d-735a-4af6-bd95-9e13b16acda1"}}, "__type__": "1"}, "30be9a9c-d226-4133-8642-6ca620cc0d21": {"__data__": {"text": "New Joiners This section helps new members of REG get up to speed. Please go through these page as one of the very first things when you join. First Few Days Systems Set Up ", "doc_id": "30be9a9c-d226-4133-8642-6ca620cc0d21", "embedding": null, "doc_hash": "bc50b80655afa9fcd85c6076af522c9a6a726fbddf95ba4ddf55f62a3ac69952", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/"}, "node_info": {"start": 0, "end": 173, "_node_type": "1"}, "relationships": {"1": "335ba292-7f04-43bf-a9fd-519f00b4ece3"}}, "__type__": "1"}, "ff8d6def-994a-4295-8f10-af0a6592d856": {"__data__": {"text": "First Few Days Welcome! We are super pleased that you\u2019re joining our group. This page gives you an overview of what to expect from your first few days. They are maintained by the group, we hope that new-starters feel empowered to edit them if there are useful changes to be made. Introductions Each new starter gets assigned two buddies to be friendly points of contact. The person in charge of onboarding\nshould let you know who these are a bit before you start; If that doesn\u2019t happen, please email them about it. Your buddies will welcome you on the first day. In the first few days there will be a welcome coffee to give you a chance to introduce yourself to the whole REG team, and within the first few weeks you will also have a 1-on-1 meeting with REG\u2019s Director. You will have a week or so before you will be assigned to a project. Please use this time to do admin stuff, set up your laptop and tools, get to know people, read this handbook and our internal wiki, and shadow meetings (see below). Here we go over the main points you should remember to do, but for an attempt at listing all the small tasks that occupy a new starter, please see (and improve!) this checklist . We also have a page of tables available with information about links and resources related to REG and the Alan Turing Institute. Administrivia HR will set up a meeting with you to discuss general information e.g. pay, health, leaves, benefits. IT will also meet you to discuss accounts and Turing wide systems. Both of these will typically take place in the first day or two. We have a dedicated page\nfor various systems REG uses. Have a look at the high priority section on your first day . We also have a Remote Working\npage for Zoom, Teams, and Gather. REG offers new starters the opportunity to shadow meetings across the group to meet people and get a feel for how we work. The shadowing document\nshould be updated with meetings you are free to come and listen in on, if it hasn\u2019t been please bug the person in charge of onboarding . To access the office you will need a British Library pass. To get a British Library pass you will need to go through the background screening check. Hopefully that process will have been completed by the time you start, but if it hasn\u2019t even been started yet (this happens), please talk to HR\nto make sure the process gets going. Once the screening is done, HR should let you know within a few days that your pass is ready. In the meanwhile, you can either enter the British Library through the general public entrance and come talk to the Turing\u2019s reception, or, preferably, arrange for a visitor pass for yourself for the days when you\u2019re coming in. For the first day or two, ask your line manager to take care of this, once you\u2019ve got your Turing account going you can directly ask reception . The password for the \u201cATI Guest\u201d Wi-Fi network can be seen hanging around the office. IT\ncan arrange access to the Eduroam network. Eduroam credentials can be used to access Wi-Fi at many academic institutions. Please also write a short, informal paragraph about your background for to be put into the next REG newsletter, and send it to the newsletter owner . Overview of How REG Operates Work at REG revolves around projects and service areas. You will typically be assigned to two different projects at any time, with at least one other REG person and usually some external collaborators. You may want to browse REG\u2019s portfolio of projects in the project tracker\nto get an idea of what we\u2019re up to. The project tracker is also used for expressing preferences on which upcoming projects you would like to work on, but you don\u2019t have to worry about that for first few weeks. We very much encourage REG members to learn new things, so feel free to choose projects that sound interesting to you even if they are unfamiliar. Check this section\nof the handbook for more details. Service areas are REG-internal work, such as looking after this handbook, organising recruitment, or managing our computational resources. Usually people contribute to one service area, which should take approximately half a day a week. This, too, is something you don\u2019t have to think about for now, but come back to it once you\u2019ve settled into your workflow with a project. REG also gives its members some time, known as 22 days time, to work on side projects they find interesting. Your line manager can give you the details on this. If you like, you can see our team reporting structure . Note, however, that we do not have a hierarchical culture, and line managing relations are not about assigning work to people. Your work tasks will be coordinated with the people in your projects. You should talk to your line manager about things such as How project allocation works, and what kinds of projects you would like to work on Managing your time and growth How we work (practices, tools, languages, etc. ) How you would like to grow and develop in your role and what opportunities exist to support that. Please also take a some time to browse this handbook, it\u2019s got plenty of useful information about what we do and how we do it. Set up Your Machine and Coding Environment See systems setup . Regular Events Check out our regular events and coffee breaks . Please consider signing up for a tech talk\nsometime in your first six weeks. If you don\u2019t see a calendar invite for the weekly tech talks, ask the person in charge of onboarding\nto add you to the relevant mailing list. What to do next If you\u2019ve done all the above, read through the handbook and the repo and are still waiting for your first project to start, go and talk to the person in charge of onboarding. They will find something meaningful for you to do which might be something like: Browse the Project tracker and emoji any projects in the Finding people and Awaiting go/no-go columns. Note: you will be notified to do this before being assigned to a project. Browse the Service areas list , discuss the different areas with people involved and emoji those that are looking for people. Work on a Turing Data Story. There\u2019s always a story to join or a new one to start. You can then keep working on it on your 22 days time once allocated to a project. This option has been very popular. Tackle a GitHub issue marked with the good first issue tag in a repo that REG works on. Whenever a new starter is looking for things to do, the person in charge of onboarding will send a message on Slack asking everyone to tag suitable issues. Work on a REG-internal project that is easy to rotate in/out of. Any ad hoc ideas that come up. Maybe you found a project that really interests you and want to help, or maybe there\u2019s some team organisational thing that comes up that you volunteer for. Points of Contact You can find useful points of contact here . This page\nalso holds other useful information such as remote meeting details and team responsibilities. ", "doc_id": "ff8d6def-994a-4295-8f10-af0a6592d856", "embedding": null, "doc_hash": "90cedaff2defa45c628c1ee8afbb43509ef1d089c96de5f48c73893af74a79de", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/first_few_days/"}, "node_info": {"start": 0, "end": 6914, "_node_type": "1"}, "relationships": {"1": "23cd1224-aad4-4c6e-b162-9a9141da109a"}}, "__type__": "1"}, "a84215e5-c9cc-43b8-a1c2-617a59aa35ac": {"__data__": {"text": "Systems Set Up These are not hand-holding instructions. Rather they are a reference to help with your setup. If they are wrong or incomplete, please edit them. The page has been roughly organised into high priority things (do in your first couple of days), low priority (sometimes in the first couple of weeks), and reference. If any of the material is unfamiliar ( e.g. you haven\u2019t seen a GitHub project board before), then please ask your buddies (or anyone else you meet in the group) to have an informal chat about it. High priority Getting a computer Receive (or be assigned) new computer from IT. It will have an admin account for IT, and probably an account for you with a default password. Log in with the password provided by IT. Change your password. Your new password should be strong (12 random characters), and ideally randomly generated by a password manager. Feel free to use a diceware-style passphrase. Your password should be: unique to the machine/system randomly generated (use a password generator) have an entropy of at least 64 bits, see wikipedia At least 11 characters if alphanumeric (use 12, though) At least 10 characters if alphanumeric plus symbols At least 5 words if using dice ware At this point you can also configure fingerprint authentication. Make sure you have FileVault on. You should be setup with local admin privileges. Contact IT if this isn\u2019t the case. Install Homebrew We recommend using Homebrew as your Mac package manager. Currently IT installs Homebrew by default on our laptops under /usr/local/bin . However, in the long run this might create problems with the new M1 Macs we use. To see if this problem applies to you, run which brew in the terminal and check if that\u2019s the location. Until this is fixed by IT, what we recommend is the following: Uninstall the current version of Homebrew by running the following command in the terminal: sudo /bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/uninstall.sh)\" Install Homebrew again following the instructions on the website . This will install Homebrew under /opt/homebrew/bin/brew (you can check by entering which brew in the terminal to see the new location). Communication Channels Most communication happens through Slack. More structured communication happens through GitHub. These two systems are the first things you should setup (once you\u2019re on Slack it\u2019s a lot easier to ask for help). Slack Install Slack: brew install --cask slack Once installed join the Turing Slack . You can self-subscribe with your @turing.ac.uk email address. Important channels to join: You should be added to #general and #random by default. Ask anyone in the team to add you to the private channel #hut23 . If you are a senior, please ask another senior to invite you to the REG senior\u2019s Slack channel ( #line-managers ). GitHub We use GitHub for most coordination. To get access to the Turing GitHub , create a GitHub account (or use an existing one), then fill this form on Turing Complete with your GitHub handle (if you have access to Turing Complete). Then let the GitHub org controller know that you have done this and they can add you straight away. Also check the Project tracker to get a feeling on what goes on. We use it to track official projects we are tasked with. This is where we express preferences for projects, with emojis. Project leads should be assigned to the relevant issue and are responsible for keeping the issue up to date. Check this section of the handbook for more details. Office 365 account Your Turing main account rests here . You get access to mail (Exchange), calendar, a shared drive, Office apps. Check that you can login, and change your password and configure MFA. Ask IT if in need for help. Lower Priority Here we have more information about how we use the high priority systems, and also other systems to explore. Slack Channels Turing is a large organisation and there are many other channels you may want to join. Some are purely social, some are related to programmes or projects. For example, #tps (for the closely aligned Tools, Practices and Systems programme), #kitchendropin , #social , #pubclub , #askaway , #research-computing , #interesting-events , #football . If you get interested in something just ask if there is a channel about it. All of the REG-specific channels are prefixed with hut23 , so that\u2019s a good thing to search for in the channel list. You may also want to add yourself to #hut23standup , which some of us use for automated standups of \u201cAbout Us\u201d questions. The purpose is to get to know other team members by answering a question three times a week. Geekbot should send you the next question at 10:30am on Mondays, Wednesdays and Fridays. Tips for using Geekbot: You can report before/after Geekbot asks you by sending it a message from Slack, e.g. report About Us . The Slack channels containing the responses can generate a lot of notifications. You might want to turn off notifications, or alternatively mute the channel and check in on it manually instead. (Right-click on the channel name to do this.) You can customise the time of Geekbot\u2019s questions. Send dashboard as a message to Geekbot and click the link it returns. From there, you can select a standup and the \u201cMy preferences\u201d button will take you to the customisation page. (This setting may not be enabled on all standups.) You can export a list of your previous responses to a standup from the dashboard. Click on the \u201ctimeline\u201d tab of the relevant standup, and from there you can select the participants and questions to export to a csv file. This is particularly useful when you have forgotten to fill in Harvest for a while and need the information from the daily standup! Other workspaces you could join: The Society of Research Software Engineering has a Slack worspace . For information about joining the Society see the SocRSE page . Mailing Lists The Newsletters & Mailings page on Turing Complete has a \u201cUpdate Mailing Preferences\u201d link to sign up to receive some internal newsletters/communication. Harvest and Forecast We use Harvest to (loosely) track our time, and Forecast to manage allocations to projects. Get in touch with the person in charge of researcher tool management to setup a meeting sometime in your first week. Harvest is a time tracking app used to monitor the amount of time being dedicated to each project. The easiest way to get set up is to ask your Line Manager or another member of the team to invite you and make you admin. You can then add yourself as a team member to whichever projects you\u2019re working on and start tracking your time. There is a page here which goes into more detail about where you should log different activities. Cezanne HR Our HR system is Cezanne HR . You can log onto this with your Turing credentials using the \u201cEnterprise login\u201d option. If \u201cEnterprise login\u201d does not work, email HR to fix it. This system is used for: Requesting annual leave. Recording days off due to illness etc. . Managing the objective setting and performance review process (after your probation period). Create a Public Profile on the Turing Website There are several ways to do this: You can fill in this form on Turing Complete to request a public profile. Alternatively, contact the people in charge of REG communications , providing the following info: Your title and full name A short personal bio A brief description of your Turing-related research (optional) A brief description of your achievements and award (optional) Photo (optional) Webpage You can also edit the website yourself directly. To do so, you must complete a round of online training , after which you will be granted edit permissions. Office 365 Groups Make sure you are in the following Office365 / email groups (check with IT ): Research engineering Business team (for non-Research Associate staff) Research staff All Staff If you are a senior member of staff, ask a principal to get you added to the REG-senior-staff email list. End-of-month all business team meetings (Turing Catch-ups). These are sent out as recurring calendar events. You can either email governance to forward you the invite or find a member of the REG team that is", "doc_id": "a84215e5-c9cc-43b8-a1c2-617a59aa35ac", "embedding": null, "doc_hash": "6b716d227290bbdfb6281f5c33934d169b4ec346c7abfbddd909addc40eecb91", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up/"}, "node_info": {"start": 0, "end": 8214, "_node_type": "1"}, "relationships": {"1": "7564a474-3a18-40d0-aaf6-812b2e7f18c2", "3": "69222f1d-a8bf-45eb-bd90-448ede12b0db"}}, "__type__": "1"}, "69222f1d-a8bf-45eb-bd90-448ede12b0db": {"__data__": {"text": "either email governance to forward you the invite or find a member of the REG team that is invited and ask them to forward the invite. Turing Bulletin: This internal newsletter is sent out every Thursday evening. If you don\u2019t receive it, you can sign up manually via Turing Complete . GitHub Pro and Professional IDEs Turing employees can apply for free GitHub Pro accounts, and use these to get free educational licences for professional versions of many JetBrains products ( e.g. , PyCharm, IntelliJ, \u2026) Go to education.github.com and apply for the GitHub Teacher Toolbox GitHub will ask you to verify your affiliation with an academic institution. The Turing is not on their list, but you can still use it. You will need proof of employment - a screenshot of Cezanne or the top of your Turing contract (including start date) have worked for people in the past. Once GitHub have approved your Pro account, go to the JetBrains website and create an account there (sign in via GitHub) You can now apply for a free educational licence (\u201cApply for a free student or teacher license\u201d) for professional versions of various IDEs. There is no obligation to use these IDEs, this is just an FYI in case you are interested. OneDrive Sync the Research Engineering documents: From SharePoint , under \u201cResearch Engineering\u201d private group, go to \u201cDocuments\u201d, then \u201cSync\u201d. Please avoid locally editing files that are common and might be edited concurrently. In that case, opt to use the online version through Office 365. HackMD We sometimes use HackMD for shared documents. If you don\u2019t have an account, signup for a free account here . We do not have a paid account for HackMD. Skype for business We don\u2019t use skype that often, but this can be installed through the Self Service application. You will need an application-specific password. If you want a phone number ask IT . Mathison Mathison is the Institute\u2019s intranet. You should have access to this on your first day. A few things still need to be done on the old intranet, Turing Complete . Gray Dawes Gray Dawes is the Turing\u2019s system for booking travel and accommodation. Instructions on how to get set up can be found on Mathison . When making bookings on Gray Dawes, you will have to additionally submit an Excel approval form (available at the right-hand sidebar of the same Mathison page). This form should be filled in and sent to the Principal you have a direct link to in the line management chain for approval. Certify Other work-related expenses can be reimbursed via Certify: see the Mathison page for more information on how to get set up. Briefly, you will have to fill in a Travel and Expenses Policy Compliance Form and email it to Finance. On this form, the \u2018approver\u2019 should be the REG Director. The REG Wiki contains more information on the reimbursement process, including information about the specific department and project codes you should be using. When you get access to Certify, it\u2019s a good idea to check that you\u2019ve been set up with the correct codes. If you applied for a visa to work at the Turing, you are also allowed to claim up to \u00a34000 back. More details on this can be found in Mathison as well as the REG Wiki . Purchasing home office equipment There is a budget to purchase peripherals (monitor, mouse, keyboard, etc. ) as well as other equipment such as an office chair and desk. For peripherals, fill in this form on Turing Complete . For an office chair and desk, you can purchase these yourself, and claim up to \u00a3200 back (in total) via Certify. Reference Take a look at the following sections: Regular Events Common Technical Practices ", "doc_id": "69222f1d-a8bf-45eb-bd90-448ede12b0db", "embedding": null, "doc_hash": "0b5842f6997fd0bb6afa13196f05f386df21d41c947f4b549ebc509c25177f85", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up/"}, "node_info": {"start": 8124, "end": 11747, "_node_type": "1"}, "relationships": {"1": "7564a474-3a18-40d0-aaf6-812b2e7f18c2", "2": "a84215e5-c9cc-43b8-a1c2-617a59aa35ac"}}, "__type__": "1"}, "dc58742e-6237-4a06-ab34-f8694a76f6d0": {"__data__": {"text": "Checklist This page attempts to list all the things that a new starter should remember to do. It makes no guarantees of completeness, and attempts to list both compulsory things and optional ones. It has been written by new starters as they go. If you\u2019ve recently started in REG and find yourself doing anything related to getting started, that others might want to do as well but that is not on this list, please add it. If anything is unclear, please figure it out by e.g. asking your buddies, and then clarify it here. Meetings Attend buddy meeting Attend HR induction Attend IT induction Meet with line manager Shadow meetings: https://hackmd.io/nnsWP4i_SiiwlT4meOeq6g (if you don\u2019t have access, ask someone in charge of onboarding ) Join in for welcome coffee(s) HR tasks Complete Agenda screening (hopefully this happened already before you started, but if you haven\u2019t received an email about this, ask HR) Do a bazillion things on Cezanne : Enter specific personal details, most important are: DOB Home address Emergency contact details Bank details NI Health form Additional characteristics form Complete and reupload in Cezanne documents area: \u201cBL partners - Health, Safety and Security\u201d form HMRC new starters form \u201cRight to work\u201d document Signed and dated scanned contract (can just upload the version signed with DocuSign before joining) British Library pass: \u201cBL partners - Health, Safety and Security\u201d form: complete and reupload in Cezanne documents area Upload photo to Documents section on Cezanne (where other starter forms are provided) HR require Agenda Screening report (no action if already completed Agenda Screening before starting) Temporary pass, line manager emails reception to request Send P45 from previous job to HR contact directly by email Read about health and dental insurance, decide whether to sign up Check the Turing Benefits site, to see if there are useful discounts there Send to HR for the next Turing Town Hall meeting: Casual photo Fun fact(s) Provide a description for the ATI webpage ( https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up#create-a-public-profile-on-the-turing-website ) Verify your MoorePay account (for payslips). Verification email will be sent to you in your first month of employment. See Payroll and Payslip enquiries on Mathison for more information. Verify your Certify account (for expenses). The Certify Mathison page contains a Travel & Expenses Policy Compliance Form which has to be filled in and emailed to Finance to activate your account. GitHub and communications Get access to private GitHub repos (fill in this form and send your GitHub username to the person responsible for GitHub (see The REGistry ) to join the Alan Turing Organisation GitHub Join Turing Slack Go through public Slack channels, decide which ones to join Request account on Harvest and Forecast by asking the the person in charge of researcher tool management, or your buddies to get you set up (to clarify, it is one account but allows you sign in to both) https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up#harvest-and-forecast Read through the REG handbook: https://alan-turing-institute.github.io/REG-handbook Read through the new starter page of the REG handbook: https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/first_few_days/ Have a look at REG projects on the Project tracker in the Hut23 GitHub Repo. Read through the Project Tracking page to learn how we set our preferences for projects by using emojis, and start emoji-ing for the projects in the \u201cFinding people\u201d and \u201cAwaiting go/no-go\u201d columns. Have a look at the Service areas . There is no pressure to join one right away so you can take some time talking to people and finding an area that interests you. Add [[Shared REG Calendar]] to Outlook Familiarise yourself with how to Book Rooms Message IT ( ITServices@turing.ac.uk ) to upgrade Zoom account from Basic to Pro Send a short informal bio to the REG newsletter owner . New computer set-up  https://github.com/alan-turing-institute/research-engineering-group/wiki/Moving-to-a-new-computer Ongoing activity sign-up \u201cTech Talks\u201d (Tuesdays 12:30pm), link on: https://github.com/alan-turing-institute/DataScienceSkills/wiki/Lunchtime-Tech-Talks Read up about and you are recommended to join the Society of Research Software Engineering , including the Slack workspace ", "doc_id": "dc58742e-6237-4a06-ab34-f8694a76f6d0", "embedding": null, "doc_hash": "369c7e82462e5cbc394b7359e613ff2b5020258e517c7e154e3e8591b6a66e1d", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/checklist/"}, "node_info": {"start": 0, "end": 4459, "_node_type": "1"}, "relationships": {"1": "450392e8-3be5-46b5-878b-acaae44a33cb"}}, "__type__": "1"}, "b4e15cd8-6566-4705-b0b2-cc656777a66c": {"__data__": {"text": "Buddy System It can be daunting starting a new job, especially remotely where there is sometimes less opportunity for casual questions. To help with this every new employee should be assigned buddies. The buddies are responsible for providing informal friendly faces to offer advice, guidance, and encouragement on any aspect of working within REG and ARC. We also emphasise that the buddies should be a safe space where new-starters can get help with any technical questions that they feel is silly or trivially easy and may not feel comfortable admitting to the wider group. The Process Before the new starter starts at the Turing they should be assigned two buddies from our buddy pool . If possible, the buddies should not be assigned to the projects on which the new starter is likely to work, and ideally at a similarly seniority to the new starter. We would also like for each new starter to have a third buddy from some other team. We think that in a remote & hybrid setting it will help new-starters meet people and get a feel for the wider Turing ecosystem. This has been trialed in a few instances, to positive feedback, but to make this be standard practice we would need it to be more bidirectional, with REGers volunteering to buddy with people outside of REG. The new-starter should be made aware of the buddies\u2019 role before they start, and meeting their buddies should typically be the first appointment on their first day. The buddy system in managed by the person in charge of onboarding . Buddy Guidelines Have a coffee with the new employee on their first day. If needed, introduce them to the team at welcome coffee. Be as informal as possible, make the new starter feel welcome and comfortable. Make sure the new starter is familiar with the New Starter page . Check for sticking points. Meet the new starter where they are at. Make it clear that the new employee can get in touch any time for questions and concerns, regardless of triviality. Emphasise that you\u2019re also there for technical pointers. This is to encourage the new-starter to be comfortable asking for help when they are stuck and hopefully lessening imposter syndrome anxiety. Have a check-in chat with the new starter at least once in the first couple of weeks, and again a few weeks after. During these check-ins, uncover pain points and concerns. Help socialise the new employee to the group\u2019s culture and processes. Be open, honest, and respect confidentiality. If needed, help make introductions. Tips for Buddies Don\u2019t worry about being perceived as the expert. Focus your attention on the new employee. Be patient: It takes time to develop a relationship. Don\u2019t try to cover everything right away. Be positive: New employees will grow into their roles if given proper reinforcement. Don\u2019t try to force a relationship. New employees will vary in how much they turn to a buddy. Try to identify the new employee\u2019s personality and communication style and adapt accordingly. Don\u2019t be judgemental. Simply offer feedback. Maintain a good attitude and a teaching spirit. How to be a Buddy Being a buddy can be a rewarding process. You will help avoid new starters feeling at sea and reaffirm their decision to join the group. To sign up please add your name to our buddy pool . ", "doc_id": "b4e15cd8-6566-4705-b0b2-cc656777a66c", "embedding": null, "doc_hash": "be7ba423a6147bbb3c4018f2cf6cee65f2f19c349452d95c035a89df19750d46", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/buddy_system/"}, "node_info": {"start": 0, "end": 3265, "_node_type": "1"}, "relationships": {"1": "55900417-1001-4f91-a88d-a3b59c7beb56"}}, "__type__": "1"}, "8d1f0547-7a86-4547-80d0-4749c7cd3e2d": {"__data__": {"text": "Society of Research Software Engineering The Society of Research Software Engineering (SocRSE) is a charitable incorporated organisation with the mission to establish a research environment that recognises the vital role of software in research. REG supports and encourages its members to join the SocRSE, so it\u2019s worth reviewing the society\u2019s website and deciding whether to join during your onboarding period. Benefits of SocRSE There are many benefits to joining the SocRSE. By joining the Society you will: Contribute to the Society\u2019s work supporting the research software engineering community Gain access to the Society\u2019s Events & Initiatives fund Get discounted access to the Society\u2019s annual conference Be eligible to vote and stand for election at the Society\u2019s annual AGM Have your say in one of the Society\u2019s special interest, regional or working groups Be able to apply for the Society\u2019s mentorship scheme Gain access to discounts from the Society\u2019s corporate sponsors Communication Channels The Society has a website, Slack and email list. Their Slack workspace contains plenty of details about relevant events. Claiming Back Subscriptions SocRSE membership is considered a professional subscription, which you\u2019re entitled to claim back as a REG member. You should claim it back quickly after payment. Use Certify to claim it back and don\u2019t select a project when making your claim. ", "doc_id": "8d1f0547-7a86-4547-80d0-4749c7cd3e2d", "embedding": null, "doc_hash": "7339996eb8f50c59da4fb3c2cf3d710f2895eb461baca0415c0f7a9050141638", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/society_of_research_software_engineering/"}, "node_info": {"start": 0, "end": 1395, "_node_type": "1"}, "relationships": {"1": "3bdd822d-8d17-490b-8945-8cb3fc25e078"}}, "__type__": "1"}, "eb1611e0-048f-4d40-8759-18ea8bb446ee": {"__data__": {"text": "Communications ", "doc_id": "eb1611e0-048f-4d40-8759-18ea8bb446ee", "embedding": null, "doc_hash": "56b3d6e5dd831a7026c59a5191ca025f714396b3ac32cf099428abdc3e1d28b8", "extra_info": {"filename": "https://alan-turing-institute.github.io/REG-handbook/docs/communications/"}, "node_info": {"start": 0, "end": 15, "_node_type": "1"}, "relationships": {"1": "15973cde-a6c5-42d5-b091-b5fef2ab54be"}}, "__type__": "1"}, "98b1e8d2-1971-4141-be92-0609195c2280": {"__data__": {"text": "Twitter The group has a Twitter account @turinghut23 . The purpose of the account is to have an informal public-facing channel where we can communicate about projects we are working on and about life as a research engineer at the Turing. Access If you want to tweet under the group account, ask the account controller for access. This also gives you access to Medium What to Post Projects we work on (if public) Blog posts Talks that we organise Conferences and workshops Any public outputs \u2014 papers, published code etc. Please refer to the Turing\u2019s social media policy when making posts. 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{"file_path": "content/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "93c225056acbb5484fe7994c647f755f93adf958", "node_type": "4", "metadata": {"file_path": "content/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/_index.md"}, "hash": "3c54bc1d6adeaab54c862a519162f5440745eac80d5b173b7a94af009c0608f5", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: Introduction\ntype: docs\n---\n\n# Welcome to the Research Engineering Group Handbook\n\n## What is this?\n\nThis Handbook is written by and for [The Alan Turing Institute's](https://www.turing.ac.uk/) Research Engineering Group (REG).\nIt is our attempt to describe both how we do work and how we aspire to.\nIt is inspired by other open, community-driven books such as [The GitLab Team Handbook](https://about.gitlab.com/handbook/) and [The Turing Way](https://the-turing-way.netlify.app/collaboration/remote-collab.html).\n\n## Who is the audience?\n\nAnyone!\nMost commonly it is used by members of REG in their day-to-day work.\nIt also aims to be a helpful introduction to REG for anyone who might work with us, either as collaborators or \u2014 when the opportunity arises \u2014 as [future colleagues](https://www.turing.ac.uk/opportunities-turing/jobs).\n\nIt is open to all, in the hope others will find it useful too.\n\n## Who are REG anyway?\n\nFor an introduction to REG and what we do see our page on the [Turing website](https://www.turing.ac.uk/research-engineering).", "start_char_idx": 0, "end_char_idx": 1063, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d0a13454-b117-45a1-a7f3-c088eaae7dce": {"__data__": {"id_": "d0a13454-b117-45a1-a7f3-c088eaae7dce", "embedding": null, "metadata": {"file_path": "content/docs/communications/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/communications/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cf380f2bc01891cffe4e2b991e3427cc023b8c89", "node_type": "4", "metadata": {"file_path": "content/docs/communications/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/communications/_index.md"}, "hash": "bebd1c752fd41aa8772dc023f9078ff18dc43fd399c23b36758903ffd1d93c4c", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Communications\"\nweight: 7\n---\n\n# Communications", "start_char_idx": 0, "end_char_idx": 59, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3c7bf40c-c5e4-40db-80b2-dbb4e0f11289": {"__data__": {"id_": "3c7bf40c-c5e4-40db-80b2-dbb4e0f11289", "embedding": null, "metadata": {"file_path": "content/docs/communications/social_media.md", "file_name": "social_media.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/communications/social_media.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "fd1b5253d191ca0839c393826ddf12f23b54dd1d", "node_type": "4", "metadata": {"file_path": "content/docs/communications/social_media.md", "file_name": "social_media.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/communications/social_media.md"}, "hash": "84db9233a2e66d92c4e6692b8bce6bc9243fc70c49d3cc49ce077b7d92a6863c", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Social Media\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Social Media\n\n## LinkedIn\n\nPage: [turing-research-engineering](https://www.linkedin.com/showcase/turing-research-engineering/?viewAsMember=true)\n\nOur LinkedIn page showcases the work done under the Alan Turing Institute umbrella and highlights the achievements of our researchers and engineers to a professional audience.\n\n## X (Formerly Twitter)\n\nHandle: [@turinghut23](https://twitter.com/turinghut23)\n\nOur Twitter account offers an informal public-facing channel for sharing updates about our projects and life at REG, engaging with our audience through insights, and updates.\n\n## Medium\n\nHandle: [@turinghut23](https://medium.com/@turinghut23)\n\nOur Medium account hosts blog posts about our projects and experiences within REG, providing in-depth analysis, reflections, and insights into our work and research at the Alan Turing Institute.\n\n## Creating Posts\n\nIf you would like to create a post under any of the group accounts, please reach out to [the account\ncontroller(s)](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry).\n\n### What to Post\n\n- Projects we work on (if public)\n- Blog posts\n- Talks that we organise\n- Conferences and workshops\n- Any public outputs \u2014 papers, published code _etc._\n- Researcher spotlights\n\nPlease refer to the Turing's [social media\npolicy](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2203&SearchId=11587) when making posts.", "start_char_idx": 0, "end_char_idx": 1819, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "672cbc89-3888-483a-a14c-7e3ed578d4b4": {"__data__": {"id_": "672cbc89-3888-483a-a14c-7e3ed578d4b4", "embedding": null, "metadata": {"file_path": "content/docs/contributing/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f841571dd12d2ae66ee9a9e7a07f95e82355010a", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/_index.md"}, "hash": "9f200d8e648bd3805262a92b70c17097b2ad6ed7a32a8f185f17f62617fa80cd", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Contributing\"\nweight: 99\n---\n\n# Contributing\n\nThis section contains a guide for contributing to the handbook.\nIt is not a comprehensive guide to Hugo or any of the other tools used.\nInstead it is aimed to be a user-focused guide explaining how add to or edit the handbook without dwelling on the details of the underlying technologies.\nFor those interested in learning more, links will be liberally placed throughout for further reading.", "start_char_idx": 0, "end_char_idx": 449, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e11b64d8-ec6b-4956-8a57-ad499b1eb9b7": {"__data__": {"id_": "e11b64d8-ec6b-4956-8a57-ad499b1eb9b7", "embedding": null, "metadata": {"file_path": "content/docs/contributing/advanced.md", "file_name": "advanced.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/advanced.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a394749d72c4f3795a384330295b06c9846db26c", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/advanced.md", "file_name": "advanced.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/advanced.md"}, "hash": "3b780a0453babd4dba909081d7115e7f723df6c63448ede5ea48c6c5072775b0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Advanced\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 7\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Advanced\n\n## Using Data\n\nThrough using templates Hugo can [build page content from datafiles](https://gohugo.io/templates/data-templates/).\nThis is particularly useful for when you want to display structured data in a page and when it would be easier to maintain a datafile rather than a Markdown or HTML document.\n\n## Creating Shortcodes\n\nIt is possible to [create your own shortcodes](https://gohugo.io/templates/shortcode-templates/).\nThese should be placed in {{% repo_link path=\"layouts/shortcodes/\" text=\"`layouts/shortcodes/`\" %}}.\n\nIf you feel the need to use HTML or want to create page content from a datafile (like a YAML file or csv) then a shortcode is probably the right answer.\n\nYou should refer to Hugo's [templates](https://gohugo.io/templates/) and [functions](https://gohugo.io/functions/) documentation for resources to help writing a shortcode.\n\n## Partial Templates\n\nWhole page templates are built from of a number of smaller [partial templates](https://gohugo.io/templates/partials/).\nThis approach reduces repeated code in templates and help keep making changes simple.\n\nThe theme defines a number of dummy [partial templates](https://github.com/alex-shpak/hugo-book#partials) for us to overwrite.\n\nFor example, the Creative Commons notice at the bottom of each page was added by editing {{% repo_link path=\"layouts/partials/docs/inject/footer.html\" text=\"`layouts/partial/docs/inject/footer.html`\" %}}.", "start_char_idx": 0, "end_char_idx": 1817, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "721230de-13e8-4aa4-be8f-e6bfc5b79200": {"__data__": {"id_": "721230de-13e8-4aa4-be8f-e6bfc5b79200", "embedding": null, "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3055cf35fa98f5a6b1f22f976a37a42ed6594d2d", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9cde67cdbd198dc11182f0acdd1fd8a07a500c740b5fd4e46ff594572e52163a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "91d01686-e2e1-4e30-8f24-54bc362bfde6", "node_type": "1", "metadata": {}, "hash": "63ab73b600e6dfedc4b816cb5301e54a780a5f545e03e09648f6f2d499c65ae0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Contributing Changes\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 5\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Contributing Changes\n\nOnce you have made changes, you will need to contribute them back to the remote repository.\nThis page explains the process of pushing your changes back to the remote, and how your contribution will be checked in the CI and review process.\n\nThe handbook uses the [GitHub flow](https://docs.github.com/en/get-started/quickstart/github-flow) workflow.\nIn short:\n\n- create a branch\n- make changes\n- open pull request\n- review\n- merge to `main`\n\nThis workflow should help changes be deployed quickly.\n\n## Creating a Branch\n\nYou should make your changes on a new branch, and not on `main`.\nThis will make it easier to merge changes from many contributors.\nTry to pick a branch name which is short and describes the change you are making.\n\n{{% hint warning %}}\nThe changes on one branch or pull request should address a single issue and be self-contained.\nDon't try to solve more than one unrelated problem at once.\n\nSticking to this practice will help ensure pull requests are small and easy to review.\nOtherwise, you might end up having to make many changes during review, have difficultly keeping your branch up to date or upset the reviewers.\n{{% /hint %}}\n\n## Making Changes\n\nAt this point, you can edit files and create `git` commits as necessary.\n\n{{% hint info %}}\nWhen making commits, it is a good idea to follow Tim Pope's [recommendations](https://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html) for good commit messages.\n{{% /hint %}}\n\nIt is often useful to run the Markdown style checker (and other tests) before committing.\nThis allows you to catch, and fix, potential issues before opening a pull request.", "start_char_idx": 0, "end_char_idx": 2052, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "91d01686-e2e1-4e30-8f24-54bc362bfde6": {"__data__": {"id_": "91d01686-e2e1-4e30-8f24-54bc362bfde6", "embedding": null, "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3055cf35fa98f5a6b1f22f976a37a42ed6594d2d", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9cde67cdbd198dc11182f0acdd1fd8a07a500c740b5fd4e46ff594572e52163a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "721230de-13e8-4aa4-be8f-e6bfc5b79200", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9f93032d71d0e43fb439fee15c35607cbba50ef6f7d18b8711c09f8167990733", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "72f60be5-209a-4251-9961-999d7847be69", "node_type": "1", "metadata": {}, "hash": "be11a7c67ac28d5cbac0dbd2a658dfd798dce7c7f3c4c92a38ce9be7846609db", "class_name": "RelatedNodeInfo"}}, "text": "{{% hint warning %}}\nThe changes on one branch or pull request should address a single issue and be self-contained.\nDon't try to solve more than one unrelated problem at once.\n\nSticking to this practice will help ensure pull requests are small and easy to review.\nOtherwise, you might end up having to make many changes during review, have difficultly keeping your branch up to date or upset the reviewers.\n{{% /hint %}}\n\n## Making Changes\n\nAt this point, you can edit files and create `git` commits as necessary.\n\n{{% hint info %}}\nWhen making commits, it is a good idea to follow Tim Pope's [recommendations](https://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html) for good commit messages.\n{{% /hint %}}\n\nIt is often useful to run the Markdown style checker (and other tests) before committing.\nThis allows you to catch, and fix, potential issues before opening a pull request.\nTo do so, use the [`pre-commit` tool](https://pre-commit.com/): the website contains installation instructions if you have not used it before.\nAfter installing `pre-commit`, navigate to the repository root and run:\n\n```bash\npre-commit install\n```\n\nThis uses the {{% repo_link path=\".pre-commit-config.yaml\" text=\"`.pre-commit-config.yaml`\" %}} file in the repository to create a pre-commit [Git hook](https://git-scm.com/book/en/v2/Customizing-Git-Git-Hooks),\nwhich (as the name suggests) will be automatically run every time before a commit is created.\nIt currently performs the following checks, which are designed to mimic those in [Continuous Integration](#continuous-integration):\n\n- Markdown linting\n- A Hugo build, to make sure that the pages can be built\n\nIf you want to manually perform these checks at any point in time, the following will check all files that have changed since the last commit:\n\n```bash\npre-commit run\n```\n\nTo check all files, regardless of their changed status:\n\n```bash\npre-commit run --all-files\n```\n\n## Opening a Pull Request\n\nWhen you think your changes are ready to be merged, open a pull request.", "start_char_idx": 1157, "end_char_idx": 3185, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "72f60be5-209a-4251-9961-999d7847be69": {"__data__": {"id_": "72f60be5-209a-4251-9961-999d7847be69", "embedding": null, "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3055cf35fa98f5a6b1f22f976a37a42ed6594d2d", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9cde67cdbd198dc11182f0acdd1fd8a07a500c740b5fd4e46ff594572e52163a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "91d01686-e2e1-4e30-8f24-54bc362bfde6", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "28147e490299ba5ad9f9336c35401a1459e46cc81a815c6623d95a36d1d0f2bb", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b22dd272-77eb-482f-b515-14e76a55a8b7", "node_type": "1", "metadata": {}, "hash": "cada229f434d5345fabdf2d11c35cd429296b08b7a7725d4b2f168b20f44f3ba", "class_name": "RelatedNodeInfo"}}, "text": "It currently performs the following checks, which are designed to mimic those in [Continuous Integration](#continuous-integration):\n\n- Markdown linting\n- A Hugo build, to make sure that the pages can be built\n\nIf you want to manually perform these checks at any point in time, the following will check all files that have changed since the last commit:\n\n```bash\npre-commit run\n```\n\nTo check all files, regardless of their changed status:\n\n```bash\npre-commit run --all-files\n```\n\n## Opening a Pull Request\n\nWhen you think your changes are ready to be merged, open a pull request.\nIf you don't want your changes to be merged yet but would like some feedback, you can open a draft pull request.\n\nWhen you open a pull request you will be presented with a template.\nComplete the sections and answer the questions as best you can.\nIn particular, make sure to reference any issues your work closes or is related to.\n\n### Continuous Integration\n\nA series of tests are automatically conducted on each commit pushed to GitHub, as well as each pull request.\nThe tests are defined in {{% repo_link path=\".github/workflows/ci.yaml\" text=\"`ci.yaml`\" %}}.\n\nThe following tests are required to pass before merging a pull request:\n\n- A Markdown linter\n- A Hugo build\n\nThere are other tests that are not *required* for merging, although you should aim to pass all tests.\nThose are:\n\n- A Markdown link checker\n- A HTML proofer (including link checking) on the output of Hugo\n\n## Keeping Your Branch up to Date\n\nThe branch protection rules applied to `main` will prevent out-of-date branches from being merged.\nThis means if your branch doesn't include the changes from the `HEAD` of `origin/main`, it won't be possible to merge it.\n\nIf your branch isn't up to date, you can fix it with a merge or rebase.\n\n### Merging\n\n[Merging](https://git-scm.com/book/en/v2/Git-Branching-Basic-Branching-and-Merging) the changes from `origin/main` is probably the simpler way to get your branch up to date.\nIt will leave the Git history a bit messier which can bother some people.", "start_char_idx": 2607, "end_char_idx": 4654, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b22dd272-77eb-482f-b515-14e76a55a8b7": {"__data__": {"id_": "b22dd272-77eb-482f-b515-14e76a55a8b7", "embedding": null, "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3055cf35fa98f5a6b1f22f976a37a42ed6594d2d", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9cde67cdbd198dc11182f0acdd1fd8a07a500c740b5fd4e46ff594572e52163a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "72f60be5-209a-4251-9961-999d7847be69", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "df27f892d609b2aece963720792ea5c3756a0a667df5b7027dd7aa263d6c01cb", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c8d40a5f-08f1-49b4-aec3-74fe69681315", "node_type": "1", "metadata": {}, "hash": "52d85e2161e8eb73a15c517a70648c3161a62bccd25ae7b168d8e79901139216", "class_name": "RelatedNodeInfo"}}, "text": "Those are:\n\n- A Markdown link checker\n- A HTML proofer (including link checking) on the output of Hugo\n\n## Keeping Your Branch up to Date\n\nThe branch protection rules applied to `main` will prevent out-of-date branches from being merged.\nThis means if your branch doesn't include the changes from the `HEAD` of `origin/main`, it won't be possible to merge it.\n\nIf your branch isn't up to date, you can fix it with a merge or rebase.\n\n### Merging\n\n[Merging](https://git-scm.com/book/en/v2/Git-Branching-Basic-Branching-and-Merging) the changes from `origin/main` is probably the simpler way to get your branch up to date.\nIt will leave the Git history a bit messier which can bother some people.\n\nEnsure you have the latest changes on `origin/main` in your local repository\n\n```bash\ngit fetch\n```\n\nMerge `origin/main` into your branch.\nIf your branch was called `my_branch` you would run\n\n```bash\ngit switch my_branch\ngit merge origin/main\n```\n\nGit will notify you of any conflicts.\nIf there are conflicts, you should fix them before running `git merge --continue` to commit.\nAfter you are done, you can push your changes.\n\n### Rebasing\n\n[Rebasing](https://git-scm.com/book/en/v2/Git-Branching-Rebasing) your branch on top of `origin/main` can make conflicts more difficult or confusing to deal with, but leads to a more linear Git history..\n\nEnsure you have the latest changes on `origin/main` in your local repository\n\n```bash\ngit fetch\n```\n\nApply your commits on top of `origin/main`.\nIf your branch was called `my_branch` you would run\n\n```bash\ngit switch my_branch\ngit rebase origin/main\n```\n\nIf you want to tidy up your commits or messages you might want to use an interactive rebase: `git rebase -i origin/main`.\n\nGit will notify you of any conflicts.\nIf there are conflicts you will need to fix them iteratively, running `git rebase --continue` to apply each commit.", "start_char_idx": 3960, "end_char_idx": 5833, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c8d40a5f-08f1-49b4-aec3-74fe69681315": {"__data__": {"id_": "c8d40a5f-08f1-49b4-aec3-74fe69681315", "embedding": null, "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3055cf35fa98f5a6b1f22f976a37a42ed6594d2d", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "9cde67cdbd198dc11182f0acdd1fd8a07a500c740b5fd4e46ff594572e52163a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "b22dd272-77eb-482f-b515-14e76a55a8b7", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/contributing_changes.md", "file_name": "contributing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/contributing_changes.md"}, "hash": "052823287b8ef7c64127674bfc14c951b25d905dc79cccc062138213a58aa79c", "class_name": "RelatedNodeInfo"}}, "text": "After you are done, you can push your changes.\n\n### Rebasing\n\n[Rebasing](https://git-scm.com/book/en/v2/Git-Branching-Rebasing) your branch on top of `origin/main` can make conflicts more difficult or confusing to deal with, but leads to a more linear Git history..\n\nEnsure you have the latest changes on `origin/main` in your local repository\n\n```bash\ngit fetch\n```\n\nApply your commits on top of `origin/main`.\nIf your branch was called `my_branch` you would run\n\n```bash\ngit switch my_branch\ngit rebase origin/main\n```\n\nIf you want to tidy up your commits or messages you might want to use an interactive rebase: `git rebase -i origin/main`.\n\nGit will notify you of any conflicts.\nIf there are conflicts you will need to fix them iteratively, running `git rebase --continue` to apply each commit.\n\nWhen the rebase is done, you will need to force-push your changes as you have rewritten the history of your branch.\n\n```bash\ngit push -f\n```\n\n{{% hint warning %}}\nRewriting history is considered impolite on a branch where you are working collaboratively.\nIt can cause problems when the state of a remote changes unexpectedly.\n{{% /hint %}}\n\n## The Review Process\n\nCurrently, reviews are not required and pull requests may be merged by anyone with write access once all required CI checks pass.\nIf you like, you can enable [auto-merge](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/incorporating-changes-from-a-pull-request/automatically-merging-a-pull-request) so that the merge happens automatically when this is the case.\n\nHowever, if you'd like your changes reviewed you can suggest reviewers when, or after, opening your pull request.\nIf a reviewer then requests any changes or makes any comments, these should be resolved before the pull request is merged.", "start_char_idx": 5035, "end_char_idx": 6824, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f5f01aab-661a-4766-9639-edcb7719160c": {"__data__": {"id_": "f5f01aab-661a-4766-9639-edcb7719160c", "embedding": null, "metadata": {"file_path": "content/docs/contributing/creating_a_page.md", "file_name": "creating_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/creating_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a296d4feb5a490599d60d8bed68961c31624c8fb", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/creating_a_page.md", "file_name": "creating_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/creating_a_page.md"}, "hash": "0880a5253ece62db93097a96950cbadede94d5b79e870fcc7fdbd1d787960b65", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "052be6ea-fc41-49cb-a5fe-5309172d18fc", "node_type": "1", "metadata": {}, "hash": "5c5b53839be7e0587796f71dc833c454c998d7f1b40112755fb91bab2a65494c", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Adding New Content\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 3\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Adding New Content\n\n## How Hugo Arranges Content\n\nPages are built from content files in the {{% repo_link path=\"content/\" text=\"`content/`\" %}} directory.\nHugo automatically gives pages a URL based on the organisation of files in the `content/` directory.\n\nIn Hugo, the first directory after `content/` is significant and [defines the content type](https://gohugo.io/content-management/sections/).\nAll handbook pages should be nested in the `content/docs/` directory.\n\nSections may be created in `content/docs/` (to any depth) by creating a directory containing a content file called `_index.md`.\n\n{{% hint warning %}}\nIt is important that the index file exists for Hugo to [correctly assign pages to sections](https://gohugo.io/content-management/sections/) and for the [theme](https://github.com/alex-shpak/hugo-book) to arrange pages in the tree-like menu.\n{{% /hint %}}\n\n## Creating a New Page\n\nTo create a new handbook page use the `hugo new` command.\n\n```bash\nhugo new content/docs/<path/to/page>.md\n```\n\nIf you wanted to create this page, you would type\n\n```bash\nhugo new content/docs/contributing/creating_a_page.md\n```\n\nHugo will create a new Markdown document at the path you specified.\n\nHugo provides a convenient way to generate new pages from templates called [Archetypes](https://gohugo.io/content-management/archetypes/).\nYou can see the archetypes in the {{% repo_link path=\"archetypes\" text=\"`archetypes/` directory\" %}}.", "start_char_idx": 0, "end_char_idx": 1838, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "052be6ea-fc41-49cb-a5fe-5309172d18fc": {"__data__": {"id_": "052be6ea-fc41-49cb-a5fe-5309172d18fc", "embedding": null, "metadata": {"file_path": "content/docs/contributing/creating_a_page.md", "file_name": "creating_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/creating_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a296d4feb5a490599d60d8bed68961c31624c8fb", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/creating_a_page.md", "file_name": "creating_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/creating_a_page.md"}, "hash": "0880a5253ece62db93097a96950cbadede94d5b79e870fcc7fdbd1d787960b65", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f5f01aab-661a-4766-9639-edcb7719160c", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/creating_a_page.md", "file_name": "creating_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/creating_a_page.md"}, "hash": "78b2c61181dc6a809a49b0c839f62906f9e4c28bdfec42ee64a5567b26e9b026", "class_name": "RelatedNodeInfo"}}, "text": "{{% /hint %}}\n\n## Creating a New Page\n\nTo create a new handbook page use the `hugo new` command.\n\n```bash\nhugo new content/docs/<path/to/page>.md\n```\n\nIf you wanted to create this page, you would type\n\n```bash\nhugo new content/docs/contributing/creating_a_page.md\n```\n\nHugo will create a new Markdown document at the path you specified.\n\nHugo provides a convenient way to generate new pages from templates called [Archetypes](https://gohugo.io/content-management/archetypes/).\nYou can see the archetypes in the {{% repo_link path=\"archetypes\" text=\"`archetypes/` directory\" %}}.\n\nBecause your document is in the `docs` directory, Hugo will look for an archetype called {{% repo_link path=\"archetypes/docs.md\" text=\"`docs.md`\" %}} to use as a template.\n\nNow you can edit your page with your favourite text editor.\nFor example\n\n```bash\nvim content/docs/contributing/creating_a_page.md\n```\n\n## Creating a Section\n\nWe can also use the `hugo new` command to create a new section, both the directory and index file.\nFor example\n\n```bash\nhugo new content/docs/new_section/_index.md\nvim content/docs/new_section/_index.md\n```\n\nIf you only want a section to organise some pages and not to have a page of its own, you can simply leave the content section of `_index.md` (after the YAML front matter) empty.", "start_char_idx": 1260, "end_char_idx": 2556, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b6b0ec4b-ec86-4351-b63f-6bd042d13ec2": {"__data__": {"id_": "b6b0ec4b-ec86-4351-b63f-6bd042d13ec2", "embedding": null, "metadata": {"file_path": "content/docs/contributing/discussions_and_issues.md", "file_name": "discussions_and_issues.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/discussions_and_issues.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9e31d5beddf2be8d558ea581d08d793a8dd542a9", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/discussions_and_issues.md", "file_name": "discussions_and_issues.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/discussions_and_issues.md"}, "hash": "15276ab2ebb81efce3460866de6c086a813b808fa40dccbaa7145d57a00259e3", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Discussions and Issues\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: -2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Discussions and Issues\n\nContributing does not only mean adding code or writing pages.\nBeing involved in reporting issues and discussing ideas are important and valuable aspects to contributing.\nThe handbook uses both issues and discussions on GitHub.\n\n## Discussions\n\n[The *Discussions* on the handbook repository](https://github.com/alan-turing-institute/REG-handbook/discussions) are the best place for informal talk about the handbook.\n\nYou should feel welcome to create a discussion on any relevant topic, without the formality of an issue.\n\nGood examples of discussions are\n\n- Any questions\n- Possible bugs ('does anyone else have this problem?')\n- Chapter suggestions\n- Looking for collaborators\n- Community support\n- Any other on-topic talk\n\n## Issues\n\n[The issue tracker](https://github.com/alan-turing-institute/REG-handbook/issues) is best used for development work.\nThis is because issues integrate well with GitHub development tools like projects, pull requests, assignments and so on.\n\nEach issue should ideally represent a well-defined, self-contained piece of work suitable to become a single pull request.\n\nGood examples of issues are\n\n- Bug reports with technical detail\n- Developed chapter proposals\n- Feature requests (such as new shortcodes)\n- Specific ideas for changes\n\nWhen opening an issue, pick a suitable template (if any) to make the process easier.", "start_char_idx": 0, "end_char_idx": 1781, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "4855e3ee-b3fc-46fc-a437-576b42bb2b5e": {"__data__": {"id_": "4855e3ee-b3fc-46fc-a437-576b42bb2b5e", "embedding": null, "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "54ee33ee0e3a07ea15165101dcbc06495eced766", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "11fb9417db52b3e76bd93df7d52f61aec7f5ae3d9674b0e4d898bb59c5625d61", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f020b5d1-5ac0-4c4d-83f6-ab67bc6dfb48", "node_type": "1", "metadata": {}, "hash": "abfa0b3ca07647b546d46711da111fb2ba6c653a7aa1f8a9b8ac4a0a57c27d15", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Editing a Page\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 4\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Editing a Page\n\nIf you followed the instructions in the [Getting Started section]({{% ref \"getting_started.md\" %}}) to checkout the repository and serve the handbook locally you can edit a page locally.\nHowever, as you may have noticed, at the bottom of each page is a link to edit the page in the GitHub web editor if you would prefer.\nThis may be easy for making small changes.\n\n## Pages\n\nEach page is a [Markdown](https://www.markdownguide.org/) file with [YAML](https://yaml.org/) front matter followed by the page contents in Markdown.\n\n### Front Matter\n\nThe [front matter](https://gohugo.io/content-management/front-matter/) is used to define various pieces of metadata related to a page.\nThe front matter appears at the top of a content file.\nIn the handbook we format front matter as YAML, preceded and followed by three hyphens.\n\n```yaml\n---\ntitle: \"Example Page\"\nweight: 1\n---\n```\n\nThe [full YAML specification](https://yaml.org/spec/1.2.2/) is long and comprehensive.\nThe most important thing to understand here is that the front matter YAML consists of keys and values separated by a hyphen.\nFor example, in the expression `weight: 1`, `weight` is the key with a value of `1`.\n\nIf you created a page using `hugo new` then some boilerplate front matter with explanatory comments should already be present.\nIf you are editing an existing page there should already be front matter.\n\nMost of the time, the only keys you will need to consider are `title` and `weight`.\n\n`title`\n: The title of a page as it appears in the navigation menu\n\n`weight`\n: Determines the order of pages in the navigation menu.", "start_char_idx": 0, "end_char_idx": 2005, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f020b5d1-5ac0-4c4d-83f6-ab67bc6dfb48": {"__data__": {"id_": "f020b5d1-5ac0-4c4d-83f6-ab67bc6dfb48", "embedding": null, "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "54ee33ee0e3a07ea15165101dcbc06495eced766", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "11fb9417db52b3e76bd93df7d52f61aec7f5ae3d9674b0e4d898bb59c5625d61", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "4855e3ee-b3fc-46fc-a437-576b42bb2b5e", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "d57a44f9f94a9721de18c8caec3d756720b201017a91d0638215783fe7785f4f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c27977c8-8d38-420a-ae8a-cd48bd5d0f77", "node_type": "1", "metadata": {}, "hash": "96bd306c42f02d01ae65a97cbe1115a97ee6918da8c95720c12469b7ae8ce80f", "class_name": "RelatedNodeInfo"}}, "text": "```yaml\n---\ntitle: \"Example Page\"\nweight: 1\n---\n```\n\nThe [full YAML specification](https://yaml.org/spec/1.2.2/) is long and comprehensive.\nThe most important thing to understand here is that the front matter YAML consists of keys and values separated by a hyphen.\nFor example, in the expression `weight: 1`, `weight` is the key with a value of `1`.\n\nIf you created a page using `hugo new` then some boilerplate front matter with explanatory comments should already be present.\nIf you are editing an existing page there should already be front matter.\n\nMost of the time, the only keys you will need to consider are `title` and `weight`.\n\n`title`\n: The title of a page as it appears in the navigation menu\n\n`weight`\n: Determines the order of pages in the navigation menu. Smaller numbers appear\nfirst\n\nThe Hugo documentation details a set of [predefined front matter keys](https://gohugo.io/content-management/front-matter/).\nOther valid fields related to the handbook theme are documented in the [theme's\nREADME](https://github.com/alex-shpak/hugo-book#page-configuration).\n\n### Content\n\nAfter the front matter is the content section.\n\n```markdown\n---\ntitle: \"Example Page\"\nweight: 1\n---\n\n| content section |\n```\n\nThe content section is formatted in Markdown.\nThe following links from Markdown Guide describe [basic](https://www.markdownguide.org/basic-syntax/) and [extended](https://www.markdownguide.org/extended-syntax/) Markdown syntax.\n\n{{% hint warning %}}\nNot all of the extended Markdown syntax may be implemented by Hugo's [Markdown renderer](https://gohugo.io/getting-started/configuration-markup).\n{{% /hint %}}\n\n### Shortcodes\n\n[Shortcodes](https://gohugo.io/content-management/shortcodes/) are templates (which can be parametrised) and included the content section.\nThey can be particularly useful for including more complex features than Markdown's simple syntax will allow.\nUsing a shortcode is preferable to including raw HTML in a content file.", "start_char_idx": 1235, "end_char_idx": 3197, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c27977c8-8d38-420a-ae8a-cd48bd5d0f77": {"__data__": {"id_": "c27977c8-8d38-420a-ae8a-cd48bd5d0f77", "embedding": null, "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "54ee33ee0e3a07ea15165101dcbc06495eced766", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "11fb9417db52b3e76bd93df7d52f61aec7f5ae3d9674b0e4d898bb59c5625d61", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f020b5d1-5ac0-4c4d-83f6-ab67bc6dfb48", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "403960fd06a08f7efe327495e7b9dd145838df6bed2c456d751383bf9455f24b", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2a44316f-12b9-4af9-9413-39ce6f9a126d", "node_type": "1", "metadata": {}, "hash": "9845709cac8fa1fb2c7e38ab8b76913f4c983c801b8e77a19b54bca4d6be152b", "class_name": "RelatedNodeInfo"}}, "text": "### Content\n\nAfter the front matter is the content section.\n\n```markdown\n---\ntitle: \"Example Page\"\nweight: 1\n---\n\n| content section |\n```\n\nThe content section is formatted in Markdown.\nThe following links from Markdown Guide describe [basic](https://www.markdownguide.org/basic-syntax/) and [extended](https://www.markdownguide.org/extended-syntax/) Markdown syntax.\n\n{{% hint warning %}}\nNot all of the extended Markdown syntax may be implemented by Hugo's [Markdown renderer](https://gohugo.io/getting-started/configuration-markup).\n{{% /hint %}}\n\n### Shortcodes\n\n[Shortcodes](https://gohugo.io/content-management/shortcodes/) are templates (which can be parametrised) and included the content section.\nThey can be particularly useful for including more complex features than Markdown's simple syntax will allow.\nUsing a shortcode is preferable to including raw HTML in a content file.\n\nShortcodes can called in Markdown content files the following way\n\n```markdown\n{{%/* shortcode parameter */%}}\n```\n\nSome shortcodes may have an opening and closing tag, similar to HTML\n\n```markdown\n{{%/* shortcode */%}} content to process {{%/* /shortcode */%}}\n```\n\n#### Hugo Shortcodes\n\nHugo has a set of useful [built-in shortcodes](https://gohugo.io/content-management/shortcodes/#embedded-shortcodes).\n\nIn particular\n\n- [figure](https://gohugo.io/content-management/shortcodes/#figure)\n- [gist](https://gohugo.io/content-management/shortcodes/#gist)\n- [highlight](https://gohugo.io/content-management/shortcodes/#highlight)\n- [param](https://gohugo.io/content-management/shortcodes/#param)\n- [ref](https://gohugo.io/content-management/shortcodes/#ref) and [relref](https://gohugo.io/content-management/shortcodes/#relref)\n\n#### Theme Shortcodes\n\nThe theme has a [number of shortcodes](https://github.com/alex-shpak/hugo-book#shortcodes) which may be helpful.", "start_char_idx": 2310, "end_char_idx": 4162, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2a44316f-12b9-4af9-9413-39ce6f9a126d": {"__data__": {"id_": "2a44316f-12b9-4af9-9413-39ce6f9a126d", "embedding": null, "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "54ee33ee0e3a07ea15165101dcbc06495eced766", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "11fb9417db52b3e76bd93df7d52f61aec7f5ae3d9674b0e4d898bb59c5625d61", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "c27977c8-8d38-420a-ae8a-cd48bd5d0f77", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/editing_a_page.md", "file_name": "editing_a_page.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/editing_a_page.md"}, "hash": "5cb7c22206bb15ba2759a7031d2b64281fa14b03b4af75670088139fa6f3b3bf", "class_name": "RelatedNodeInfo"}}, "text": "In particular\n\n- [figure](https://gohugo.io/content-management/shortcodes/#figure)\n- [gist](https://gohugo.io/content-management/shortcodes/#gist)\n- [highlight](https://gohugo.io/content-management/shortcodes/#highlight)\n- [param](https://gohugo.io/content-management/shortcodes/#param)\n- [ref](https://gohugo.io/content-management/shortcodes/#ref) and [relref](https://gohugo.io/content-management/shortcodes/#relref)\n\n#### Theme Shortcodes\n\nThe theme has a [number of shortcodes](https://github.com/alex-shpak/hugo-book#shortcodes) which may be helpful.\n\nIn particular\n\n- [hints](https://hugo-book-demo.netlify.app/docs/shortcodes/hints/)\n- [expand](https://hugo-book-demo.netlify.app/docs/shortcodes/expand/)\n- [KaTeX](https://hugo-book-demo.netlify.app/docs/shortcodes/katex/)\n- [tabs](https://hugo-book-demo.netlify.app/docs/shortcodes/tabs/)\n\n#### Repository Shortcodes\n\nShortcodes can be included in the handbooks repository.\nFor more information see [Creating Shortcodes]({{% ref \"advanced.md#creating-shortcodes\" %}})\n\n##### repo_link\n\nCreate a link to a file or directory in the handbook's repository.\n\n```markdown\n{{%/* repo_link path=\"README.md\" text=\"README\" */%}}\n```\n\n`path`\n: Path to the file or directory relative to the root of the repository\n\n`text`\n: Text of the link (can include Markdown formatting)", "start_char_idx": 3607, "end_char_idx": 4928, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "cd8d3ab6-1259-40a0-a44b-7f679fee56d4": {"__data__": {"id_": "cd8d3ab6-1259-40a0-a44b-7f679fee56d4", "embedding": null, "metadata": {"file_path": "content/docs/contributing/getting_started.md", "file_name": "getting_started.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/getting_started.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3ad4a616007ca5441cfcd229f6688e3d939bc374", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/getting_started.md", "file_name": "getting_started.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/getting_started.md"}, "hash": "7fdce3c8f4350ee1dca51660676d6144c25af306985d203f0d653d00b7a46323", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f215f5cf-5057-4b73-a09c-a6ef0b073beb", "node_type": "1", "metadata": {}, "hash": "f6a992aff34c76e7d9c2e220977ffdf1bc725d9a8abca013020a6798d93b7327", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Getting Started\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Getting Started\n\n## Install dependencies\n\nTo contribute to the handbook you will need\n\n- [Git](https://git-scm.com/downloads)\n- [Hugo](https://gohugo.io/getting-started/installing/) (You will need the extended version with Sass/SCSS support)\n- Your favourite text editor\n- Optionally [pre-commit](https://pre-commit.com/); see [Making Changes]({{< relref \"/docs/contributing/contributing_changes.md#making-changes\" >}}) for more details.\n\n## Clone the repository\n\nClone the repository with one of the following options (depending on how you authenticate with GitHub):\n\n{{< tabs \"clone-command\" >}}\n{{< tab \"SSH authentication\" >}}\n\n```bash\ngit clone --recurse-submodules git@github.com:alan-turing-institute/REG-handbook.git\ncd REG-handbook\n```\n\n{{< /tab >}}\n{{< tab \"HTTPS authentication\" >}}\n\n```bash\ngit clone --recurse-submodules https://github.com/alan-turing-institute/REG-handbook.git\ncd REG-handbook\n```\n\n{{< /tab >}}\n{{< /tabs >}}\n\nThe `--recurse-submodules` flag is important as the handbook's theme is included as a [Git submodule](https://git-scm.com/book/en/v2/Git-Tools-Submodules).\n\n## Serve the handbook locally\n\nWhile working on the handbook, it will be useful to preview your changes locally.", "start_char_idx": 0, "end_char_idx": 1607, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f215f5cf-5057-4b73-a09c-a6ef0b073beb": {"__data__": {"id_": "f215f5cf-5057-4b73-a09c-a6ef0b073beb", "embedding": null, "metadata": {"file_path": "content/docs/contributing/getting_started.md", "file_name": "getting_started.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/getting_started.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3ad4a616007ca5441cfcd229f6688e3d939bc374", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/getting_started.md", "file_name": "getting_started.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/getting_started.md"}, "hash": "7fdce3c8f4350ee1dca51660676d6144c25af306985d203f0d653d00b7a46323", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "cd8d3ab6-1259-40a0-a44b-7f679fee56d4", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/getting_started.md", "file_name": "getting_started.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/getting_started.md"}, "hash": "4682b14684e265293dd055c18266716591a1aca15a92db8dc48599d98d4a156d", "class_name": "RelatedNodeInfo"}}, "text": "## Clone the repository\n\nClone the repository with one of the following options (depending on how you authenticate with GitHub):\n\n{{< tabs \"clone-command\" >}}\n{{< tab \"SSH authentication\" >}}\n\n```bash\ngit clone --recurse-submodules git@github.com:alan-turing-institute/REG-handbook.git\ncd REG-handbook\n```\n\n{{< /tab >}}\n{{< tab \"HTTPS authentication\" >}}\n\n```bash\ngit clone --recurse-submodules https://github.com/alan-turing-institute/REG-handbook.git\ncd REG-handbook\n```\n\n{{< /tab >}}\n{{< /tabs >}}\n\nThe `--recurse-submodules` flag is important as the handbook's theme is included as a [Git submodule](https://git-scm.com/book/en/v2/Git-Tools-Submodules).\n\n## Serve the handbook locally\n\nWhile working on the handbook, it will be useful to preview your changes locally.\nTo do this use Hugo's `server` command\n\n```bash\nhugo server --minify\n```\n\nIn the output of this command will be a line similar to\n\n```bash\nWeb Server is available at http://localhost:1313/REG-handbook/ (bind address 127.0.0.1)\n```\n\nwhich contains the URL of the locally served site.\nNavigate to this address in your browser to preview the handbook.\n\nThe Hugo server will monitor the files in the repository and automatically rebuild the site whenever you make changes.\nThis is a useful tool to immediately view your changes.", "start_char_idx": 836, "end_char_idx": 2132, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b5bdd873-3d71-45d3-b88d-06193ffb2cd0": {"__data__": {"id_": "b5bdd873-3d71-45d3-b88d-06193ffb2cd0", "embedding": null, "metadata": {"file_path": "content/docs/contributing/recognising_contributions.md", "file_name": "recognising_contributions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/recognising_contributions.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3716e2551f49aa2261c6160f8a03d7939eed0a76", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/recognising_contributions.md", "file_name": "recognising_contributions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/recognising_contributions.md"}, "hash": "965836b40dce8d4c6e5f70f6b46ded76e720ef3cf287e802f39503455d2063c7", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Recognising Contributions\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: -1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Recognising Contributions\n\nWe aim to recognise all contributions to the handbook, not just those made through commits.\nFor that purpose we use [All Contributors](https://allcontributors.org/) to help manage a record of who has contributed to the book and how.\n\nUse the 'add' command in an issue or pull-request comment to ask @all-contributors to add a contributor\n\n```text\n@all-contributors please add @<username> for <contributions>\n```\n\nSee the [bot usage documentation](https://allcontributors.org/docs/en/bot/usage) for full details and more examples.\nA list of contribution types (and the related emoji) can be seen [here](https://allcontributors.org/docs/en/emoji-key).", "start_char_idx": 0, "end_char_idx": 1084, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ff919679-c51e-4f38-bf87-f5cabb8b85bb": {"__data__": {"id_": "ff919679-c51e-4f38-bf87-f5cabb8b85bb", "embedding": null, "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e347caf2f22c1a7dc2e89205dd05653f1fdceef8", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "hash": "e8f8404bef0b45f0180c3167a22a5a73a2825d299c339c88bda7424bee1698a2", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f32ce356-37da-4a43-a12d-8bf950636c30", "node_type": "1", "metadata": {}, "hash": "17fc860a9caefe3b9c04e3f1036c335c6e60651792180258c375d0564c0b94e8", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Reviewing Changes\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 6\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Reviewing Changes\n\nThe review process helps to ensure high quality and catch problems in contributions.\nWhen acting as a reviewer, you should see your position as sharing your knowledge and working with the contributors to achieve the best possible result.\nReviewing should **not** be an adversarial process.\n\n## Recognising Contributions\n\nWhen reviewing a pull request you should try to make sure everyone's work is recognised.\nSee the [Recognising Contributions]({{< relref \"/docs/contributing/recognising_contributions.md\" >}}) page for how to do this.\n\n## Code Quality\n\nAs a reviewer, one of your jobs is to ensure the quality of the codebase remains high.\n\nThe CI process will help to assess pull requests by subjecting each commit to a series of tests.\nSome tests are required to pass and will block merging until they do.\n\nOther tests are allowed to fail.\nThis is because these tests check external hyperlinks which can fail for reasons out of our control: for example, a website being offline, or an API rate limit.\nHowever, you should always aim to have all tests passing and investigate why any test fails.\nIn particular, a genuinely incorrect link should not be ignored.\n\nYou should **always** clone the branch, build the handbook locally (using `hugo server --minify`) and inspect the changes using your browser.\nNot all bugs will be caught by CI and not all changes will be obvious in the source files.\n\n## Reviewing the Pull Request\n\nUse the [GitHub review system](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/reviewing-proposed-changes-in-a-pull-request) to check the diffs of all source files.", "start_char_idx": 0, "end_char_idx": 2072, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f32ce356-37da-4a43-a12d-8bf950636c30": {"__data__": {"id_": "f32ce356-37da-4a43-a12d-8bf950636c30", "embedding": null, "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e347caf2f22c1a7dc2e89205dd05653f1fdceef8", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "hash": "e8f8404bef0b45f0180c3167a22a5a73a2825d299c339c88bda7424bee1698a2", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ff919679-c51e-4f38-bf87-f5cabb8b85bb", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "hash": "01a3713eef5d4cf0382afe3aac36d49a2ab39540fa93f5b39726bb596b6488c0", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "176c89b9-abf7-4e55-bb1c-7af2ccf0f82b", "node_type": "1", "metadata": {}, "hash": "a603186966b996d4898644b4d9e94a3486357da339524a37fdd5cb5c81d7743b", "class_name": "RelatedNodeInfo"}}, "text": "Some tests are required to pass and will block merging until they do.\n\nOther tests are allowed to fail.\nThis is because these tests check external hyperlinks which can fail for reasons out of our control: for example, a website being offline, or an API rate limit.\nHowever, you should always aim to have all tests passing and investigate why any test fails.\nIn particular, a genuinely incorrect link should not be ignored.\n\nYou should **always** clone the branch, build the handbook locally (using `hugo server --minify`) and inspect the changes using your browser.\nNot all bugs will be caught by CI and not all changes will be obvious in the source files.\n\n## Reviewing the Pull Request\n\nUse the [GitHub review system](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/reviewing-proposed-changes-in-a-pull-request) to check the diffs of all source files.\n\nYou should make use of [line comments](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/commenting-on-a-pull-request#adding-line-comments-to-a-pull-request) where you have comments or questions about particular lines of sections.\nThis gives context so that everyone knows where the problem is or what the question refers to.\n\nLine comments can also be used to suggest changes (using the \u00b1 button).\nYou should do this when you have a simple solution.\nThis is an excellent way to share knowledge.\n\nWhen you are finished, [submit your review](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/reviewing-proposed-changes-in-a-pull-request#submitting-your-review), making sure to choose \"comment\", \"approve\" or \"request changes\" as appropriate.\n\n## Changes\n\nIf you request changes, the pull request will enter an iterative process where the contributors make adjustments you repeat the review process.\n\nThe contributors may accept your proposed changes, make their own changes or push back against changes.\nAll of these may be appropriate.\nIt is important to work together with the contributors to resolve any conversations.", "start_char_idx": 1158, "end_char_idx": 3312, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "176c89b9-abf7-4e55-bb1c-7af2ccf0f82b": {"__data__": {"id_": "176c89b9-abf7-4e55-bb1c-7af2ccf0f82b", "embedding": null, "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e347caf2f22c1a7dc2e89205dd05653f1fdceef8", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "hash": "e8f8404bef0b45f0180c3167a22a5a73a2825d299c339c88bda7424bee1698a2", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f32ce356-37da-4a43-a12d-8bf950636c30", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/reviewing_changes.md", "file_name": "reviewing_changes.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/reviewing_changes.md"}, "hash": "b322d7938ad8b025fdebe0e584f2ab13ea53c6d10a80fd0a7ebe41d631217214", "class_name": "RelatedNodeInfo"}}, "text": "This gives context so that everyone knows where the problem is or what the question refers to.\n\nLine comments can also be used to suggest changes (using the \u00b1 button).\nYou should do this when you have a simple solution.\nThis is an excellent way to share knowledge.\n\nWhen you are finished, [submit your review](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/reviewing-proposed-changes-in-a-pull-request#submitting-your-review), making sure to choose \"comment\", \"approve\" or \"request changes\" as appropriate.\n\n## Changes\n\nIf you request changes, the pull request will enter an iterative process where the contributors make adjustments you repeat the review process.\n\nThe contributors may accept your proposed changes, make their own changes or push back against changes.\nAll of these may be appropriate.\nIt is important to work together with the contributors to resolve any conversations.\nAll conversations **must** be resolved before merging.\n\n## Merging\n\nUpdate your review status to \"approve\" when you are happy with the state of the pull request.\nWhen all reviewers are satisfied, merge the pull request.", "start_char_idx": 2364, "end_char_idx": 3532, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e3729547-1eca-467b-9bb0-fe1804f9a874": {"__data__": {"id_": "e3729547-1eca-467b-9bb0-fe1804f9a874", "embedding": null, "metadata": {"file_path": "content/docs/contributing/style_guide.md", "file_name": "style_guide.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/style_guide.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8b19f56f4e3d0e16ff15d6f3bc378f03d292ccae", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/style_guide.md", "file_name": "style_guide.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/style_guide.md"}, "hash": "c969046daeae1b4d7ed1ecb5132baba10ed90f952d1d0301a651cefbf1f4e2e0", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b33cd473-ff1c-4fd5-9e29-3957daf45389", "node_type": "1", "metadata": {}, "hash": "e74a1265891ee8d762816969b16eddbf71bca204674791b34dd331f7d2172d09", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Style Guide\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Style Guide\n\n## Semantic Line Breaks\n\nIt is encouraged to use [Semantic Line Breaks](https://sembr.org/) when writing for the handbook.\nThis improves the readability of source files and make diffs clearer.\nThe Semantic Line Breaks [specification](https://sembr.org) explains the rules and reasoning.\nThe most important rules are,\n\n> - A semantic line break MUST occur after a sentence, as punctuated by a period (.), exclamation mark (!), or question mark (?).\n> - A semantic line break SHOULD occur after an independent clause as punctuated by a comma (,), semicolon (;), colon (:), or em dash (\u2014).\n> - A semantic line break MAY occur after a dependent clause in order to clarify grammatical structure or satisfy line length constraints.\n\n## Markdown\n\nWhenever possible, the handbook's prose should be written in Markdown rather than HTML.\nHowever, it is completely reasonable use HTML when it is needed.\nWhen it is advantageous, data should be [stored in data files and processed using shortcodes](https://gohugo.io/templates/data-templates/) rather than presented in raw Markdown.\n\n## Style Enforcement\n\nMarkdown styling is enforced using the [`markdownlint`](https://github.com/markdownlint/markdownlint) tool, using the style rules in {{% repo_link path=\".mdl_style.rb\" text=\"`.mdl_style.rb`\" %}}.\nAn explanation of the rules can be found [here](https://github.com/markdownlint/markdownlint/blob/master/docs/RULES.md).\n\nMarkdown linting is performed on all pull requests made to the handbook repository, as described in the [Continuous Integration]({{< relref \"/docs/contributing/contributing_changes.md#continuous-integration\" >}}) section.", "start_char_idx": 0, "end_char_idx": 2039, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b33cd473-ff1c-4fd5-9e29-3957daf45389": {"__data__": {"id_": "b33cd473-ff1c-4fd5-9e29-3957daf45389", "embedding": null, "metadata": {"file_path": "content/docs/contributing/style_guide.md", "file_name": "style_guide.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/style_guide.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8b19f56f4e3d0e16ff15d6f3bc378f03d292ccae", "node_type": "4", "metadata": {"file_path": "content/docs/contributing/style_guide.md", "file_name": "style_guide.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/style_guide.md"}, "hash": "c969046daeae1b4d7ed1ecb5132baba10ed90f952d1d0301a651cefbf1f4e2e0", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "e3729547-1eca-467b-9bb0-fe1804f9a874", "node_type": "1", "metadata": {"file_path": "content/docs/contributing/style_guide.md", "file_name": "style_guide.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributing/style_guide.md"}, "hash": "56fd089cad12727b641148e36dc21df6f09838f01978b279cc24038a32b4bf8d", "class_name": "RelatedNodeInfo"}}, "text": "## Markdown\n\nWhenever possible, the handbook's prose should be written in Markdown rather than HTML.\nHowever, it is completely reasonable use HTML when it is needed.\nWhen it is advantageous, data should be [stored in data files and processed using shortcodes](https://gohugo.io/templates/data-templates/) rather than presented in raw Markdown.\n\n## Style Enforcement\n\nMarkdown styling is enforced using the [`markdownlint`](https://github.com/markdownlint/markdownlint) tool, using the style rules in {{% repo_link path=\".mdl_style.rb\" text=\"`.mdl_style.rb`\" %}}.\nAn explanation of the rules can be found [here](https://github.com/markdownlint/markdownlint/blob/master/docs/RULES.md).\n\nMarkdown linting is performed on all pull requests made to the handbook repository, as described in the [Continuous Integration]({{< relref \"/docs/contributing/contributing_changes.md#continuous-integration\" >}}) section.\n\nIf you wish to lint locally, the repository contains a [pre-commit hook](https://pre-commit.com/) to do this, described in the [Making Changes]({{< relref \"/docs/contributing/contributing_changes.md#making-changes\" >}}) section.", "start_char_idx": 1133, "end_char_idx": 2269, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "142c2d96-ae16-4cf2-9256-dd7251e34c94": {"__data__": {"id_": "142c2d96-ae16-4cf2-9256-dd7251e34c94", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "4acae13b-0545-456d-b97b-aa278ff7ef96", "node_type": "1", "metadata": {}, "hash": "a4a808f3a38310cd9b254e4d544246b331aae89cc44b2e4069eec04769581d65", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Contributors\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: -1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Contributors\n\nIn this table we try to capture everyone who has contributed to the project and how they have contributed.\nThe table is generated and maintained using [All Contributors](https://allcontributors.org/).\n\n<!-- markdown-link-check-disable -->\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n  <tbody>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/andrewphilipsmith\"><img src=\"https://avatars.githubusercontent.com/u/5346065?v=4?s=100\" width=\"100px;\" alt=\"Andy Smith\"/><br /><sub><b>Andy Smith</b></sub></a><br /><a href=\"#content-andrewphilipsmith\" title=\"Content\">\ud83d\udd8b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Aandrewphilipsmith\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#ideas-andrewphilipsmith\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=andrewphilipsmith\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#design-andrewphilipsmith\" title=\"Design\">\ud83c\udfa8</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://christinalast.com/\"><img src=\"https://avatars.githubusercontent.", "start_char_idx": 0, "end_char_idx": 1655, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "4acae13b-0545-456d-b97b-aa278ff7ef96": {"__data__": {"id_": "4acae13b-0545-456d-b97b-aa278ff7ef96", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "142c2d96-ae16-4cf2-9256-dd7251e34c94", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "a4810f8bb047d4e5a33395165a51c65b22bc6f5fe594ccedf366895188da2168", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5ae511d6-d49d-4ddd-80eb-25edd5a56437", "node_type": "1", "metadata": {}, "hash": "d5dcf63d28ab4b7ac7c5c2dab00924b9ab3dcd193ca88ef2da5321aedc56fb1c", "class_name": "RelatedNodeInfo"}}, "text": "com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Aandrewphilipsmith\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#ideas-andrewphilipsmith\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=andrewphilipsmith\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#design-andrewphilipsmith\" title=\"Design\">\ud83c\udfa8</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://christinalast.com/\"><img src=\"https://avatars.githubusercontent.com/u/36204574?v=4?s=100\" width=\"100px;\" alt=\"Christina Last\"/><br /><sub><b>Christina Last</b></sub></a><br /><a href=\"#content-ChristinaLast\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/DavidBeavan\"><img src=\"https://avatars.githubusercontent.com/u/6524799?v=4?s=100\" width=\"100px;\" alt=\"David Beavan\"/><br /><sub><b>David Beavan</b></sub></a><br /><a href=\"#ideas-DavidBeavan\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3ADavidBeavan\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#content-DavidBeavan\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#maintenance-DavidBeavan\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.flypig.co.", "start_char_idx": 1141, "end_char_idx": 2487, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5ae511d6-d49d-4ddd-80eb-25edd5a56437": {"__data__": {"id_": "5ae511d6-d49d-4ddd-80eb-25edd5a56437", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "4acae13b-0545-456d-b97b-aa278ff7ef96", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "05031554728597e2f2ba4f6b3bd1883a2de4408c996d0a3fdec500f9566bf52f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "99d4167f-919f-4a15-bc13-70656440d6a8", "node_type": "1", "metadata": {}, "hash": "b9d8a280da303d7dfb0c27063807153456f492be351aa1a1c9a15b6c5e3d6ce8", "class_name": "RelatedNodeInfo"}}, "text": "githubusercontent.com/u/6524799?v=4?s=100\" width=\"100px;\" alt=\"David Beavan\"/><br /><sub><b>David Beavan</b></sub></a><br /><a href=\"#ideas-DavidBeavan\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3ADavidBeavan\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#content-DavidBeavan\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#maintenance-DavidBeavan\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://www.flypig.co.uk\"><img src=\"https://avatars.githubusercontent.com/u/1446122?v=4?s=100\" width=\"100px;\" alt=\"David Llewellyn-Jones\"/><br /><sub><b>David Llewellyn-Jones</b></sub></a><br /><a href=\"#content-llewelld\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-llewelld\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/issues?q=author%3Allewelld\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=llewelld\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Allewelld\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#maintenance-llewelld\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.", "start_char_idx": 1946, "end_char_idx": 3261, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "99d4167f-919f-4a15-bc13-70656440d6a8": {"__data__": {"id_": "99d4167f-919f-4a15-bc13-70656440d6a8", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5ae511d6-d49d-4ddd-80eb-25edd5a56437", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "28003bb3c26efedc1059b636d25a6fca48d8e0538a01d6b9575db064eb19a06c", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c9459fc0-9e0f-456d-8450-323acb47ce30", "node_type": "1", "metadata": {}, "hash": "8493cf1028101a62dcfc30b17d6dbdfb4bd2f5be02e688d09f868162a2546672", "class_name": "RelatedNodeInfo"}}, "text": "Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/issues?q=author%3Allewelld\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=llewelld\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Allewelld\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#maintenance-llewelld\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/dsj976\"><img src=\"https://avatars.githubusercontent.com/u/57944311?v=4?s=100\" width=\"100px;\" alt=\"David Salvador Jasin\"/><br /><sub><b>David Salvador Jasin</b></sub></a><br /><a href=\"#content-dsj976\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-dsj976\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Adsj976\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#maintenance-dsj976\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://edchalstrey.com/\"><img src=\"https://avatars.githubusercontent.com/u/5486164?v=4?s=100\" width=\"100px;\" alt=\"Ed Chalstrey\"/><br /><sub><b>Ed Chalstrey</b></sub></a><br /><a href=\"#ideas-edwardchalstrey1\" title=\"Ideas, Planning,", "start_char_idx": 2749, "end_char_idx": 4077, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c9459fc0-9e0f-456d-8450-323acb47ce30": {"__data__": {"id_": "c9459fc0-9e0f-456d-8450-323acb47ce30", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "99d4167f-919f-4a15-bc13-70656440d6a8", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "d1527add5fedd5f4dcde90e39eeec7f2907ea7b5e73300027cb560ac73daa31d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "eabb7bb4-3083-4b5f-9f00-68211f7e32b9", "node_type": "1", "metadata": {}, "hash": "d16d7681de80aac731d678a647dd03b0b33f4bf9b7927b2118b4dfdf550bb848", "class_name": "RelatedNodeInfo"}}, "text": "Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Adsj976\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#maintenance-dsj976\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://edchalstrey.com/\"><img src=\"https://avatars.githubusercontent.com/u/5486164?v=4?s=100\" width=\"100px;\" alt=\"Ed Chalstrey\"/><br /><sub><b>Ed Chalstrey</b></sub></a><br /><a href=\"#ideas-edwardchalstrey1\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-edwardchalstrey1\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/edchapman88\"><img src=\"https://avatars.githubusercontent.com/u/93717706?v=4?s=100\" width=\"100px;\" alt=\"Ed Chapman\"/><br /><sub><b>Ed Chapman</b></sub></a><br /><a href=\"#ideas-edchapman88\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-edchapman88\" title=\"Maintenance\">\ud83d\udea7</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/fedenanni\"><img src=\"https://avatars.githubusercontent.com/u/8415204?v=4?s=100\" width=\"100px;", "start_char_idx": 3555, "end_char_idx": 4738, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "eabb7bb4-3083-4b5f-9f00-68211f7e32b9": {"__data__": {"id_": "eabb7bb4-3083-4b5f-9f00-68211f7e32b9", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "c9459fc0-9e0f-456d-8450-323acb47ce30", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "4c5db75f43a0cff26d1754d7f9a2ff98138b10e7cc663e3d4198766958ebab42", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "9a202e7f-1c69-4845-aca0-5fd6aa403053", "node_type": "1", "metadata": {}, "hash": "f443b6d1ef8b0759e174a76465d625bffe0d83939aa0c807de5bde25c56a6213", "class_name": "RelatedNodeInfo"}}, "text": "28%\"><a href=\"https://github.com/edchapman88\"><img src=\"https://avatars.githubusercontent.com/u/93717706?v=4?s=100\" width=\"100px;\" alt=\"Ed Chapman\"/><br /><sub><b>Ed Chapman</b></sub></a><br /><a href=\"#ideas-edchapman88\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-edchapman88\" title=\"Maintenance\">\ud83d\udea7</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/fedenanni\"><img src=\"https://avatars.githubusercontent.com/u/8415204?v=4?s=100\" width=\"100px;\" alt=\"Federico Nanni\"/><br /><sub><b>Federico Nanni</b></sub></a><br /><a href=\"#content-fedenanni\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-fedenanni\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/ghanganuT\"><img src=\"https://avatars.githubusercontent.com/u/46718848?v=4?s=100\" width=\"100px;\" alt=\"Gabriel\"/><br /><sub><b>Gabriel</b></sub></a><br /><a href=\"#content-ghanganuT\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-ghanganuT\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/IFenton\"><img src=\"https://avatars.githubusercontent.com/u/5773962?v=4?s=100\" width=\"100px;", "start_char_idx": 4215, "end_char_idx": 5465, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9a202e7f-1c69-4845-aca0-5fd6aa403053": {"__data__": {"id_": "9a202e7f-1c69-4845-aca0-5fd6aa403053", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "eabb7bb4-3083-4b5f-9f00-68211f7e32b9", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "fd0e55428b88e90e237b8bec80cd1c046d59c6e2f7b6a3ca74363782110a86cf", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "53c234b1-1bef-41e7-8d0f-81e4a2e21a09", "node_type": "1", "metadata": {}, "hash": "5973e072d3dfe56a88a2c73ba93fabf8fb6da8651070fb37b175af4684274e01", "class_name": "RelatedNodeInfo"}}, "text": "& Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/ghanganuT\"><img src=\"https://avatars.githubusercontent.com/u/46718848?v=4?s=100\" width=\"100px;\" alt=\"Gabriel\"/><br /><sub><b>Gabriel</b></sub></a><br /><a href=\"#content-ghanganuT\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-ghanganuT\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/IFenton\"><img src=\"https://avatars.githubusercontent.com/u/5773962?v=4?s=100\" width=\"100px;\" alt=\"Isabel Fenton\"/><br /><sub><b>Isabel Fenton</b></sub></a><br /><a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3AIFenton\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#ideas-IFenton\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-IFenton\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jack89roberts\"><img src=\"https://avatars.githubusercontent.com/u/16308271?v=4?s=100\" width=\"100px;\" alt=\"Jack Roberts\"/><br /><sub><b>Jack Roberts</b></sub></a><br /><a href=\"#content-jack89roberts\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jemrobinson\"><img src=\"https://avatars.githubusercontent.", "start_char_idx": 4912, "end_char_idx": 6248, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "53c234b1-1bef-41e7-8d0f-81e4a2e21a09": {"__data__": {"id_": "53c234b1-1bef-41e7-8d0f-81e4a2e21a09", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "9a202e7f-1c69-4845-aca0-5fd6aa403053", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "5adfc814eb9cbd84af820ef44f151e10bcabb2e192fd40b4402e26d12e6d30e8", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5e4128d2-0246-49c5-97ae-50b5363f547d", "node_type": "1", "metadata": {}, "hash": "b07172d04a1486f898f5856da6f5b20d044309605abebc82be3b257758b7adcd", "class_name": "RelatedNodeInfo"}}, "text": "Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-IFenton\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jack89roberts\"><img src=\"https://avatars.githubusercontent.com/u/16308271?v=4?s=100\" width=\"100px;\" alt=\"Jack Roberts\"/><br /><sub><b>Jack Roberts</b></sub></a><br /><a href=\"#content-jack89roberts\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/jemrobinson\"><img src=\"https://avatars.githubusercontent.com/u/3502751?v=4?s=100\" width=\"100px;\" alt=\"James Robinson\"/><br /><sub><b>James Robinson</b></sub></a><br /><a href=\"#content-jemrobinson\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/JimMadge\"><img src=\"https://avatars.githubusercontent.com/u/23616154?v=4?s=100\" width=\"100px;\" alt=\"Jim Madge\"/><br /><sub><b>Jim Madge</b></sub></a><br /><a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=JimMadge\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#content-JimMadge\" title=\"Content\">\ud83d\udd8b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=JimMadge\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.", "start_char_idx": 5713, "end_char_idx": 6949, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5e4128d2-0246-49c5-97ae-50b5363f547d": {"__data__": {"id_": "5e4128d2-0246-49c5-97ae-50b5363f547d", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "53c234b1-1bef-41e7-8d0f-81e4a2e21a09", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "fa6d0669ad821c723e87d898bd17289214fb6106c296a31dae90f60bec154736", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2b3d28a7-49c8-4f38-92b2-5e2f22c35763", "node_type": "1", "metadata": {}, "hash": "a1cae9204a78eb4016a713f6f005983d17226fd244360d6483a84ce2231cd990", "class_name": "RelatedNodeInfo"}}, "text": "28%\"><a href=\"https://github.com/JimMadge\"><img src=\"https://avatars.githubusercontent.com/u/23616154?v=4?s=100\" width=\"100px;\" alt=\"Jim Madge\"/><br /><sub><b>Jim Madge</b></sub></a><br /><a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=JimMadge\" title=\"Code\">\ud83d\udcbb</a> <a href=\"#content-JimMadge\" title=\"Content\">\ud83d\udd8b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/commits?author=JimMadge\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3AJimMadge\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#ideas-JimMadge\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#infra-JimMadge\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/issues?q=author%3AJimMadge\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/yongrenjie\"><img src=\"https://avatars.githubusercontent.com/u/22414895?v=4?s=100\" width=\"100px;\" alt=\"Jonathan Yong\"/><br /><sub><b>Jonathan Yong</b></sub></a><br /><a href=\"#content-yongrenjie\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-yongrenjie\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-yongrenjie\" title=\"Maintenance\">\ud83d\udea7</a> <a href=\"https://github.", "start_char_idx": 6464, "end_char_idx": 7790, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2b3d28a7-49c8-4f38-92b2-5e2f22c35763": {"__data__": {"id_": "2b3d28a7-49c8-4f38-92b2-5e2f22c35763", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5e4128d2-0246-49c5-97ae-50b5363f547d", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "c7147410efc4ebc322e08d881a62a303867f4255ef64e986446b4111c50e97aa", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "d08eb6d7-705b-4369-ab85-17f47f9090b5", "node_type": "1", "metadata": {}, "hash": "5791d6d47a239120709c908dbf30b39de4a4335ccacaff40e77fa5a27ca39c7d", "class_name": "RelatedNodeInfo"}}, "text": "com/alan-turing-institute/REG-handbook/issues?q=author%3AJimMadge\" title=\"Bug reports\">\ud83d\udc1b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/yongrenjie\"><img src=\"https://avatars.githubusercontent.com/u/22414895?v=4?s=100\" width=\"100px;\" alt=\"Jonathan Yong\"/><br /><sub><b>Jonathan Yong</b></sub></a><br /><a href=\"#content-yongrenjie\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-yongrenjie\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-yongrenjie\" title=\"Maintenance\">\ud83d\udea7</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Ayongrenjie\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://katrionagoldmann.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/25952322?v=4?s=100\" width=\"100px;\" alt=\"Katriona Goldmann\"/><br /><sub><b>Katriona Goldmann</b></sub></a><br /><a href=\"#content-KatrionaGoldmann\" title=\"Content\">\ud83d\udd8b</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3AKatrionaGoldmann\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#ideas-KatrionaGoldmann\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-KatrionaGoldmann\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.", "start_char_idx": 7240, "end_char_idx": 8593, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d08eb6d7-705b-4369-ab85-17f47f9090b5": {"__data__": {"id_": "d08eb6d7-705b-4369-ab85-17f47f9090b5", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2b3d28a7-49c8-4f38-92b2-5e2f22c35763", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "705c59c78a6c695f34a873ba19812bb00de4fec43d8605cc29cc972ea6294640", "class_name": "RelatedNodeInfo"}, "3": {"node_id": 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Bokeria\"/><br /><sub><b>Levan Bokeria</b></sub></a><br /><a href=\"https://github.com/alan-turing-institute/REG-handbook/issues?q=author%3ALevanBokeria\" title=\"Bug reports\">\ud83d\udc1b</a> <a href=\"#content-LevanBokeria\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-LevanBokeria\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-LevanBokeria\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/LydiaFrance\"><img src=\"https://avatars.githubusercontent.com/u/85945427?v=4?s=100\" width=\"100px;", "start_char_idx": 8019, "end_char_idx": 9283, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ed3797f6-6391-46b7-8c12-723d805edd3f": {"__data__": {"id_": "ed3797f6-6391-46b7-8c12-723d805edd3f", "embedding": null, "metadata": {"file_path": 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France</b></sub></a><br /><a href=\"#content-LydiaFrance\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-LydiaFrance\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-LydiaFrance\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://malvikasharan.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/5370471?v=4?s=100\" width=\"100px;\" alt=\"Malvika Sharan\"/><br /><sub><b>Malvika Sharan</b></sub></a><br /><a href=\"#ideas-malvikasharan\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"http://mhauru.org\"><img src=\"https://avatars.githubusercontent.com/u/5229876?v=4?s=100\" width=\"100px;", "start_char_idx": 8722, "end_char_idx": 10031, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "4c2e522d-d8b6-4793-ba0f-38d61ef86ec4": {"__data__": {"id_": "4c2e522d-d8b6-4793-ba0f-38d61ef86ec4", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ed3797f6-6391-46b7-8c12-723d805edd3f", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": 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href=\"#content-mhauru\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-mhauru\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"https://github.com/alan-turing-institute/REG-handbook/pulls?q=is%3Apr+reviewed-by%3Amhauru\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#maintenance-mhauru\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/martintoreilly\"><img src=\"https://avatars.githubusercontent.com/u/21147592?v=4?s=100\" width=\"100px;\" alt=\"Martin O'Reilly\"/><br /><sub><b>Martin O'Reilly</b></sub></a><br /><a href=\"#ideas-martintoreilly\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/craddm\"><img src=\"https://avatars.githubusercontent.", "start_char_idx": 9589, "end_char_idx": 10878, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2ece4974-14ce-450b-be09-73076b3913e2": {"__data__": {"id_": "2ece4974-14ce-450b-be09-73076b3913e2", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "4c2e522d-d8b6-4793-ba0f-38d61ef86ec4", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "e33b8a39eec552adec89f90d27b2a400f3b7bff506f92e724e4b1378f570d0d5", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a1aee0ee-beab-4caf-b3f6-a6c22e8e9e0f", "node_type": "1", "metadata": {}, "hash": "b97125fd3bc0dd97c4c8ea8adf20babc1e1d26b1fc14bfd425762e7327aae9a7", "class_name": "RelatedNodeInfo"}}, "text": "28%\"><a href=\"https://github.com/martintoreilly\"><img src=\"https://avatars.githubusercontent.com/u/21147592?v=4?s=100\" width=\"100px;\" alt=\"Martin O'Reilly\"/><br /><sub><b>Martin O'Reilly</b></sub></a><br /><a href=\"#ideas-martintoreilly\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/craddm\"><img src=\"https://avatars.githubusercontent.com/u/5796417?v=4?s=100\" width=\"100px;\" alt=\"Matt Craddock\"/><br /><sub><b>Matt Craddock</b></sub></a><br /><a href=\"#ideas-craddm\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-craddm\" title=\"Maintenance\">\ud83d\udea7</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/phinate\"><img src=\"https://avatars.githubusercontent.com/u/49782545?v=4?s=100\" width=\"100px;\" alt=\"Nathan Simpson\"/><br /><sub><b>Nathan Simpson</b></sub></a><br /><a href=\"#ideas-phinate\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-phinate\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/OscartGiles\"><img src=\"https://avatars.githubusercontent.com/u/12784013?v=4?s=100\" width=\"100px;\" alt=\"Oscar T Giles\"/><br /><sub><b>Oscar T Giles</b></sub></a><br /><a href=\"#ideas-OscartGiles\" title=\"Ideas, Planning,", "start_char_idx": 10460, "end_char_idx": 11805, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a1aee0ee-beab-4caf-b3f6-a6c22e8e9e0f": {"__data__": {"id_": "a1aee0ee-beab-4caf-b3f6-a6c22e8e9e0f", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2ece4974-14ce-450b-be09-73076b3913e2", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "be160b3071dfeffe7b4f8295574e6968e11b4214f880900340d96389d7b746e5", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "77848f07-e6f1-4e28-b5e8-2a1e6f01a8a2", "node_type": "1", "metadata": {}, "hash": "2518cb5ee63e04c1ffa0fb3aaec0d387e1fc1bd315aa4b5aec69873dea65ef48", "class_name": "RelatedNodeInfo"}}, "text": "githubusercontent.com/u/49782545?v=4?s=100\" width=\"100px;\" alt=\"Nathan Simpson\"/><br /><sub><b>Nathan Simpson</b></sub></a><br /><a href=\"#ideas-phinate\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-phinate\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/OscartGiles\"><img src=\"https://avatars.githubusercontent.com/u/12784013?v=4?s=100\" width=\"100px;\" alt=\"Oscar T Giles\"/><br /><sub><b>Oscar T Giles</b></sub></a><br /><a href=\"#ideas-OscartGiles\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/radka-j\"><img src=\"https://avatars.githubusercontent.com/u/29207091?v=4?s=100\" width=\"100px;\" alt=\"Radka Jersakova\"/><br /><sub><b>Radka Jersakova</b></sub></a><br /><a href=\"#content-radka-j\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rolyp\"><img src=\"https://avatars.githubusercontent.com/u/121074?v=4?s=100\" width=\"100px;\" alt=\"Roly Perera\"/><br /><sub><b>Roly Perera</b></sub></a><br /><a href=\"#ideas-rolyp\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rwood-97\"><img src=\"https://avatars.githubusercontent.", "start_char_idx": 11248, "end_char_idx": 12570, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "77848f07-e6f1-4e28-b5e8-2a1e6f01a8a2": {"__data__": {"id_": "77848f07-e6f1-4e28-b5e8-2a1e6f01a8a2", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a1aee0ee-beab-4caf-b3f6-a6c22e8e9e0f", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "a5fa1855b7e4be5dd3ac977709c5d51417ea379b40846dcdb71f5b6a31b6a572", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "52f57058-d56a-4d99-b021-e100acbf7d99", "node_type": "1", "metadata": {}, "hash": "90548f4f52aef76dbc7eaa4474e2aff3b74c5e5d7ab0ea6a697245c93d5c8561", "class_name": "RelatedNodeInfo"}}, "text": "\" alt=\"Radka Jersakova\"/><br /><sub><b>Radka Jersakova</b></sub></a><br /><a href=\"#content-radka-j\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rolyp\"><img src=\"https://avatars.githubusercontent.com/u/121074?v=4?s=100\" width=\"100px;\" alt=\"Roly Perera\"/><br /><sub><b>Roly Perera</b></sub></a><br /><a href=\"#ideas-rolyp\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/rwood-97\"><img src=\"https://avatars.githubusercontent.com/u/72076688?v=4?s=100\" width=\"100px;\" alt=\"Rosie Wood\"/><br /><sub><b>Rosie Wood</b></sub></a><br /><a href=\"#ideas-rwood-97\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-rwood-97\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://rchan26.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/44200705?v=4?s=100\" width=\"100px;\" alt=\"Ryan Chan\"/><br /><sub><b>Ryan Chan</b></sub></a><br /><a href=\"#content-rchan26\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/thobson88\"><img src=\"https://avatars.githubusercontent.com/u/26117394?v=4?s=100\" width=\"100px;", "start_char_idx": 12002, "end_char_idx": 13268, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "52f57058-d56a-4d99-b021-e100acbf7d99": {"__data__": {"id_": "52f57058-d56a-4d99-b021-e100acbf7d99", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "77848f07-e6f1-4e28-b5e8-2a1e6f01a8a2", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "abfa4263dc7162f86267a4e3633c00ca4c1b814048a159830d96cb1317e3d82c", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ec0b6f9e-fa47-4006-942f-74d62306946c", "node_type": "1", "metadata": {}, "hash": "b34fcf101a0ed05f057b74e498a3c2eef0ef6a117106604722c0fce8f36fe563", "class_name": "RelatedNodeInfo"}}, "text": "Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-rwood-97\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://rchan26.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/44200705?v=4?s=100\" width=\"100px;\" alt=\"Ryan Chan\"/><br /><sub><b>Ryan Chan</b></sub></a><br /><a href=\"#content-rchan26\" title=\"Content\">\ud83d\udd8b</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/thobson88\"><img src=\"https://avatars.githubusercontent.com/u/26117394?v=4?s=100\" width=\"100px;\" alt=\"Tim Hobson\"/><br /><sub><b>Tim Hobson</b></sub></a><br /><a href=\"#content-thobson88\" title=\"Content\">\ud83d\udd8b</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/griff-rees\"><img src=\"https://avatars.githubusercontent.com/u/60181741?v=4?s=100\" width=\"100px;\" alt=\"griff-rees\"/><br /><sub><b>griff-rees</b></sub></a><br /><a href=\"#ideas-griff-rees\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/helenashowell\"><img src=\"https://avatars.githubusercontent.com/u/101103393?v=4?s=100\" width=\"100px;", "start_char_idx": 12713, "end_char_idx": 13902, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ec0b6f9e-fa47-4006-942f-74d62306946c": {"__data__": {"id_": "ec0b6f9e-fa47-4006-942f-74d62306946c", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "52f57058-d56a-4d99-b021-e100acbf7d99", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "892f34208a4d67b03a1fe0d9a27fd8330b605ce6624c46cf25dfef25c1570d16", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2f45fac7-da65-4dbc-975c-4c0385a61c5a", "node_type": "1", "metadata": {}, "hash": "f68833df7f71553b46eed7dcc9e8bfa0ea6a6aba28c178ee4a3f4f8994a23995", "class_name": "RelatedNodeInfo"}}, "text": "28%\"><a href=\"https://github.com/griff-rees\"><img src=\"https://avatars.githubusercontent.com/u/60181741?v=4?s=100\" width=\"100px;\" alt=\"griff-rees\"/><br /><sub><b>griff-rees</b></sub></a><br /><a href=\"#ideas-griff-rees\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/helenashowell\"><img src=\"https://avatars.githubusercontent.com/u/101103393?v=4?s=100\" width=\"100px;\" alt=\"helenashowell\"/><br /><sub><b>helenashowell</b></sub></a><br /><a href=\"#content-helenashowell\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-helenashowell\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/mastoffel\"><img src=\"https://avatars.githubusercontent.com/u/7348440?v=4?s=100\" width=\"100px;\" alt=\"martin\"/><br /><sub><b>martin</b></sub></a><br /><a href=\"#content-mastoffel\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-mastoffel\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-mastoffel\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/nbarlowATI\"><img src=\"https://avatars.githubusercontent.com/u/33832774?v=4?s=100\" width=\"100px;", "start_char_idx": 13455, "end_char_idx": 14695, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2f45fac7-da65-4dbc-975c-4c0385a61c5a": {"__data__": {"id_": "2f45fac7-da65-4dbc-975c-4c0385a61c5a", "embedding": null, "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "53f966366a793fd602bd41df877eb4dff3ffce6a", "node_type": "4", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "b8da954bc51018d35f98c4256c2940b4aa89b10081ca56d7af0c6f5fffbaa686", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ec0b6f9e-fa47-4006-942f-74d62306946c", "node_type": "1", "metadata": {"file_path": "content/docs/contributors/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/contributors/_index.md"}, "hash": "c19d42333889b13fda52555ea351d80dad649d0b4e6d0b459ad6bceaccad9ba8", "class_name": "RelatedNodeInfo"}}, "text": "28%\"><a href=\"https://github.com/mastoffel\"><img src=\"https://avatars.githubusercontent.com/u/7348440?v=4?s=100\" width=\"100px;\" alt=\"martin\"/><br /><sub><b>martin</b></sub></a><br /><a href=\"#content-mastoffel\" title=\"Content\">\ud83d\udd8b</a> <a href=\"#ideas-mastoffel\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#maintenance-mastoffel\" title=\"Maintenance\">\ud83d\udea7</a></td>\n      <td align=\"center\" valign=\"top\" width=\"14.28%\"><a href=\"https://github.com/nbarlowATI\"><img src=\"https://avatars.githubusercontent.com/u/33832774?v=4?s=100\" width=\"100px;\" alt=\"nbarlowATI\"/><br /><sub><b>nbarlowATI</b></sub></a><br /><a href=\"#ideas-nbarlowATI\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a></td>\n    </tr>\n  </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n<!-- markdown-link-check-enable -->", "start_char_idx": 14153, "end_char_idx": 14995, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3feceeca-3eb9-43ae-95e8-61c9cf5c6d57": {"__data__": {"id_": "3feceeca-3eb9-43ae-95e8-61c9cf5c6d57", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7400df9edd9c4071fcbab15b4862891105a2e1bd", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/_index.md"}, "hash": "7e7e678ec393e56f637fb49919db980f27aa94f5bd88b00aec3fcb66c71a0371", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Employee processes\"\nweight: 3\n---\n\n# Employee processes", "start_char_idx": 0, "end_char_idx": 67, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "84900df3-9ac3-4d8b-a692-cfb617fe1e84": {"__data__": {"id_": "84900df3-9ac3-4d8b-a692-cfb617fe1e84", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/annual_appraisals.md", "file_name": "annual_appraisals.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/annual_appraisals.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "63768c9127a41b6c46ab497d2341cfbcde106d65", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/annual_appraisals.md", "file_name": "annual_appraisals.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/annual_appraisals.md"}, "hash": "a720175548113be9c9d4f49eb51f3c675cedb847cb2e8491c9e77afceb91f0d8", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Annual Appraisals\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Annual Appraisals\n\nREG is a large team, with a relatively flat line management structure. Therefore we run a team-wide standardisation process where appraisals and progression award recommendations are reviewed to ensure consistency and fairness across the team.\n\nOur goal is to work within the wider HR policy and guidance (see [HR Performance Review Guide](https://mathison.turing.ac.uk/page/2319)) to prioritise what we think is most important for our team (fairness, transparency and a clear progression path) and provide clear guidance to achieve that.\n\n## Principles\n\n1. REG members who are operating at an equivalent level should receive equivalent pay.\n1. We are a learning team and our default assumption is that people will generally be growing and developing in their role as they gain more experience.\n1. Everyone who is performing and developing as expected for their role and their experience level within it should expect to progress through their pay band each year on top of the cost of living adjustment. This is very much in line with the guidance on salary increases from HR.\n\n## Useful links\n\n- [Annual appraisal process for those on probation]({{< relref \"/docs/employee_processes/probation.md#annual-appraisal\" >}})\n- [REG Appraisal Guide](https://github.com/alan-turing-institute/research-engineering-group/wiki/Annual-appraisals)\n- [Annual Pay Increases](https://github.com/alan-turing-institute/research-engineering-group/wiki/Annual-pay-increases)\n- [HR Performance Review Guide](https://mathison.turing.ac.uk/page/2319)", "start_char_idx": 0, "end_char_idx": 1946, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3603f85b-d8fd-4081-8258-ca892ef85005": {"__data__": {"id_": "3603f85b-d8fd-4081-8258-ca892ef85005", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "60b8c530-ba1a-41bc-9d6b-3991791bc2b6", "node_type": "1", "metadata": {}, "hash": "49b9e4f55361e89ef5d30ef61cb5226d4c6d04475785a537c713ec5c8316adc7", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Probation\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n---\n\n# Probation\n\nDuring the initial 6 months in a team member's appointment to the institute, they will undergo probation review with their line manager. (Note that if an existing team member was previously in another role at the Turing and underwent probation review in that role, they do not require a probation review when appointed to a role in REG.) This is designed to integrate with the on-boarding process to ensure that new team members are performing their job as expected. This wiki page outlines the expectations for team members and line managers in going through the probation process.\n\nTuring-wide information on probation can be found on Mathison:\n\n- [Probation Policy](https://mathison.turing.ac.uk/Utilities/Uploads/Handler/Uploader.ashx?area=composer&filename=Probation+Policy+HRPOL017+V1.0.pdf&fileguid=070ff12d-845c-400d-9826-bac27fa5b3ad)\n- [Probation FAQs](https://mathison.turing.ac.uk/Utilities/Uploads/Handler/Uploader.ashx?area=composer&filename=Probation+Period+Training+FAQs.pdf&fileguid=bb062c31-018f-4eee-8fc3-6e74c56addb8)\n\n## Information for team members on probation\n\nProbation in REG is a lightweight review process that will ensure you are settling into the role and performing as expected. Turing policy is that such reviews should occur with meetings at 3 weeks, 3 months, and 5 months from the team member's start date, with a final recommendation at 6 months:\n\n- **3 weeks:** Create a set of initial probation objectives once the team member has settled in. Note that you might not have a clear project or projects to work on at this point, so it is fine to refine any objectives set now at the subsequent reviews. Your manager can help you set appropriate objectives.\n- **3 months:** Initial review of progress towards your probation objectives.", "start_char_idx": 0, "end_char_idx": 1937, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "60b8c530-ba1a-41bc-9d6b-3991791bc2b6": {"__data__": {"id_": "60b8c530-ba1a-41bc-9d6b-3991791bc2b6", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "3603f85b-d8fd-4081-8258-ca892ef85005", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "9b3d243e26711749e21772d284c0c3e5a950eecc888c23b49c868ded999761cb", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "22d5c08c-b3b9-4ae9-86cd-af3e8edb4af1", "node_type": "1", "metadata": {}, "hash": "67f6f99c4179dfaf21b519c0e5a8c3915a775ef7823179dfcdd40ce7847a292e", "class_name": "RelatedNodeInfo"}}, "text": "Turing policy is that such reviews should occur with meetings at 3 weeks, 3 months, and 5 months from the team member's start date, with a final recommendation at 6 months:\n\n- **3 weeks:** Create a set of initial probation objectives once the team member has settled in. Note that you might not have a clear project or projects to work on at this point, so it is fine to refine any objectives set now at the subsequent reviews. Your manager can help you set appropriate objectives.\n- **3 months:** Initial review of progress towards your probation objectives. Your line manager will solicit feedback from people you have worked with to ensure you are settling into the team. You may need to revise your objectives to adjust for changes in the projects you have been working on, which is very normal.\n- **5 months:** Final review prior to end of probation. The timing of this review is designed to give one last chance for actionable feedback to someone that is underperforming. In cases where someone is underperforming the most likely action to be taken at this time is to extend probation for an additional 3 months.\n- **6 months:** Recommendation is made to pass probation, or to extend if the team member would benefit from additional time to settle into the role.\n\nUpon passing probation, the REG Leadership Team (Director and the Principals) will review your salary to ensure you are fairly placed relative to your peers. Juniors will not undergo pay review, as we have a separate process for development and promotion for Juniors, summarised below. See the wiki page on [[Annual-pay-increases]] for more information on the REG philosophy and principles for pay and progression, and additional information specific to the end of probation review below.\n\n## Additional Probation Information for Juniors\n\nBecause the Junior role is slightly different from REG roles at Standard and above, Juniors have their growth and development monitored more closely. Juniors are assessed at the end of probation as all members of the team following the description above, but have additional promotion reviews at roughly 6 month intervals to monitor their growth and development.", "start_char_idx": 1378, "end_char_idx": 3549, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "22d5c08c-b3b9-4ae9-86cd-af3e8edb4af1": {"__data__": {"id_": "22d5c08c-b3b9-4ae9-86cd-af3e8edb4af1", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "60b8c530-ba1a-41bc-9d6b-3991791bc2b6", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "800580b30fce1e45af922764d400184f6a0f72427e4841f3f73c43c9a72ce4e2", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "7e725ce8-52cb-49c5-8e0a-bd6d4c21728d", "node_type": "1", "metadata": {}, "hash": "d7dbae8dc4897c01a3b9e4ab0b0638e438f4bb3f1f6f0d8c2c0940a544b94b3c", "class_name": "RelatedNodeInfo"}}, "text": "Upon passing probation, the REG Leadership Team (Director and the Principals) will review your salary to ensure you are fairly placed relative to your peers. Juniors will not undergo pay review, as we have a separate process for development and promotion for Juniors, summarised below. See the wiki page on [[Annual-pay-increases]] for more information on the REG philosophy and principles for pay and progression, and additional information specific to the end of probation review below.\n\n## Additional Probation Information for Juniors\n\nBecause the Junior role is slightly different from REG roles at Standard and above, Juniors have their growth and development monitored more closely. Juniors are assessed at the end of probation as all members of the team following the description above, but have additional promotion reviews at roughly 6 month intervals to monitor their growth and development. The first promotion review occurs after the Junior has been on the team for roughly one year, and it is at this time that the Junior's pay will be adjusted if they are growing and developing as expected (either through promotion to standard, or giving a development pay increase if they are growing in the role but not ready for promotion to Standard). This ensures that Juniors have their pay reviewed as frequently as all team members and are promoted to Standard as soon as they are ready.\n\n## Probation information for line managers\n\nAs a manager, you are expected to help guide your report through their initial project work (or other team activities if they are not assigned to a project) and ensure their probation objectives are appropriate for their role. You should be gathering regular feedback from the team member's collaborators (ideally someone more senior than them) and passing on information on their work and giving any constructive feedback. If all is going well, a quick message to the reviewers every few weeks to confirm that all is okay should be fine, and you should pass this information on in your regular 1/1s with the team member.\n\nAt 3 months, you will hold a mid-term probation review, which should simply be a slightly more formal version of the ongoing feedback on their performance.", "start_char_idx": 2648, "end_char_idx": 4866, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "7e725ce8-52cb-49c5-8e0a-bd6d4c21728d": {"__data__": {"id_": "7e725ce8-52cb-49c5-8e0a-bd6d4c21728d", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "22d5c08c-b3b9-4ae9-86cd-af3e8edb4af1", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "54a10956a8e3cece788f982c54a10b5aa7ac58e2a39a0a9c6ed257d1a1ad4f8a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5e5445b6-8ec1-4ab0-ace9-d2f41365a640", "node_type": "1", "metadata": {}, "hash": "b38732b319fc4877b821bd92f0e8c1b5a930f9410a2a3005d78eeb97a6662eee", "class_name": "RelatedNodeInfo"}}, "text": "This ensures that Juniors have their pay reviewed as frequently as all team members and are promoted to Standard as soon as they are ready.\n\n## Probation information for line managers\n\nAs a manager, you are expected to help guide your report through their initial project work (or other team activities if they are not assigned to a project) and ensure their probation objectives are appropriate for their role. You should be gathering regular feedback from the team member's collaborators (ideally someone more senior than them) and passing on information on their work and giving any constructive feedback. If all is going well, a quick message to the reviewers every few weeks to confirm that all is okay should be fine, and you should pass this information on in your regular 1/1s with the team member.\n\nAt 3 months, you will hold a mid-term probation review, which should simply be a slightly more formal version of the ongoing feedback on their performance. If the team member did not have a project initially, then this is a good opportunity to revisit the initial objectives and ensure they reflect the work expected of the team member. This is also a good opportunity to ensure they are participating in team events like Tech Talks, integrating socially with the team (particularly if they are not regularly working in the office), and are contributing to a Service Area.\n\nAt 5 months, you will hold a second review. This review is mostly to ensure that any performance issues are communicated well in advance of the 6 month deadline (see below), but should be similar to the 3 month review.\n\nIf you do hear any concerns about the team member's performance, it is *very* important to follow up on this promptly. Because REG is a learning team, people are frequently working in unfamiliar territory and it can be difficult to recognise that they can ask for help when they are struggling and it is not a reflection on their performance. The team member could also be facing a difficult situation outside of work -- if they are not comfortable sharing their situation with you, then refer them to the [Turing's wellbeing support services](https://mathison.turing.ac.uk/page/2228), HR or the [Turing's External Supervisor](https://mathison.turing.ac.uk/page/3052).", "start_char_idx": 3903, "end_char_idx": 6173, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5e5445b6-8ec1-4ab0-ace9-d2f41365a640": {"__data__": {"id_": "5e5445b6-8ec1-4ab0-ace9-d2f41365a640", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "7e725ce8-52cb-49c5-8e0a-bd6d4c21728d", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "9380da57be54877302272a517321fb3f13fa6784c40764d209ffb66f46d443fb", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "06acc6e2-25e8-4bf3-9fae-e254fa4a09da", "node_type": "1", "metadata": {}, "hash": "982820943a386d6c56066e9e8a347a45eb7ad87f762c0cb84aa3cfc595eeeb77", "class_name": "RelatedNodeInfo"}}, "text": "At 5 months, you will hold a second review. This review is mostly to ensure that any performance issues are communicated well in advance of the 6 month deadline (see below), but should be similar to the 3 month review.\n\nIf you do hear any concerns about the team member's performance, it is *very* important to follow up on this promptly. Because REG is a learning team, people are frequently working in unfamiliar territory and it can be difficult to recognise that they can ask for help when they are struggling and it is not a reflection on their performance. The team member could also be facing a difficult situation outside of work -- if they are not comfortable sharing their situation with you, then refer them to the [Turing's wellbeing support services](https://mathison.turing.ac.uk/page/2228), HR or the [Turing's External Supervisor](https://mathison.turing.ac.uk/page/3052). If there are problems with the team member's performance and the manager is not comfortable giving this feedback to the team member, then it is best to ask for help from the Principal into whom you report. If there are still problems with the team member's performance at the 5 month review, then you should speak to the Principal in your reporting line.\n\n## Post-probation salary review points\n\n### End of probation review\n\nAt the end of probation, REG will conduct a salary review of all Standard and above team members passing probation. Junior staff will have their salary reviewed 12 months after appointment as part of their first promotion review (see additional information above).\n\nThis is purely an opportunity to identify any rare cases where we have clearly and significantly mis-levelled a new starter on initial appointment to the team. Changes to salary will only be made in exceptional circumstances to adjust the team member's placement in the band to correct a significant misalignment and where it would not be fair to wait to the next annual appraisal to address this.", "start_char_idx": 5285, "end_char_idx": 7262, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "06acc6e2-25e8-4bf3-9fae-e254fa4a09da": {"__data__": {"id_": "06acc6e2-25e8-4bf3-9fae-e254fa4a09da", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5e5445b6-8ec1-4ab0-ace9-d2f41365a640", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "5018807062a23afb42251134f429a132d4ca438777806868252f7523d84f4ca6", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "35af2b14-e6e5-48fd-9926-2ad98a0ee79a", "node_type": "1", "metadata": {}, "hash": "4601f692d45a5153b0d5d7abf06a84bbe6c1ed2a3485288b466df758640fc5a4", "class_name": "RelatedNodeInfo"}}, "text": "If there are still problems with the team member's performance at the 5 month review, then you should speak to the Principal in your reporting line.\n\n## Post-probation salary review points\n\n### End of probation review\n\nAt the end of probation, REG will conduct a salary review of all Standard and above team members passing probation. Junior staff will have their salary reviewed 12 months after appointment as part of their first promotion review (see additional information above).\n\nThis is purely an opportunity to identify any rare cases where we have clearly and significantly mis-levelled a new starter on initial appointment to the team. Changes to salary will only be made in exceptional circumstances to adjust the team member's placement in the band to correct a significant misalignment and where it would not be fair to wait to the next annual appraisal to address this.\n\nAll line managers should evaluate whether they feel each of their reports completing probation was appointed at the right 1/3 of the salary band for their role and complete the [probation completion form](https://github.com/alan-turing-institute/Hut23/tree/master/development/probation-completion-form.md) and send to the Principal in their reporting line and the REG Director.\n\nAs with progression award recommendations made as part of the annual appraisal process, the final decision on whether a salary adjustment will be made lies with the REG Director, subject to approval by Institute Senior Management.\n\n### Annual appraisal\n\nFrom the 2023-24 appraisal year, all REG staff who have completed probation on or before 31 May will be eligible for a progression award as part of the annual appraisal process, with these awards pro-rated for those in-post less than 12 months.\n\nAll employees who complete probation on or before 31 January will go through the standard appraisal process on the HR system (Cezanne). Those completing probation between 01 February and 31 May will not be included in the formal appraisal process on the HR system but, for REG, line managers will perform a similar appraisal of performance as for those staff who are going through the formal appraisal process and will make a recommendation for a progression award on the same basis as for those going through the formal appraisal process.", "start_char_idx": 6380, "end_char_idx": 8682, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "35af2b14-e6e5-48fd-9926-2ad98a0ee79a": {"__data__": {"id_": "35af2b14-e6e5-48fd-9926-2ad98a0ee79a", "embedding": null, "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "028550c18bfc12b1ad2eab4e0ad96f36fb520d9d", "node_type": "4", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "e3f8822b8dae8cfbb35d7df9be8b327cc85fdd8b9ba7d9b80c5f0f2c8c1cdfa5", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "06acc6e2-25e8-4bf3-9fae-e254fa4a09da", "node_type": "1", "metadata": {"file_path": "content/docs/employee_processes/probation.md", "file_name": "probation.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/employee_processes/probation.md"}, "hash": "36662840003334fa4f91c129deab3a495d4add6e4bddfd9442818bca0dd1616d", "class_name": "RelatedNodeInfo"}}, "text": "As with progression award recommendations made as part of the annual appraisal process, the final decision on whether a salary adjustment will be made lies with the REG Director, subject to approval by Institute Senior Management.\n\n### Annual appraisal\n\nFrom the 2023-24 appraisal year, all REG staff who have completed probation on or before 31 May will be eligible for a progression award as part of the annual appraisal process, with these awards pro-rated for those in-post less than 12 months.\n\nAll employees who complete probation on or before 31 January will go through the standard appraisal process on the HR system (Cezanne). Those completing probation between 01 February and 31 May will not be included in the formal appraisal process on the HR system but, for REG, line managers will perform a similar appraisal of performance as for those staff who are going through the formal appraisal process and will make a recommendation for a progression award on the same basis as for those going through the formal appraisal process. Whether undergoing the formal or informal appraisal process, **progression awards will be pro-rated for staff with less than 12 months of service**. See the [Performance Review Guide](https://mathison.turing.ac.uk/page/2319) for more information.\n\nSee the [REG Appraisal Guide](https://github.com/alan-turing-institute/research-engineering-group/wiki/Annual-appraisals) for details of how the appraisal process (formal or informal) is run in REG. Note that progression awards made as part of the annual appraisal process are separate to the universal unconditional cost of living increase awarded to all employees, regardless of probation status, each April. See the [annual pay increases](https://github.com/alan-turing-institute/research-engineering-group/wiki/Annual-pay-increases) page for details of cost of living and progression awards from previous years.", "start_char_idx": 7643, "end_char_idx": 9546, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2dbabf73-3621-4277-810f-def259b60568": {"__data__": {"id_": "2dbabf73-3621-4277-810f-def259b60568", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "355608b3e4baabc116162490b302ac19818f0c72", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/_index.md"}, "hash": "e37b231396950826b365c3d13dabf9d8bfb1b392863a1e81ea0d59ea12e2a045", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"How we work\"\nweight: 4\n---\n\n# How We Work", "start_char_idx": 0, "end_char_idx": 53, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "308301a6-2983-480c-b771-15de12d77e0f": {"__data__": {"id_": "308301a6-2983-480c-b771-15de12d77e0f", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/acronyms_and_abbreviations.md", "file_name": "acronyms_and_abbreviations.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/acronyms_and_abbreviations.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "0dca6c86fd4b2ddcac032fa90a740f2c2f254e34", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/acronyms_and_abbreviations.md", "file_name": "acronyms_and_abbreviations.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/acronyms_and_abbreviations.md"}, "hash": "236a182283f6d593c2def574ae32904422725915e92a250444b290ac4fe77165", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "9f03394b-9689-4c1e-a008-2f40d8c0ee0e", "node_type": "1", "metadata": {}, "hash": "8b73b1fea2bddfd5d296080db0e626292c290b1f73c340cb5af31a23a1b0a455", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Acronyms and Abbreviations\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 100\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Acronyms and Abbreviations\n\nAIDA: Artificial Intelligence for Data Analytics\n\nALOGIT: name of a Fortran package (see [issue](https://github.com/alan-turing-institute/Hut23/issues/295))\n\nASG: AI for Science and Government (the new name of the EPSRC funds from SPF)\n\nARC: Applied Research Centre / Australian Research Council\n\nBL: British Library\n\nBPT: Bayesian Performance Tuning\n\nChrono: Chronotopic Cartographies\n\nCPD: Change-point detection (method to apply to data in AIDA-Lloyds project)\n\nCT: Computerised Tomography\n\nDAS: Data Availability Statements (Giovanni's 22-days research paper)\n\nDAFNI: [Data & Analytics Facility for National Infrastructure](https://www.dafni.ac.uk/)\n\nDC: Detector-Checker\n\nD&S: Defence and Security (Programme)\n\nDS&DH: Data Science & Digital Humanities (Special Interest Group)\n\nDSB: Data Science Benchmarking\n\nDS for SD: Data Science for Sustainable Development\n\nDSG: Data Study Group\n\nDSSG: Data Science for Social Good\n\nDSSH: Data Science for Science and the Humanities (Programme)\n\nEDS: Economic Data Science\n\nEOM: End of message\n\nFTE: Full Time Equivalent\n\nGUARD: Global Urban Analytics for Resilient Defence\n\nHPC: High Performance Computing\n\nIAG: International Airlines Group (British Airways merged with Iberia to form IAG)\n\nKF: Knowledge Flows\n\nLwM: Living with Machines\n\nNATS: National Air Traffic Services\n\nOpenMP: [Open Multi-Processing](https://en.wikipedia.", "start_char_idx": 0, "end_char_idx": 1812, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9f03394b-9689-4c1e-a008-2f40d8c0ee0e": {"__data__": {"id_": "9f03394b-9689-4c1e-a008-2f40d8c0ee0e", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/acronyms_and_abbreviations.md", "file_name": "acronyms_and_abbreviations.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/acronyms_and_abbreviations.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "0dca6c86fd4b2ddcac032fa90a740f2c2f254e34", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/acronyms_and_abbreviations.md", "file_name": "acronyms_and_abbreviations.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/acronyms_and_abbreviations.md"}, "hash": "236a182283f6d593c2def574ae32904422725915e92a250444b290ac4fe77165", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "308301a6-2983-480c-b771-15de12d77e0f", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/acronyms_and_abbreviations.md", "file_name": "acronyms_and_abbreviations.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/acronyms_and_abbreviations.md"}, "hash": "ca94bf7a976316fdcb7f13c8b4f17a2720ae635bd4e3b7adc86e2bd26578c961", "class_name": "RelatedNodeInfo"}}, "text": "dafni.ac.uk/)\n\nDC: Detector-Checker\n\nD&S: Defence and Security (Programme)\n\nDS&DH: Data Science & Digital Humanities (Special Interest Group)\n\nDSB: Data Science Benchmarking\n\nDS for SD: Data Science for Sustainable Development\n\nDSG: Data Study Group\n\nDSSG: Data Science for Social Good\n\nDSSH: Data Science for Science and the Humanities (Programme)\n\nEDS: Economic Data Science\n\nEOM: End of message\n\nFTE: Full Time Equivalent\n\nGUARD: Global Urban Analytics for Resilient Defence\n\nHPC: High Performance Computing\n\nIAG: International Airlines Group (British Airways merged with Iberia to form IAG)\n\nKF: Knowledge Flows\n\nLwM: Living with Machines\n\nNATS: National Air Traffic Services\n\nOpenMP: [Open Multi-Processing](https://en.wikipedia.org/wiki/OpenMP)\n\nPDQ:  Proof-Driven Query\n\nPFEA: Probabilistic Finite Element Analysis\n\nRCP: Research Computing Platforms\n\nRCWG: Research Computing Working Group\n\nRDA: Research Data Alliance\n\nRDS: Research Data Scientist\n\nREG: Research Engineering Group\n\nRepRes: Reproducible Research\n\nRSD: Research Software Development\n\nRSE: Research Software Engineer(ing)\n\nSHEEP: SHEEP is a Homomorphic Encryption Evaluation Platform\n\nSPF: [Strategic Priorities Fund](https://www.turing.ac.uk/research/research-programmes/artificial-intelligence-ai/programme-articles/alan-turing-institute-spearhead-new-cutting-edge-data-science-and-ai-research-after-ps48-million) (see also ASG)\n\nTDA: Topological Data Analysis\n\nTPS: Tools, Practices, and Systems\n\nTMF: Toyota Mobility Foundation\n\nTNA: The National Archives\n\nTTW: The Turing Way\n\nUQBB: Uncertainty Quantification for Black Box Computational Models\n\nUQM^3: Uncertainty Quantification in Multi-scale, Multi-physics Models\n\nWFH: Working from home", "start_char_idx": 1078, "end_char_idx": 2795, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "fce3078e-c270-459f-a637-5009e3c264e1": {"__data__": {"id_": "fce3078e-c270-459f-a637-5009e3c264e1", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "aac8d6e30090f116e25aadfb09076acb7c3a5346", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "hash": "07587a402538d64924ecc1b11fd07da62a6713ac489bc76a60f03654226c844a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "0efe9dfa-f503-40c3-b065-20103e991baf", "node_type": "1", "metadata": {}, "hash": "a2d3e37bdd3220f7322e4092b9655697e10cb528d5640fe302dd0ba964a3fda0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Repo Audits\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 2\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Repo Audits\n\nAs required we conduct audits of GitHub repositories created during projects.\nThe aim is to check that the documentation is clear, installation is easy etc.\n\nAudits should be conducted by people who have not worked on the project.\n\n## Past experience\n\nThese comments from running the first round of these in June 2021:\n\n> Installation is a big barrier for software with complex build requirements.\n> We tried looking at two web app projects (Counterfactual Covid 19 and CROP) and despite Docker images being available we weren't able to get them to build correctly.\n> We might have had more success if all we needed to do was a pip or CRAN install.\n>\n> - First lesson: installation is ALWAYS harder than you think, and we need to get more fresh eyes on build instructions if we want our software to be easily usable.\n> - Second Lesson: Not having someone on hand from the project can be a big barrier, as we suspect there may have just been some small tweaks or changes that would fix our problem.\n> - One suggestion was to have a mix of unsupervised hacking and more knowledgeable feedback, maybe by having the person working on the project show up an hour in. If we can't get someone who knows the project well to attend, at least have them available on Slack in case we get stuck.\n> - At the very least, we were able to document our issues on the repo, and in at least one case the project was able to make fixes.\n>\n> One suggestion is to encourage \"Good First Issue\" tags or \"Hacktoberfest\"-type tags that would help people know what simple things could be tackled in a few hours if they succeed in installing the software.", "start_char_idx": 0, "end_char_idx": 1984, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0efe9dfa-f503-40c3-b065-20103e991baf": {"__data__": {"id_": "0efe9dfa-f503-40c3-b065-20103e991baf", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "aac8d6e30090f116e25aadfb09076acb7c3a5346", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "hash": "07587a402538d64924ecc1b11fd07da62a6713ac489bc76a60f03654226c844a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "fce3078e-c270-459f-a637-5009e3c264e1", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "hash": "3be40d49596f7ea1409f610c1038314f3e4274d066ae54c5a539fb0ea413b28c", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "02041b0f-2b6e-4ef5-9bff-b504d45e83c7", "node_type": "1", "metadata": {}, "hash": "d83f15d67b44d617494538a8a00cdb71003d62e244d50f234b6ff1d419b4696b", "class_name": "RelatedNodeInfo"}}, "text": "> - Second Lesson: Not having someone on hand from the project can be a big barrier, as we suspect there may have just been some small tweaks or changes that would fix our problem.\n> - One suggestion was to have a mix of unsupervised hacking and more knowledgeable feedback, maybe by having the person working on the project show up an hour in. If we can't get someone who knows the project well to attend, at least have them available on Slack in case we get stuck.\n> - At the very least, we were able to document our issues on the repo, and in at least one case the project was able to make fixes.\n>\n> One suggestion is to encourage \"Good First Issue\" tags or \"Hacktoberfest\"-type tags that would help people know what simple things could be tackled in a few hours if they succeed in installing the software.\n> I have found that it isn't always straightforward to come up with such issues (I tried thinking of some for a previous iteration of the activity, but always got stuck when I realized I didn't have a clue what to do and explaining the problem was hard enough), and in projects that are currently running it isn't always easy to leave these uncompleted when you have more time to work on a project.\n>\n> One idea was to have a HackMD document, similar to the one that suggests project meetings for new joiners to shadow, that lists all repos with such issues in one place, making it easier to find something fruitful to work on if these audits continue to be spontaneous.\n>\n> An alternative is to make these non-spontaneous and schedule them in advance.\n> We've kicked around the thought of scheduling lightning talks for all projects \u2014 perhaps we could better leverage people's brains if we gave a lightning talk intro on Tuesday and then guided people through installing and hacking on the software the next day?\n>\n> New joiners to projects inevitably spend a bunch of time doing exactly what we were trying to do in these sessions.\n> Can we leverage the time investment of the new joiner by doubling up other team members to do this?\n>\n> Should new joiners to the team be encouraged to do this for a couple of weeks before being assigned to a project?", "start_char_idx": 1174, "end_char_idx": 3338, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "02041b0f-2b6e-4ef5-9bff-b504d45e83c7": {"__data__": {"id_": "02041b0f-2b6e-4ef5-9bff-b504d45e83c7", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "aac8d6e30090f116e25aadfb09076acb7c3a5346", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "hash": "07587a402538d64924ecc1b11fd07da62a6713ac489bc76a60f03654226c844a", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "0efe9dfa-f503-40c3-b065-20103e991baf", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/code_audits.md", "file_name": "code_audits.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/code_audits.md"}, "hash": "06102c82d426d9ae8c7c0d2a6e6fe0a8743488f62065766446af95eda4abfae8", "class_name": "RelatedNodeInfo"}}, "text": ">\n> One idea was to have a HackMD document, similar to the one that suggests project meetings for new joiners to shadow, that lists all repos with such issues in one place, making it easier to find something fruitful to work on if these audits continue to be spontaneous.\n>\n> An alternative is to make these non-spontaneous and schedule them in advance.\n> We've kicked around the thought of scheduling lightning talks for all projects \u2014 perhaps we could better leverage people's brains if we gave a lightning talk intro on Tuesday and then guided people through installing and hacking on the software the next day?\n>\n> New joiners to projects inevitably spend a bunch of time doing exactly what we were trying to do in these sessions.\n> Can we leverage the time investment of the new joiner by doubling up other team members to do this?\n>\n> Should new joiners to the team be encouraged to do this for a couple of weeks before being assigned to a project?\n> I had a few new joiners on one of the audit sessions, and they said it was a good way to learn more about the team and our projects.", "start_char_idx": 2384, "end_char_idx": 3473, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2d448858-f9d3-4100-99b4-5f915b545d92": {"__data__": {"id_": "2d448858-f9d3-4100-99b4-5f915b545d92", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f7c1d6d16f4411a8515fce7ca22798085eb99e00", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "hash": "4c1c990d65dd5aa3844216624b8cacef2ffb3b4845fa2b4c10d703b4b09e6a75", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "83517c70-7306-4404-b05e-67fb691d01a8", "node_type": "1", "metadata": {}, "hash": "1efe86d1cdefbc8790abb7ee3c01bc1e8a5ba3ac8e8038c21ed04420f2a4330e", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Defining Project Doneness\"\nweight: 2\n\n---\n\n# Defining Project Doneness\n\nThis working document was produced as part of Accelerating AI in the Arts and Humanities (AAAH).\n\nAs a Research Software Engineer, we often join a collaborative project when a code base already exists.\nDefining the aims of a collaboration can be challenging, and when there is limited time it is important to apply attention in the right places.\nThe following document is designed to help:\n\n- Showcase what is important to us as RSEs\n- Assessing the current status of an existing repo\n- Define clear objectives\n- Determine where to put effort\n- Provide a common framework and language for collaborators\n- Clearly define when the collaboration is done!\n\nThe framework includes objectives that focus on improving the impact and legacy of the software, expanding communities of practice, and providing a confident codebase for future research.\n\nFor each project discuss with collaborators which \"Level\" in each of the following categories is a MINIMUM aim for defining done.\nRemember, time is a resource and so consider where efforts should be prioritised.\n\nReach and Robustness\n: Level 01. **Internal users**: Identify how users already use the tool and its current issues.\n: Level 02. **Best practices**: Address minimum code best practices with view to robustness and accessibility\n: Level 03. **External users**: Identify how similar external users could use the tool and current barriers\n: Level 04. **Generalise**: Identify how the tool could be made general and current barriers to that\n: Level 05. **Proof of concept**: Demonstrate its use outside its original intended purpose/audience.\n\nFunctionality\n: Level 01. **Standard use**: Install the tool, try out a standard use case, write a demo if one doesn't exist\n: Level 02. **Known Limitations**: Understand and report.\n: Level 03. **Advanced use**: Try out normal range but more advanced use cases, with edge cases.\n: Level 04. **New features**: Identifying features for new functionality.\n: Level 05. **Enhancements**: Fix functionality bugs and add features.\n\nDocumentation\n: Level 01.", "start_char_idx": 0, "end_char_idx": 2129, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "83517c70-7306-4404-b05e-67fb691d01a8": {"__data__": {"id_": "83517c70-7306-4404-b05e-67fb691d01a8", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f7c1d6d16f4411a8515fce7ca22798085eb99e00", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "hash": "4c1c990d65dd5aa3844216624b8cacef2ffb3b4845fa2b4c10d703b4b09e6a75", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2d448858-f9d3-4100-99b4-5f915b545d92", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "hash": "2ba3dd041b5328f157d2260825490959fd80bfd18658d9ee22a434384f05a5f6", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "fe552232-5b34-4657-b842-1fd039e24eb2", "node_type": "1", "metadata": {}, "hash": "6526f0e460fdb927b3e101a9dde447687e11ea4e0e85fdcc03b8f12048e480ed", "class_name": "RelatedNodeInfo"}}, "text": "**External users**: Identify how similar external users could use the tool and current barriers\n: Level 04. **Generalise**: Identify how the tool could be made general and current barriers to that\n: Level 05. **Proof of concept**: Demonstrate its use outside its original intended purpose/audience.\n\nFunctionality\n: Level 01. **Standard use**: Install the tool, try out a standard use case, write a demo if one doesn't exist\n: Level 02. **Known Limitations**: Understand and report.\n: Level 03. **Advanced use**: Try out normal range but more advanced use cases, with edge cases.\n: Level 04. **New features**: Identifying features for new functionality.\n: Level 05. **Enhancements**: Fix functionality bugs and add features.\n\nDocumentation\n: Level 01. **Follow docs**: Follow the existing starting point of documentation (no help!)\n: Level 02. **What's missing**: Identify what documentation is missing: e.g. installation, optional arguments, licensing, diagrams/screenshots, future work, etc.\n: Level 03. **Write docs**: Write the documentation in markdown files\n: Level 04. **Published**: Release the documentation on GitPages etc\n: Level 05. **Walkthrough**: Write walkthroughs/demos (also gifs or videos)\n\nTesting\n: Level 01. **Continous Integration** Start the automated infrastructure\n: Level 02. **Defining Positive/Negative Cases** Conversation about what are the expected outcomes/how the tool works\n: Level 03. **Automated User Tests** End-to-end tests that check expected outcomes from above, black box\n: Level 04. **Fine-grain Tests/Unit Tests** Focus on critical/pain points, remove brittleness, modularise and reuseable components\n: Level 05. **Error Reporting** Focus on negative cases, informative error communication for external collaboration\n\nOpen Release\n: Level 01. **Find barriers**: Identify minimum criteria that need to be solved before public release e.g. authorship, sensitivity, licensing, publicity, timings\n: Level 02.", "start_char_idx": 1378, "end_char_idx": 3326, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "fe552232-5b34-4657-b842-1fd039e24eb2": {"__data__": {"id_": "fe552232-5b34-4657-b842-1fd039e24eb2", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f7c1d6d16f4411a8515fce7ca22798085eb99e00", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "hash": "4c1c990d65dd5aa3844216624b8cacef2ffb3b4845fa2b4c10d703b4b09e6a75", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "83517c70-7306-4404-b05e-67fb691d01a8", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/defining-done.md", "file_name": "defining-done.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/defining-done.md"}, "hash": "76bf0987317afb8b778a68b6a8ca8619778b35ea3fc5d09ee631e11aaae01f68", "class_name": "RelatedNodeInfo"}}, "text": "**Walkthrough**: Write walkthroughs/demos (also gifs or videos)\n\nTesting\n: Level 01. **Continous Integration** Start the automated infrastructure\n: Level 02. **Defining Positive/Negative Cases** Conversation about what are the expected outcomes/how the tool works\n: Level 03. **Automated User Tests** End-to-end tests that check expected outcomes from above, black box\n: Level 04. **Fine-grain Tests/Unit Tests** Focus on critical/pain points, remove brittleness, modularise and reuseable components\n: Level 05. **Error Reporting** Focus on negative cases, informative error communication for external collaboration\n\nOpen Release\n: Level 01. **Find barriers**: Identify minimum criteria that need to be solved before public release e.g. authorship, sensitivity, licensing, publicity, timings\n: Level 02. **Solve barriers**: Address the minimum criteria and milestone efforts\n: Level 03. **Release**: Make the repo public.\n: Level 04. **Distribute**: Release the repo on PiPY or CRAN or equivalent, include DOI\n: Level 05. **Publisise**: Publish the repo with JOSS, and/or publicly present the work.", "start_char_idx": 2523, "end_char_idx": 3621, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "60f9d284-345d-448e-b593-0fbe326aa09a": {"__data__": {"id_": "60f9d284-345d-448e-b593-0fbe326aa09a", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "80badcbb-374e-4a2f-82e0-22acbf1ee2a8", "node_type": "1", "metadata": {}, "hash": "ac5eaf2289918b89155b6eea7374c29f6017cb373217dfc85434bddd853b7dd5", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Knowledge Sharing\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 1\n---\n\n# Knowledge Sharing\n\nThis page lists the tools, resources and activities that REG has to promote general communication as well as sharing and gaining knowledge across the team (and the Turing as a whole), and some thoughts on what we could try to improve this.\nIf you're looking to learn something new, get help with a problem, share something cool you've been doing, or find out what others in the team are doing, the list below may give an idea of where to look/what to try.\n\nIf you'd like to organise something the best options are usually to post to [Slack](#slack) and the [Hut23 mailing list](#hut23-mailing-list) (see below).\n\n## Daily\n\n### Github Hut23 Repo\n\nWe use GitHub (and a couple of related tools) extensively for tracking projects and other tasks within the team.\nThere is a [separate page describing how we use them]({{< relref \"/docs/how_we_work/project_tracking.md\" >}}).\n\n### Coffee Breaks\n\nWe have coffee breaks on Gather twice daily.\nSee the [Regular Events]({{< relref \"../regular_events\" >}}) page for details.\nIf the times aren't convenient and you fancy a break then try posting on Slack and the chances are that there'll be others that fancy a break too.\n\n### Slack\n\nSlack is our main tool for informal messaging and announcements (there's some info on how to join on\n[here]({{< relref \"/docs/onboarding/new_joiners/systems_set_up.md#slack\" >}})).\nIt's also a good place to ask for help.", "start_char_idx": 0, "end_char_idx": 1581, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "80badcbb-374e-4a2f-82e0-22acbf1ee2a8": {"__data__": {"id_": "80badcbb-374e-4a2f-82e0-22acbf1ee2a8", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "60f9d284-345d-448e-b593-0fbe326aa09a", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "53bbe35fdd1eec75d5418dacac313bdd17cca804f13f803409110d6e6ee6c42d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b0ac2673-089e-43a9-b004-cb55670d5613", "node_type": "1", "metadata": {}, "hash": "d888de9bf71d422b26430648cafdba8e8011a4d3a002efdd49d5d3836aff7f86", "class_name": "RelatedNodeInfo"}}, "text": "## Daily\n\n### Github Hut23 Repo\n\nWe use GitHub (and a couple of related tools) extensively for tracking projects and other tasks within the team.\nThere is a [separate page describing how we use them]({{< relref \"/docs/how_we_work/project_tracking.md\" >}}).\n\n### Coffee Breaks\n\nWe have coffee breaks on Gather twice daily.\nSee the [Regular Events]({{< relref \"../regular_events\" >}}) page for details.\nIf the times aren't convenient and you fancy a break then try posting on Slack and the chances are that there'll be others that fancy a break too.\n\n### Slack\n\nSlack is our main tool for informal messaging and announcements (there's some info on how to join on\n[here]({{< relref \"/docs/onboarding/new_joiners/systems_set_up.md#slack\" >}})).\nIt's also a good place to ask for help.\nChannels of interest include:\n\n- `#hut23`: Our channel for the REG team (ask someone to add you if you don't have access)\n- `#tps`: Channel for the [tools, practices and systems (TPS) community](https://www.turing.ac.uk/research/research-programmes/tools-practices-and-systems) at the Turing\n- `#askaway`: Place to ask general questions (_e.g._ that don't fit in a specific channel)\n- `#interesting-events`, `#general`, `#social`, `#random`: General chat and announcement about what's happening around the Turing.\n\nWe use [Geekbot](https://geekbot.com/), which is a Slack App.\nWe use this in two different ways (both are voluntary):\n\n- A virtual daily standup: Geekbot asks what you're planning to do each day, what you've achieved and if there is anything you are stuck on. This can be useful for your own planning, but also helps give an overview of the range of other work and skills within the team.\n- Ice breaker-style questions a few times a week.", "start_char_idx": 801, "end_char_idx": 2535, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b0ac2673-089e-43a9-b004-cb55670d5613": {"__data__": {"id_": "b0ac2673-089e-43a9-b004-cb55670d5613", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "80badcbb-374e-4a2f-82e0-22acbf1ee2a8", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d8210cd43019d2129094b75215b5923e1b37188e19b950eb099039f5cbb2f707", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ff718593-9a32-4079-8566-b2eeccec373d", "node_type": "1", "metadata": {}, "hash": "34b2971244362db54df364a834a825440344ecadbdbd8f91613203e332b07e06", "class_name": "RelatedNodeInfo"}}, "text": "We use [Geekbot](https://geekbot.com/), which is a Slack App.\nWe use this in two different ways (both are voluntary):\n\n- A virtual daily standup: Geekbot asks what you're planning to do each day, what you've achieved and if there is anything you are stuck on. This can be useful for your own planning, but also helps give an overview of the range of other work and skills within the team.\n- Ice breaker-style questions a few times a week.\n\nTo join in with the use of Geekbot, you will need to first add the [Geekbot app to Slack](https://alan-turing-institute.slack.com/apps/A0H67RAG0-standups-surveys-polls-geekbot) and then join these slack channels `#hut23standup` and `#hut23standup-tasks`.\n\n## Weekly\n\n### Tech Talks\n\n[See here]({{< relref \"docs/regular_events/lunchtime_tech_talks.md\" >}}).\n\n### Reading Groups\n\n[See here]({{< relref \"docs/regular_events/reading_groups.md\" >}}).\n\n## Approximately Monthly\n\n### REG Team Meetings\n\nRoughly once a month we have an all-REG meeting where we welcome new joiners and a few people briefly present news from around REG or the Turing.\nThis is usually followed by a discussion on a topic of interest for the wider team.\nIf you don't have the calendar invites (new joiners might not initially) ask someone else in the team to forward them to you.\n\n### Turing Events\n\nThe main focus of this page is what we do in REG, but there are many things happening around the Turing that REG members are also encouraged to attend, including:\n\n- Turing Catchups & Town Halls \u2014 Regular meetings open to all staff with updates from around the institute. New starters may not have the calendar invites initially \u2014 ask others in the team to forward the details if you're unsure.\n- [Turing Events](https://www.turing.ac.uk/events) - often public talks & workshops.", "start_char_idx": 2097, "end_char_idx": 3888, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ff718593-9a32-4079-8566-b2eeccec373d": {"__data__": {"id_": "ff718593-9a32-4079-8566-b2eeccec373d", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "b0ac2673-089e-43a9-b004-cb55670d5613", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "e32c2aab3e8456ec5fc61b1e77283c1a1d8d15f9faf6833f697c1909838eba65", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a0611aa2-9601-457a-9a45-7ebdea9de014", "node_type": "1", "metadata": {}, "hash": "d5fb6270650b81cd5c0a7c71ec7d5d533cd128a38e6f8f82e497828dd05179ab", "class_name": "RelatedNodeInfo"}}, "text": "## Approximately Monthly\n\n### REG Team Meetings\n\nRoughly once a month we have an all-REG meeting where we welcome new joiners and a few people briefly present news from around REG or the Turing.\nThis is usually followed by a discussion on a topic of interest for the wider team.\nIf you don't have the calendar invites (new joiners might not initially) ask someone else in the team to forward them to you.\n\n### Turing Events\n\nThe main focus of this page is what we do in REG, but there are many things happening around the Turing that REG members are also encouraged to attend, including:\n\n- Turing Catchups & Town Halls \u2014 Regular meetings open to all staff with updates from around the institute. New starters may not have the calendar invites initially \u2014 ask others in the team to forward the details if you're unsure.\n- [Turing Events](https://www.turing.ac.uk/events) - often public talks & workshops.\n\n### Open Source Hacksessions\n\n[See here]({{< relref \"docs/regular_events/open_source_hacksessions.md\" >}}).\n\n### REG Newsletter\n\nWe have a monthly REG newsletter which is usually sent around about a week before the monthly team meeting.\nIt contains short project updates and other news/updates from around the team and the institute.\nIt comes to the Hut23 mailing list (see above).\nIf you have something you'd like to be included contact the [newsletter owner](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n### Hut23 Mailing List\n\nYou should automatically be added to the [Hut23 mailing list](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) when you join REG (if not ask your line manager about it).\nThis is often the best way to contact everyone in the team when organising events, or for other news/communication that should be a bit more permanent/formal than Slack and doesn't fit in an issue in the Hut23 repo.", "start_char_idx": 2984, "end_char_idx": 4922, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a0611aa2-9601-457a-9a45-7ebdea9de014": {"__data__": {"id_": "a0611aa2-9601-457a-9a45-7ebdea9de014", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ff718593-9a32-4079-8566-b2eeccec373d", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "af8339fa611d042853ba1305ac79a04e67f974b7ac4ff1b2084bbb1ba59e28e0", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f0d8f067-0996-400a-9679-3f8393fc11ff", "node_type": "1", "metadata": {}, "hash": "7eb72bb4616c4ee230701ee8a8616019484bad9e5165efd7d145b849c4315c11", "class_name": "RelatedNodeInfo"}}, "text": "### REG Newsletter\n\nWe have a monthly REG newsletter which is usually sent around about a week before the monthly team meeting.\nIt contains short project updates and other news/updates from around the team and the institute.\nIt comes to the Hut23 mailing list (see above).\nIf you have something you'd like to be included contact the [newsletter owner](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n### Hut23 Mailing List\n\nYou should automatically be added to the [Hut23 mailing list](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) when you join REG (if not ask your line manager about it).\nThis is often the best way to contact everyone in the team when organising events, or for other news/communication that should be a bit more permanent/formal than Slack and doesn't fit in an issue in the Hut23 repo.\n\n## Less frequently or ad-hoc\n\n### Data study Groups\n\n[Data study groups](https://www.turing.ac.uk/collaborate-turing/data-study-groups) (DSGs) are week-long hack sessions on different projects that happen a few times a year.\nParticipants are drawn from across the Turing and REG people have previously joined both as team members and project PIs.\n\n### Turing courses and training\n\nThere are professional and academic training opportunities around the Turing.\nThere's some info [on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Section/Default.aspx?Section=3701).\nAdditionally, REG also runs a yearly [research software engineering course](https://alan-turing-institute.github.io/rse-course).\n\n### Remote Co-working\n\nOccasionally a few REG members get together on Zoom to Gather to do our work (not necessarily to work on the same thing, but to see each other and have a quick chat whilst we're working).\nDuring the COVID lockdowns, we organised these sessions regularly.\nNow they only occur on an ad-hoc basis.\nIf you'd like to work in this way, feel free to ask on Slack.\n\nSome project teams also choose to work in this way.", "start_char_idx": 3999, "end_char_idx": 6058, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f0d8f067-0996-400a-9679-3f8393fc11ff": {"__data__": {"id_": "f0d8f067-0996-400a-9679-3f8393fc11ff", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a0611aa2-9601-457a-9a45-7ebdea9de014", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "31c6f5f19bd3abf4e272c89f40851007694f31c3fa9210dcfed4c481a25335ba", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ddf0d0fa-a27a-4231-966b-2d71c5f40d99", "node_type": "1", "metadata": {}, "hash": "ff8bf516682f7cecb2a47be1d74f658526323ddd4ed78b05811599341c48f753", "class_name": "RelatedNodeInfo"}}, "text": "Participants are drawn from across the Turing and REG people have previously joined both as team members and project PIs.\n\n### Turing courses and training\n\nThere are professional and academic training opportunities around the Turing.\nThere's some info [on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Section/Default.aspx?Section=3701).\nAdditionally, REG also runs a yearly [research software engineering course](https://alan-turing-institute.github.io/rse-course).\n\n### Remote Co-working\n\nOccasionally a few REG members get together on Zoom to Gather to do our work (not necessarily to work on the same thing, but to see each other and have a quick chat whilst we're working).\nDuring the COVID lockdowns, we organised these sessions regularly.\nNow they only occur on an ad-hoc basis.\nIf you'd like to work in this way, feel free to ask on Slack.\n\nSome project teams also choose to work in this way.\n\n### Hack Sessions & REG Collaborative Time\n\nIf there's something you'd like to work on collaboratively feel free to organise a hack session (typically these would be longer but less frequent than a reading group, and perhaps more focused on implementing rather than reading).\nIn the past several of us have got together to work on contributions for [Hacktoberfest](https://hacktoberfest.digitalocean.com/).\n\n### Repo Audits\n\nA few of us plan to get together to help check whether documentation, usage and installation instructions are clear for a project (for example), or to review the code.\nSee [this page]({{< relref \"code_audits.md\" >}}) for more details.\n\n### Project retrospective - Snark Hunts\n\nSnark hunts are facilitated discussions after a project has finished where the project team, together with others from REG that didn't work on the project, answer/ask questions relating to the experience of working on it \u2014 what went well, what could have been better _etc._.\nWe don't do it for every project, but they can be a good opportunity to reflect and to get ideas for what we should do differently/do more of in the future.\nWe're open to trying other ideas too!", "start_char_idx": 5149, "end_char_idx": 7231, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ddf0d0fa-a27a-4231-966b-2d71c5f40d99": {"__data__": {"id_": "ddf0d0fa-a27a-4231-966b-2d71c5f40d99", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f0d8f067-0996-400a-9679-3f8393fc11ff", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "9a3961710819b9613727cca6ec0f847be0c6e4b37e863d76e6ce135d655541b4", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5aadd0c8-686f-46f2-8311-14f79994b627", "node_type": "1", "metadata": {}, "hash": "9d94896ac3fd3f683d09e1d0921f50765a054accb3e8386b8d602ab57fae5f8e", "class_name": "RelatedNodeInfo"}}, "text": "In the past several of us have got together to work on contributions for [Hacktoberfest](https://hacktoberfest.digitalocean.com/).\n\n### Repo Audits\n\nA few of us plan to get together to help check whether documentation, usage and installation instructions are clear for a project (for example), or to review the code.\nSee [this page]({{< relref \"code_audits.md\" >}}) for more details.\n\n### Project retrospective - Snark Hunts\n\nSnark hunts are facilitated discussions after a project has finished where the project team, together with others from REG that didn't work on the project, answer/ask questions relating to the experience of working on it \u2014 what went well, what could have been better _etc._.\nWe don't do it for every project, but they can be a good opportunity to reflect and to get ideas for what we should do differently/do more of in the future.\nWe're open to trying other ideas too!\n\nFor more info, [see here]({{< relref \"snark_hunts.md\" >}}) for more details.\n\n### Working on Other Projects\n\nWorking on different projects with different people causes a natural sharing of knowledge around the team.\nBeyond the usual project allocation process, there may be other ways to get involved with another project for a short time (some of these are speculative and not currently common practice in REG):\n\n- Rotating between projects: Where a team member has been on a project for a long time, they may wish to change. Team members should ask to rotate to a different project in the next set of project allocations.\n- Gaps between projects: Occasionally there are short gaps between one project finishing and another starting. These can be opportunities to get involved in something different for a while. One example could be to try a project with the ARC team (though I don't think anyone in REG has done this yet). Projects may also want to get into the habit of creating \"Hacktoberfest\" style issues that newcomers could quickly get up to speed with and get involved if they have a gap in allocations.", "start_char_idx": 6336, "end_char_idx": 8346, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5aadd0c8-686f-46f2-8311-14f79994b627": {"__data__": {"id_": "5aadd0c8-686f-46f2-8311-14f79994b627", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "7f2ac23942c4b6feadd462ef69d53718c8c2b3f9", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "d9e91d23f041b8b3ae1490d07ced447c832cd5ce38bc256652fa3d569cc01cac", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ddf0d0fa-a27a-4231-966b-2d71c5f40d99", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/knowledge_sharing.md", "file_name": "knowledge_sharing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/knowledge_sharing.md"}, "hash": "29dcd3c614c4dfa50927ab626072f280e72a15408fc89ba7d9aa9844575b15db", "class_name": "RelatedNodeInfo"}}, "text": "Beyond the usual project allocation process, there may be other ways to get involved with another project for a short time (some of these are speculative and not currently common practice in REG):\n\n- Rotating between projects: Where a team member has been on a project for a long time, they may wish to change. Team members should ask to rotate to a different project in the next set of project allocations.\n- Gaps between projects: Occasionally there are short gaps between one project finishing and another starting. These can be opportunities to get involved in something different for a while. One example could be to try a project with the ARC team (though I don't think anyone in REG has done this yet). Projects may also want to get into the habit of creating \"Hacktoberfest\" style issues that newcomers could quickly get up to speed with and get involved if they have a gap in allocations.\n- Shadowing projects: Especially for new starters, we've started to encourage the idea of shadowing projects for a short while, to get an idea of how we work.", "start_char_idx": 7449, "end_char_idx": 8505, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f7f09540-e847-4d0c-a813-b6e580bedcb4": {"__data__": {"id_": "f7f09540-e847-4d0c-a813-b6e580bedcb4", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eb7da3de122c27799f16e0b9f68e9ac6fcefc043", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "hash": "69fbce679c06458a35878f2a3b419446736fb8d8b7a28fd4de33774a0128c23f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "fc8c1d05-99cf-471d-a569-8b4398a28a04", "node_type": "1", "metadata": {}, "hash": "ed60112d8828caca70f817be281416a800a2ea27afe0ccc31156d33613444f02", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Line Management\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 5\n---\n\n# Line Management\n\nLine management in REG is a little different than in other parts of the Turing as you often will not be working directly with the people you are managing. This guide aims to explain how this relationship might work.\n\n## How line management duties are assigned\n\nAt (current) senior level, people are expected to line manage 1-2 standards. This will probably change with the new lead role (see the [REG role matrix](https://github.com/alan-turing-institute/Hut23/blob/master/development/REG-Roles-Matrix.md#line-managementmentoring)). Currently, if seniors are willing to line manage more than 2 people, they should inform the recruitment lead (can be found in [service areas](https://github.com/alan-turing-institute/research-engineering-group/wiki/Service-areas)).\n\nAt standard level, opportunities for line management and project mentoring of junior members and interns are offered during the year. To assign such duties, we follow whenever possible time spent at standard level in REG.\n\nAt both levels, we try to avoid overlaps between projects and line management, at least during probation.\n\n## First day\n\nAs a line manager you will have a 1-to-1 meeting with the person you will be managing on their first day. By this point they will have had various other inductions (eg. with HR, IT, their REG buddies). You might want to focus on the following points\n\n- introduce what line management is at REG\n    - part mentor, part escalation point, part pastoral support, part admin, can take different shapes depending on need\n- ... and what it is isn\u2019t\n    - task-setting, boss vibes, oversight\n- point out that line management, like everything else in REG, is a collaboration\n    - have a chat about what management styles have worked well for them.", "start_char_idx": 0, "end_char_idx": 1933, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "fc8c1d05-99cf-471d-a569-8b4398a28a04": {"__data__": {"id_": "fc8c1d05-99cf-471d-a569-8b4398a28a04", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eb7da3de122c27799f16e0b9f68e9ac6fcefc043", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "hash": "69fbce679c06458a35878f2a3b419446736fb8d8b7a28fd4de33774a0128c23f", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f7f09540-e847-4d0c-a813-b6e580bedcb4", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "hash": "6abd43e847829f4b07f2a44feea1b2eba3c56b917f7f73c919d0a8ca268290ad", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a5af40e6-1d06-41d6-9640-9b0b047b263a", "node_type": "1", "metadata": {}, "hash": "a95d6301f0e2f3b5c6ba177080284f6abdf6840b9acabac8816f54ccadc4800d", "class_name": "RelatedNodeInfo"}}, "text": "To assign such duties, we follow whenever possible time spent at standard level in REG.\n\nAt both levels, we try to avoid overlaps between projects and line management, at least during probation.\n\n## First day\n\nAs a line manager you will have a 1-to-1 meeting with the person you will be managing on their first day. By this point they will have had various other inductions (eg. with HR, IT, their REG buddies). You might want to focus on the following points\n\n- introduce what line management is at REG\n    - part mentor, part escalation point, part pastoral support, part admin, can take different shapes depending on need\n- ... and what it is isn\u2019t\n    - task-setting, boss vibes, oversight\n- point out that line management, like everything else in REG, is a collaboration\n    - have a chat about what management styles have worked well for them.\n- find out their background, why they decided to join the group\n- discuss what their day-to-day might look like\n    - 80% projects (either one or two projects)\n    - 10% REG responsibilities (eg.", "start_char_idx": 1084, "end_char_idx": 2129, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a5af40e6-1d06-41d6-9640-9b0b047b263a": {"__data__": {"id_": "a5af40e6-1d06-41d6-9640-9b0b047b263a", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eb7da3de122c27799f16e0b9f68e9ac6fcefc043", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "hash": "69fbce679c06458a35878f2a3b419446736fb8d8b7a28fd4de33774a0128c23f", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "fc8c1d05-99cf-471d-a569-8b4398a28a04", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/line-management.md", "file_name": "line-management.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/line-management.md"}, "hash": "c316c2693e3a61a15d4492754f48936fe67027d35f654151714f4df18b3f81bc", "class_name": "RelatedNodeInfo"}}, "text": "## First day\n\nAs a line manager you will have a 1-to-1 meeting with the person you will be managing on their first day. By this point they will have had various other inductions (eg. with HR, IT, their REG buddies). You might want to focus on the following points\n\n- introduce what line management is at REG\n    - part mentor, part escalation point, part pastoral support, part admin, can take different shapes depending on need\n- ... and what it is isn\u2019t\n    - task-setting, boss vibes, oversight\n- point out that line management, like everything else in REG, is a collaboration\n    - have a chat about what management styles have worked well for them.\n- find out their background, why they decided to join the group\n- discuss what their day-to-day might look like\n    - 80% projects (either one or two projects)\n    - 10% REG responsibilities (eg. service areas)\n    - 10% personal development\n- briefly talk about how people are allocated to projects\n- check that they've taken a look at the [new starters checklist](https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/checklist/)\n- leave time for questions\n\n## First week\n\nLater in the first week you might want to go through some of the more technical points:\n\n- [Team reporting structure](https://github.com/alan-turing-institute/research-engineering-group/wiki/Team-reporting-structure)\n- Adding yourself to the [Turing GitHub organisation](https://turingcomplete.topdesk.net/tas/public/ssp/content/serviceflow?unid=3844fabf8b1c45ca9028758a350ff230)\n- Emoji reacting to projects\n    - Projects [looking for people](https://github.com/alan-turing-institute/Hut23/projects/2#column-2488036)\n    - [Currently active](https://github.com/alan-turing-institute/Hut23/projects/2#column-1062281) projects\n- Project allocations on [Forecast](https://forecastapp.com/974183/schedule/team)\n- Personal time tracking on [Harvest](https://thealanturinginstitute.harvestapp.com/time/week) (noting that this is not compulsory)\n- Probation formalities", "start_char_idx": 1280, "end_char_idx": 3300, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "8a8133a4-4245-4942-8cb0-db3cb28c44f9": {"__data__": {"id_": "8a8133a4-4245-4942-8cb0-db3cb28c44f9", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/meeting_record.md", "file_name": "meeting_record.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/meeting_record.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3fb6caa127af57616062f5efa38208b722a42221", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/meeting_record.md", "file_name": "meeting_record.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/meeting_record.md"}, "hash": "d80bc6673ff8036aa8730455e39d165bf36cc119d1d95ff5838b0b6ef9992f1f", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Meeting Records\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Meeting Records\n\nWhen a synchronous meeting is unavoidable, it is critical to produce a clear and thorough record.\n\nThe meeting record is important as a reference for what happened at the meeting.\nIn particular, any decisions made or actions assigned need to be clearly recorded for future reference.\n\nA good record will also be invaluable to anyone who was not able to attend an in-person meeting.\n\n## Meeting Record Template\n\nBelow is a suggested [template]({{% static_url url=\"meeting_record.md\" %}}) for a meeting record document.\nThe template uses [Pandoc](https://pandoc.org/MANUAL.html#variables-for-latex) [Markdown](https://pandoc.org/MANUAL.html#pandocs-markdown).\nParts of the template are already filled with explanations of how they should be used.\n\n{{% source_file file=\"static/meeting_record.md\" language=\"md\" %}}\n\nFor printing or wider distribution, a meeting record following the template may be converted to pdf using Pandoc,\n\n```bash\npandoc meeting_record.md -o meeting_record.pdf\n```\n\nA Pandoc [YAML metadata block](https://pandoc.org/MANUAL.html#extension-yaml_metadata_block) at the top of the template contains metadata about the meeting and helps the record render into a good-looking pdf document.\n\n## How To Use\n\n- Create the meeting record document before the meeting.\n- Use a collaborative text editor like [HackMD](https://hackmd.io) so that all attendees may edit the document.\n- Circulate the document to allow others to add and agree on agenda items.\n- Distribute the record or store in a location visible to everyone who may need to read it.\n\nThe template may also be used to avoid a meeting entirely.\nThe document can be completed collaboratively and asynchronously while achieving the same result.", "start_char_idx": 0, "end_char_idx": 2129, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2b17d74a-862c-435b-a4fe-b602778b2680": {"__data__": {"id_": "2b17d74a-862c-435b-a4fe-b602778b2680", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/open_access.md", "file_name": "open_access.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/open_access.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "710a44b3fd231bdb2ce3cf1f5884505969def43e", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/open_access.md", "file_name": "open_access.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/open_access.md"}, "hash": "b1237e4ef70620ea2316e75bfc29e4e440c34d8a7ae842f7d792b0d413b50402", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Publishing Open Access Journal Articles\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 7\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Open Access Journal Articles\n\nThe Turing has access to a block grant to cover open access charges for journal publications for UKRI funded projects if there are not already funds in the project-specific grant to cover them. If you have published an article, you may be able to cover the open access fees using this funding source. See [Open Access Publishing - Funding](https://mathison.turing.ac.uk/page/BlockGrant?SearchId=380294) on Mathison for further information.", "start_char_idx": 0, "end_char_idx": 890, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d27c338e-31f3-4279-a7e5-01daae284dbc": {"__data__": {"id_": "d27c338e-31f3-4279-a7e5-01daae284dbc", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/project_tracking.md", "file_name": "project_tracking.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/project_tracking.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "988753b83c1f32468cb2f580dec2e0ea17c5adcd", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/project_tracking.md", "file_name": "project_tracking.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/project_tracking.md"}, "hash": "bd1ac70e476b79c89eeb881517011ffeba421072338b66db983ed258199e9029", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "baacf820-88ef-4e27-ab64-5a55a04f1e5f", "node_type": "1", "metadata": {}, "hash": "34548d1671a36ce34dafae22ab3b60922aa7c556c1e7123cf6896abea5220145", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Project Tracking\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 3\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exlude this page from the search\n# bookSearchExclude = true\n---\n\n# Project Tracking\n\nThis page describes some of the tools we use for tracking projects and other tasks within the team. It does not go into details of how individual projects are managed, but how REG coordinates its commitments to the range of projects its involved in.\n\n## Hut23 GitHub Repo\n\nThe [Hut23 repo](https://github.com/alan-turing-institute/Hut23) is the central hub for most team matters and admin.\n\n- [Project Tracker](https://github.com/alan-turing-institute/Hut23/projects/2): Board shows all upcoming, current and completed projects. There's an issue for each project (see below). Depending on the project status, it will be in one column or another. In order to assign REG team members to projects, we use emojis to vote for the projects that we would be interested in taking part. You should react to all projects on the \"Finding people\" column, but also on the \"Awaiting go/no-go\" column. The voting system is as follows:\n\n  - :thumbsup: Would be okay working on this project\n  - :thumbsdown: Would rather not work on this project\n  - :smile: Would really like to work on this project\n\n- [Issues](https://github.com/alan-turing-institute/Hut23/issues): Each project has an issue following a common template, including a general description of a project and its current status, for example. There are also issues for service areas and other activities/organisational issues for the team.\n\n### Project Repos\n\nAll repos in the Turing organisation that REG have worked on should have the `hut23` tag.", "start_char_idx": 0, "end_char_idx": 1913, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "baacf820-88ef-4e27-ab64-5a55a04f1e5f": {"__data__": {"id_": "baacf820-88ef-4e27-ab64-5a55a04f1e5f", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/project_tracking.md", "file_name": "project_tracking.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/project_tracking.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "988753b83c1f32468cb2f580dec2e0ea17c5adcd", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/project_tracking.md", "file_name": "project_tracking.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/project_tracking.md"}, "hash": "bd1ac70e476b79c89eeb881517011ffeba421072338b66db983ed258199e9029", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "d27c338e-31f3-4279-a7e5-01daae284dbc", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/project_tracking.md", "file_name": "project_tracking.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/project_tracking.md"}, "hash": "ba0daebf91d7451418927881c3f4ae8dec2d41da2a5844bc0fc5232671a301eb", "class_name": "RelatedNodeInfo"}}, "text": "In order to assign REG team members to projects, we use emojis to vote for the projects that we would be interested in taking part. You should react to all projects on the \"Finding people\" column, but also on the \"Awaiting go/no-go\" column. The voting system is as follows:\n\n  - :thumbsup: Would be okay working on this project\n  - :thumbsdown: Would rather not work on this project\n  - :smile: Would really like to work on this project\n\n- [Issues](https://github.com/alan-turing-institute/Hut23/issues): Each project has an issue following a common template, including a general description of a project and its current status, for example. There are also issues for service areas and other activities/organisational issues for the team.\n\n### Project Repos\n\nAll repos in the Turing organisation that REG have worked on should have the `hut23` tag.\nSo if you're looking for something from a previous project [searching for these tags](https://github.com/search?q=topic%3Ahut23+org%3Aalan-turing-institute&type=Repositories) may be a good place to start.\nAdditionally, they should have a tag of the format `hut23-123`, where `123` is the issue number for that project in the Hut23 repo.\n\n### Forecast & Wimbledon\n\nWe use [Forecast](https://forecastapp.com/974183/schedule/projects) to track everyone's allocations to projects.\nYou can see which projects & service areas everyone in the team is currently involved in, as well as what they were allocated to in the past and will be allocated to in the future.\n\n\"[Wimbledon planner](https://wimbledon-planner.azurewebsites.net/)\" is an online version of a poster we previously had in the office to display the information in Forecast.\nIt's easier to see allocations for a longer period, and you can click project names to take you directly to the issue describing that project in the Hut23 repo.", "start_char_idx": 1065, "end_char_idx": 2906, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "14a40177-16bb-46f1-96fd-b86f287158c0": {"__data__": {"id_": "14a40177-16bb-46f1-96fd-b86f287158c0", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b1b12c59add5cecfb80d084f57cd6141ba4daa45", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "35acbafb23598890b3bc991d858ed14e28442bd4f664c33e2f0c9abd3ef4fb07", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "fb1a7658-c476-4a44-b85d-212669923fd8", "node_type": "1", "metadata": {}, "hash": "4388d1a4ebc288e517177e8a022ab73a5cde9d2e88098b3efb499b06636657c0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Remote Working\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 8\t\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Remote Working\n\nInformation and tips and tricks on working from home and other remote working.\n\n## Meetings, calls and screen sharing\n\n## Gather\n\nREG uses Gather for some of the coffee breaks and social events.\nIt gives a virtual space (modelled on the office!) to wander around and have conversations with people near to you.\n\n- See [The REGistry](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#video-call-links) for how to gain entry to gather.\n\nNote: Gather is blocked on the Turing VPN and on IT-managed computers.\n\n### Zoom\n\nUnless requested from IT, you will have a \"Basic\" Zoom account.\nThis allows you to create meetings for up to 40 minutes (unlimited for meetings with only 2 people).\nFor meetings longer than this, an upgrade to a \"Pro\" account is needed. To request a pro account contact [IT Services](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n\nWe have a persistent REG Zoom meeting room, which we use for things like coffee breaks, tech talks and co-working.\nSee [The REGistry](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#video-call-links) for the location.\n\nFor larger events or meetings, Zoom also has some features for Webinars and breakout rooms.\n\nZoom Rooms (like those associated with each meeting room in the office) are persistent virtual meeting spaces.\nAdditional rooms might be able to be requested from IT if required (these seem to be priced at \u00a332/month, so some justification should be given).", "start_char_idx": 0, "end_char_idx": 1963, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "fb1a7658-c476-4a44-b85d-212669923fd8": {"__data__": {"id_": "fb1a7658-c476-4a44-b85d-212669923fd8", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b1b12c59add5cecfb80d084f57cd6141ba4daa45", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "35acbafb23598890b3bc991d858ed14e28442bd4f664c33e2f0c9abd3ef4fb07", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "14a40177-16bb-46f1-96fd-b86f287158c0", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "81d95fcdb28fc350db3137a0c5bd6deeb13ef4c6684bd4292d6de0ef5ea1ebd1", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "94bd70b1-fb83-4c4e-ab35-b1a16a0234b7", "node_type": "1", "metadata": {}, "hash": "bed1063ef8312fe61a27e36a4e4f16aac7a473b49ad73546c691848efe94bfe5", "class_name": "RelatedNodeInfo"}}, "text": "For meetings longer than this, an upgrade to a \"Pro\" account is needed. To request a pro account contact [IT Services](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n\nWe have a persistent REG Zoom meeting room, which we use for things like coffee breaks, tech talks and co-working.\nSee [The REGistry](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#video-call-links) for the location.\n\nFor larger events or meetings, Zoom also has some features for Webinars and breakout rooms.\n\nZoom Rooms (like those associated with each meeting room in the office) are persistent virtual meeting spaces.\nAdditional rooms might be able to be requested from IT if required (these seem to be priced at \u00a332/month, so some justification should be given).\n\nFor more information on how to get or use Zoom, see the [Zoom TopDesk page](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/knowledgeitem?unid=e63d1edd1cdc4e8ab3ac3c4e75bb768f).\n\n### Teams\n\nNote: We mostly use Zoom for meetings and Slack for messaging in REG, but others at the Turing use Teams more heavily (e.g. the business team), and some projects have meetings there.\n\n- Get it from [the Microsoft site](https://teams.microsoft.com/downloads).\n  There is also a mobile app.\n- Make calls, group calls and share screen straightforwardly within the app.\n- You can find your VoIP number by looking at your \"My Office profile\" (office.com, then click your profile picture).\n  - Ask IT for one if you don't have one.\n- Calls to this number will ring in the Teams app.\n\n#### Some notes on using Teams\n\nTeams has several ways to share text and/or live voice and video with others:\n\n- calls\n- chats\n- meetings\n\nCalls and chats are between you and one or more of your \"contacts\".\nYou can put your contacts into \"groups\", which are distinct from \"teams\".\n\nThe most versatile option is a **meeting**.", "start_char_idx": 1131, "end_char_idx": 3075, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "94bd70b1-fb83-4c4e-ab35-b1a16a0234b7": {"__data__": {"id_": "94bd70b1-fb83-4c4e-ab35-b1a16a0234b7", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b1b12c59add5cecfb80d084f57cd6141ba4daa45", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "35acbafb23598890b3bc991d858ed14e28442bd4f664c33e2f0c9abd3ef4fb07", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "fb1a7658-c476-4a44-b85d-212669923fd8", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "aac2b5034914cbbdcc666a325f85a6178206f413cab241859beb9b5bad941d4b", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b86b8e65-d3fc-4e3b-afe2-48afa23b7a68", "node_type": "1", "metadata": {}, "hash": "6ac73eea21b6db1fe6e7fe3dd6effb97d1a42522422b7298c16feeb42a9b5fd9", "class_name": "RelatedNodeInfo"}}, "text": "the business team), and some projects have meetings there.\n\n- Get it from [the Microsoft site](https://teams.microsoft.com/downloads).\n  There is also a mobile app.\n- Make calls, group calls and share screen straightforwardly within the app.\n- You can find your VoIP number by looking at your \"My Office profile\" (office.com, then click your profile picture).\n  - Ask IT for one if you don't have one.\n- Calls to this number will ring in the Teams app.\n\n#### Some notes on using Teams\n\nTeams has several ways to share text and/or live voice and video with others:\n\n- calls\n- chats\n- meetings\n\nCalls and chats are between you and one or more of your \"contacts\".\nYou can put your contacts into \"groups\", which are distinct from \"teams\".\n\nThe most versatile option is a **meeting**.\nThe way to make one of these in the Teams app is on the *calendar* tab (obviously!).\nIt must be associated with a calendar event, and can be joined by people who are invited or by sharing a link.\nThe meeting itself has a chat associated with it.\nIt exists before and after the duration of the meeting, so can be used to set up persistent meeting spaces.\n\n## Wellbeing tips for remote working\n\nRemote working brings different challenges.\nThe work-life boundaries are less clear and some people struggle with lack of connection.\nHere we collate tips that the group has found to positively impact their wellbeing while working remotely.\nAt the beginning of the COVID-19 lockdowns, having wellbeing tips proved really useful, see below:\nFor more information on Wellbeing in REG, see the [Wellbeing page](https://mathison.turing.ac.uk/page/2157?SearchId=380431).\n\n- **Delineate the start and end of the working day.**\n  A huge challenge is separating the working day from life outside of work, especially if both happen in the same location.\n  Suggestions that might help bound work to working hours:\n  - Simulate a commute to allow your brain to switch off.\n    For example, go for a 20 minute walk, run, or read a book.\n  - Have separate _work_ clothes and _non-work_ clothes.", "start_char_idx": 2296, "end_char_idx": 4349, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b86b8e65-d3fc-4e3b-afe2-48afa23b7a68": {"__data__": {"id_": "b86b8e65-d3fc-4e3b-afe2-48afa23b7a68", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b1b12c59add5cecfb80d084f57cd6141ba4daa45", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "35acbafb23598890b3bc991d858ed14e28442bd4f664c33e2f0c9abd3ef4fb07", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "94bd70b1-fb83-4c4e-ab35-b1a16a0234b7", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/remote_working.md", "file_name": "remote_working.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/remote_working.md"}, "hash": "97f0108a18ba0a7fd4a8731e9d978e1f2acf52213853f7b6379df155527fa5df", "class_name": "RelatedNodeInfo"}}, "text": "The work-life boundaries are less clear and some people struggle with lack of connection.\nHere we collate tips that the group has found to positively impact their wellbeing while working remotely.\nAt the beginning of the COVID-19 lockdowns, having wellbeing tips proved really useful, see below:\nFor more information on Wellbeing in REG, see the [Wellbeing page](https://mathison.turing.ac.uk/page/2157?SearchId=380431).\n\n- **Delineate the start and end of the working day.**\n  A huge challenge is separating the working day from life outside of work, especially if both happen in the same location.\n  Suggestions that might help bound work to working hours:\n  - Simulate a commute to allow your brain to switch off.\n    For example, go for a 20 minute walk, run, or read a book.\n  - Have separate _work_ clothes and _non-work_ clothes.\n  - Tidy your laptop/screen away if you are using the same space for relaxation.\n  - If possible use a different device for leisure activity to your work machine.\n- **Finish on time.**\n  Without the impetus of having colleagues around you clocking off it seems easy to work later than usual and eat into your relaxation time.\n  Take a 1hr lunch break, and finish on time.\n  If you are finding that you are repeatedly doing this then there is an issue with your workload that needs to be corrected.\n  Reach out!\n- **Talk**.\n  Many of us have missed the casual connection you get with being around people, and when you are working alone it is easy to forget the you are part of a team.\n  This point is just a reminder that you work in a team that genuinely cares about the wellbeing of every member of the group.", "start_char_idx": 3513, "end_char_idx": 5160, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a25ada4b-5845-43aa-9c0c-cfd763e4a100": {"__data__": {"id_": "a25ada4b-5845-43aa-9c0c-cfd763e4a100", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/snark_hunts.md", "file_name": "snark_hunts.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/snark_hunts.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c5690b32682b3b73911668d58fead0852a6cf8d6", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/snark_hunts.md", "file_name": "snark_hunts.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/snark_hunts.md"}, "hash": "21920f0cf8e9fd55e8ed20eb09e4a69e069548ce451b0793195c901e3922e59d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "fef485ab-1d16-4402-9c80-2d89b39599c8", "node_type": "1", "metadata": {}, "hash": "ded8548569f00d378d1d3485b0a49a9544e2d08b69a7a44e3048e1e142c13b8f", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Snark Hunts\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 1\n---\n\n# Snark Hunts\n\nA \"Snark Hunt\" is a project retrospective based around a facilitated discussion.\nThis is typically carried out during a meeting that lasts for approximately one hour.\nThe output of the meeting is a very short document which can be usefully re-read in the future.\nThe hope is that anyone reading it might be able to find useful learnings to apply to their own work.\n\nYou can see notes from all previous [Snark Hunts here](https://thealanturininstitute.sharepoint.com/:f:/s/ResearchEngineering/ElICSwf1rOJFj-ssePnMkOwBZmruJuRHnOKCSpC0M8QDUg?e=XGz6fl).\n\nWe're open to trying other ideas too!\n\n## How to set up a Snark Hunt\n\n- Find some people to take on the following roles\n  - **Facilitator:** Someone not on the project to facilitate the session\n  - **Scribe:** Someone else (also not on the project) to take notes and make these available after the session\n- Find a suitable date\n  - Ideally you want a date that multiple people who did **and** didn't work on the project can make\n- Invite people!\n  - As this is mainly a learning exercise for REG, you should invite the whole team\n  - Feel free to invite non-REG people if this seems appropriate - inviting the PI might make team members reticent to talk freely however!\n\n## Facilitator\n\n- Try to pursue questions almost to absurdity.\n- Prefer \"what\" and \"how\" questions over \"why\" questions wherever possible.", "start_char_idx": 0, "end_char_idx": 1537, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "fef485ab-1d16-4402-9c80-2d89b39599c8": {"__data__": {"id_": "fef485ab-1d16-4402-9c80-2d89b39599c8", "embedding": null, "metadata": {"file_path": "content/docs/how_we_work/snark_hunts.md", "file_name": "snark_hunts.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/snark_hunts.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c5690b32682b3b73911668d58fead0852a6cf8d6", "node_type": "4", "metadata": {"file_path": "content/docs/how_we_work/snark_hunts.md", "file_name": "snark_hunts.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/snark_hunts.md"}, "hash": "21920f0cf8e9fd55e8ed20eb09e4a69e069548ce451b0793195c901e3922e59d", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a25ada4b-5845-43aa-9c0c-cfd763e4a100", "node_type": "1", "metadata": {"file_path": "content/docs/how_we_work/snark_hunts.md", "file_name": "snark_hunts.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/how_we_work/snark_hunts.md"}, "hash": "107bb79bf20be17094a020cf41405579a8de684cb540dca0bbde47886d254bc6", "class_name": "RelatedNodeInfo"}}, "text": "We're open to trying other ideas too!\n\n## How to set up a Snark Hunt\n\n- Find some people to take on the following roles\n  - **Facilitator:** Someone not on the project to facilitate the session\n  - **Scribe:** Someone else (also not on the project) to take notes and make these available after the session\n- Find a suitable date\n  - Ideally you want a date that multiple people who did **and** didn't work on the project can make\n- Invite people!\n  - As this is mainly a learning exercise for REG, you should invite the whole team\n  - Feel free to invite non-REG people if this seems appropriate - inviting the PI might make team members reticent to talk freely however!\n\n## Facilitator\n\n- Try to pursue questions almost to absurdity.\n- Prefer \"what\" and \"how\" questions over \"why\" questions wherever possible.\n\nTake a look at the [example questions (on Sharepoint)](https://thealanturininstitute.sharepoint.com/:t:/s/ResearchEngineering/EXF9_xcvWk9LlqdJOuDLWgQBQOGYNfhnnJAux9UkQAaDNg?e=qlC0ev) for inspiration but don't feel constrained by them.\n\n## Scribe\n\n- Use this [note-taking template (on Sharepoint)](https://thealanturininstitute.sharepoint.com/:t:/s/ResearchEngineering/EdpY3Bk7AxtJjjrtqRAkxRkBUyc_yo0q0PFW6vJjbz7jVg?e=RXyEXE)\n- Attach names to people's questions and answers (unless they'd prefer to be anonymous).\n- Confirm after the meeting that all attendees are happy with your notes.\n- Add the completed notes to Sharepoint.\n\n## Note on naming\n\nThe name comes from [The Hunting of the Snark](https://en.wikipedia.org/wiki/The_Hunting_of_the_Snark), a Lewis Carroll poem about the crew of a boat who are hunting a Snark - a creature which may turn out to be a dangerous Boojum.", "start_char_idx": 727, "end_char_idx": 2419, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "25c15c1c-6042-434d-81ab-116505771d66": {"__data__": {"id_": "25c15c1c-6042-434d-81ab-116505771d66", "embedding": null, "metadata": {"file_path": "content/docs/join_us/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a719156971fa8ebb242749926fbf38be72a44d13", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/_index.md"}, "hash": "ff3b5b96521554666dea80f65a5eb9f74322dd1cd9d93f19d1da8847bf08da2c", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Join Us\"\nweight: 99\n---\n\n# Join Us\n\nThis section contains information about our recruitment process and what it's like to work with us.", "start_char_idx": 0, "end_char_idx": 147, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0009b835-6867-4034-a974-e0495b55ed3e": {"__data__": {"id_": "0009b835-6867-4034-a974-e0495b55ed3e", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b2588806-3af9-4149-a5a3-95083eb352c0", "node_type": "1", "metadata": {}, "hash": "6f9b41d584544c0f8766a4b25edf5a291bef6deac1c043e4bda7d88227695d69", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Recruitment FAQs\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# REG Recruitment FAQs\n\nHere you can find answers to the most frequently asked questions about working for the team and the recruitment process.\n\nQuestions are grouped by:\n\n- [Application Stage](#application-stage)\n- [Profile](#profile)\n- [Position](#position)\n- [Interview](#interview)\n- [Offer](#offer)\n\n## Application Stage\n\n### Is the position open to applicants outside of the UK who do not currently have a UK working Visa but could obtain one?\n\nYes, the positions are open to applicants outside the UK and our HR team ([HR@turing.ac.uk](mailto:HR@turing.ac.uk)) will be able to offer you additional information on the process of obtaining a working Visa.\n\n### Are you accepting remote applicants?\n\nOur team is currently trialling being remote-first and is minimising the amount of time people will be required to be in the office.\nRequirements for in-office attendance will depend on projects, but we will seek to match these to individual preferences wherever we can, and no team member will be asked to be in the office more than one day per week during the trial period.\nNote however that our members need to be based in the UK.\n\n### What would be the starting date?\n\nWe are keen to grow the team so would prefer if people can start within 3 months of an offer being made and would be happy for successful candidates to start sooner.\nHowever, we are looking to recruit multiple people and we are hiring for the long term, so we can support later starting dates if a successful candidate's circumstances require it.\n\n### How many open positions are available?", "start_char_idx": 0, "end_char_idx": 1964, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b2588806-3af9-4149-a5a3-95083eb352c0": {"__data__": {"id_": "b2588806-3af9-4149-a5a3-95083eb352c0", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, 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"hash": "ce3296a7dfbd7e835136fa03d40827adeec691e5ca189d736c2fe4e78bcad943", "class_name": "RelatedNodeInfo"}}, "text": "### Are you accepting remote applicants?\n\nOur team is currently trialling being remote-first and is minimising the amount of time people will be required to be in the office.\nRequirements for in-office attendance will depend on projects, but we will seek to match these to individual preferences wherever we can, and no team member will be asked to be in the office more than one day per week during the trial period.\nNote however that our members need to be based in the UK.\n\n### What would be the starting date?\n\nWe are keen to grow the team so would prefer if people can start within 3 months of an offer being made and would be happy for successful candidates to start sooner.\nHowever, we are looking to recruit multiple people and we are hiring for the long term, so we can support later starting dates if a successful candidate's circumstances require it.\n\n### How many open positions are available?\n\nWe are looking to grow the team by up to 10 people at Standard level in this recruitment round, with the timing of recruitment dependent on finding the right candidates, project demand and the rate at which we can effectively support new starters in joining the team.\n\n### If my application was unsuccessful in the past, am I allowed to apply again this time?\n\nIf you applied and did not get selected to interview you are welcome to apply again, but we suggest to revise your cover letter and CV and make them more in line with the requirements specified in the job ad.\n\nIf you applied and were unsuccessful at the interview stage, you are welcome to reapply if your profile has significantly progressed since the interview (new projects, job roles or other sources of experience).\nIn general, we suggest to wait for at least one year before applying again.\n\n### Does the Turing cover relocation and visa costs?\n\nYes.\nThe Turing recognises that relocating and applying for a visa have different associated costs and therefore will provide support for both.\nAll claims should be submitted within a year of the employment start date to Finance.\nFor existing staff, visa claims should be submitted within one year of the visa renewal or amendment.", "start_char_idx": 1059, "end_char_idx": 3210, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "16330a08-ad08-496f-a14e-a56c8d8495eb": {"__data__": {"id_": "16330a08-ad08-496f-a14e-a56c8d8495eb", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "b2588806-3af9-4149-a5a3-95083eb352c0", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "529cdb249f6d69ea56a2a437945d5703bcbd66d55240a64c159519e626c2bb92", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "d2150194-dd47-4b24-90d6-24a9ec1930e2", "node_type": "1", "metadata": {}, "hash": "efb39d8cb2f56738f69a29e3af8ed05a8e65c0cd1167b8bdd2b35ce5ad83405e", "class_name": "RelatedNodeInfo"}}, "text": "### If my application was unsuccessful in the past, am I allowed to apply again this time?\n\nIf you applied and did not get selected to interview you are welcome to apply again, but we suggest to revise your cover letter and CV and make them more in line with the requirements specified in the job ad.\n\nIf you applied and were unsuccessful at the interview stage, you are welcome to reapply if your profile has significantly progressed since the interview (new projects, job roles or other sources of experience).\nIn general, we suggest to wait for at least one year before applying again.\n\n### Does the Turing cover relocation and visa costs?\n\nYes.\nThe Turing recognises that relocating and applying for a visa have different associated costs and therefore will provide support for both.\nAll claims should be submitted within a year of the employment start date to Finance.\nFor existing staff, visa claims should be submitted within one year of the visa renewal or amendment.\nMore information can be found on [Mathison](https://mathison.turing.ac.uk/Interact/Pages/Section/ContentListing.aspx?subsection=4141&utm_source=interact&utm_medium=quick_search&utm_term=relocation).\n\n## Profile\n\n### Are there particular domains or skill sets that you are interested in for these roles?\n\nWe are really open to any domain, area of expertise, skill set that is broadly related to research data science or research software engineering.\n\nIf you have experience or are interested in High Performance Computing, as part of the open positions, we are actively looking for new members of the team to work on that area as Research Computing Engineers.\nYou can drop an email to the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) to learn more about it.\n\nHowever, we are recruiting for multiple positions, and are interested in a wide range of skills and experience, so please do apply if you are interested and have experience relevant to research software engineering and data science, broadly defined.", "start_char_idx": 2235, "end_char_idx": 4275, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d2150194-dd47-4b24-90d6-24a9ec1930e2": {"__data__": {"id_": "d2150194-dd47-4b24-90d6-24a9ec1930e2", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "16330a08-ad08-496f-a14e-a56c8d8495eb", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "b3d5e8c12879ebcf707bcf3b0976814f53cf79c7acc06b94bdb99203d52b4f75", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2e92bb78-357e-455b-ab5c-e1e12e4b2da4", "node_type": "1", "metadata": {}, "hash": "3e7f5c2b90d79c0b343fd5aceb1efb806521ca43192e5857d226597ea51aaa5c", "class_name": "RelatedNodeInfo"}}, "text": "## Profile\n\n### Are there particular domains or skill sets that you are interested in for these roles?\n\nWe are really open to any domain, area of expertise, skill set that is broadly related to research data science or research software engineering.\n\nIf you have experience or are interested in High Performance Computing, as part of the open positions, we are actively looking for new members of the team to work on that area as Research Computing Engineers.\nYou can drop an email to the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) to learn more about it.\n\nHowever, we are recruiting for multiple positions, and are interested in a wide range of skills and experience, so please do apply if you are interested and have experience relevant to research software engineering and data science, broadly defined.\nYou can see the [range of projects](https://www.turing.ac.uk/research/research-engineering/reg-projects) the team works on and [backgrounds of current team members](https://www.turing.ac.uk/research/research-engineering/meet-the-team) on our website.", "start_char_idx": 3411, "end_char_idx": 4526, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2e92bb78-357e-455b-ab5c-e1e12e4b2da4": {"__data__": {"id_": "2e92bb78-357e-455b-ab5c-e1e12e4b2da4", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "d2150194-dd47-4b24-90d6-24a9ec1930e2", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "317d7fd3d293f42ee9660f6db00005d9b1bf2b61fe52db3364a32f05dc3d1453", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "bc877115-e3e0-4648-bef9-5e10d81a353d", "node_type": "1", "metadata": {}, "hash": "a1fd97803f7e64866be25b7105f358d84526bc62ba27feb64ce7ed7d40bf96d0", "class_name": "RelatedNodeInfo"}}, "text": "If you have experience or are interested in High Performance Computing, as part of the open positions, we are actively looking for new members of the team to work on that area as Research Computing Engineers.\nYou can drop an email to the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) to learn more about it.\n\nHowever, we are recruiting for multiple positions, and are interested in a wide range of skills and experience, so please do apply if you are interested and have experience relevant to research software engineering and data science, broadly defined.\nYou can see the [range of projects](https://www.turing.ac.uk/research/research-engineering/reg-projects) the team works on and [backgrounds of current team members](https://www.turing.ac.uk/research/research-engineering/meet-the-team) on our website.\n\nSome of the team have also been featured in project and institute spotlight interviews and the [Turing podcast](https://www.podbean.com/media/share/pb-vrci3-1366a21?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share) (including episodes on [AI for the Skies](https://www.podbean.com/media/share/pb-zzidx-d7bb24?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share), [Fantasy Football AI](https://www.podbean.com/media/share/pb-5afja-df2052?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share), [3D printed bridges](https://www.podbean.com/media/share/pb-qekh3-dfe740?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share), [Reproducible data science](https://www.podbean.com/media/share/pb-wxkjp-ecfa7f?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share), [The problems of AI](https://www.podbean.com/media/share/pb-dyq36-112350b?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share) and [Living with Machines](https://www.podbean.com/media/share/pb-s9das-1275eb2?utm_campaign=w_share_ep&utm_medium=dlink&utm_source=w_share)).", "start_char_idx": 3662, "end_char_idx": 5600, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "bc877115-e3e0-4648-bef9-5e10d81a353d": {"__data__": {"id_": "bc877115-e3e0-4648-bef9-5e10d81a353d", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2e92bb78-357e-455b-ab5c-e1e12e4b2da4", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "4470c35c7c66c550e06aaa20d17896b0c59517e7f1c36c9dd2d24e60ddd5fc0e", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "939e982a-94d2-49b3-b029-926d0ad796e3", "node_type": "1", "metadata": {}, "hash": "6c66b76e1f552bb07e36fd760e15690506737a504bde69a8cb5a8efba9f6b141", "class_name": "RelatedNodeInfo"}}, "text": "### What is a Research Computing Engineer?\n\nThis role presents a new exciting opportunity to join the growing team of research computing experts at The Turing. The team assists the Turing community with designing, building, deploying, and maintaining research infrastructures, as well as providing service delivery and user support for the Institute\u2019s research computing platforms. This is an opportunity for a technically-skilled professional with a good understanding of either cloud or high-performance computing (HPC), experience with both application and infrastructure, and an interest in facilitating and enhancing world-leading data science and AI research.\nYou will build tools and automation to eliminate mundane operations and craft repeatable processes. As part of the team, you will also work with other research software engineers and data scientists on various projects and will participate in all phases of cloud / HPC development, from requirements-capture, definition, and design; to development, deployment, and maintenance; to performance tuning and monitoring. You are also expected to keep up-to-date with the latest cutting-edge developments, and/or make use of the UK\u2019s most advanced computing facilities like Dawn, ARCHER 2, JADE 2, and Baskerville.\n\n### Should the cover letter address each of the skills and requirements in the person specification section of the job description?\n\nIt is important that the cover letter, in conjunction with the CV, highlights the requirements listed as \"check at Application\" on the Person Specification, and shows a (general) understanding of the role of a research data scientist or software engineer.\nAlso, it is important to highlight:\n\n- Your past experience working with code and/or data\n- Why you would like to become part of the Research Engineering Group\n- How your skill set would complement the activities of the team.\n\n### What does \"equivalent professional experience\" mean?\n\nIf you do not have a PhD degree but have worked for a few years and believe you have gained the essential skills mentioned in the Person Specification, we encourage you to apply!\n\nSuch experience might include exposure to research projects, awareness of the importance of reproducible software and tools, working with or leading a team of people with different skill sets, deciding on how to best achieve an objective from a data science perspective, or presenting advice/results to an audience.", "start_char_idx": 5602, "end_char_idx": 8047, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "939e982a-94d2-49b3-b029-926d0ad796e3": {"__data__": {"id_": "939e982a-94d2-49b3-b029-926d0ad796e3", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "bc877115-e3e0-4648-bef9-5e10d81a353d", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "40ee36de8a860439791a764bd78ec4c4b73a5f159013213c6dda409c632418ae", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "655b725c-a52f-4e96-93c4-361714c23830", "node_type": "1", "metadata": {}, "hash": "83fbab7bb0a048102ba97ee9431dc3f5ac9816b630d822df1b6969375f821baa", "class_name": "RelatedNodeInfo"}}, "text": "It is important that the cover letter, in conjunction with the CV, highlights the requirements listed as \"check at Application\" on the Person Specification, and shows a (general) understanding of the role of a research data scientist or software engineer.\nAlso, it is important to highlight:\n\n- Your past experience working with code and/or data\n- Why you would like to become part of the Research Engineering Group\n- How your skill set would complement the activities of the team.\n\n### What does \"equivalent professional experience\" mean?\n\nIf you do not have a PhD degree but have worked for a few years and believe you have gained the essential skills mentioned in the Person Specification, we encourage you to apply!\n\nSuch experience might include exposure to research projects, awareness of the importance of reproducible software and tools, working with or leading a team of people with different skill sets, deciding on how to best achieve an objective from a data science perspective, or presenting advice/results to an audience.\nWe would like to hear about your prior experience in your cover letter.\nIf you prefer, feel free to get in touch with the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) about this point.\n\n### Would not having a PhD prevent me from career progression, in terms of becoming a Senior Research Data Scientist/Research Software Engineer at the Turing Institute?\n\nAbsolutely not, our internal career progression is based on internal milestones and contributions to the group and the Turing community - do not worry about this at all.\n\n## Position\n\n### What is the difference between a Junior and a Standard role?\n\nThe main difference between the Junior and Standard roles is that the Junior is a training position for becoming (in 1-2 years) a Standard research data scientist or software engineer.\nWe consider candidates for the Standard position if they have a PhD or an equivalent working experience (meaning for instance a few years of work in a data scientist or software engineer position, in close contact with research projects).\nIf you think you have enough experience you can apply to the Standard position and stress this in your cover letter.", "start_char_idx": 7011, "end_char_idx": 9250, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "655b725c-a52f-4e96-93c4-361714c23830": {"__data__": {"id_": "655b725c-a52f-4e96-93c4-361714c23830", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "939e982a-94d2-49b3-b029-926d0ad796e3", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "a65543ec490d355a2d8c5f70b787d19827f299378f3615127071344f87cbd737", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "9263bd46-63d4-4010-9ef1-ebeb7c96e88f", "node_type": "1", "metadata": {}, "hash": "7aef05cc51e5f8dd74edbb62662b633d327a0e5b68b16cd261c6d814967d6e55", "class_name": "RelatedNodeInfo"}}, "text": "### Would not having a PhD prevent me from career progression, in terms of becoming a Senior Research Data Scientist/Research Software Engineer at the Turing Institute?\n\nAbsolutely not, our internal career progression is based on internal milestones and contributions to the group and the Turing community - do not worry about this at all.\n\n## Position\n\n### What is the difference between a Junior and a Standard role?\n\nThe main difference between the Junior and Standard roles is that the Junior is a training position for becoming (in 1-2 years) a Standard research data scientist or software engineer.\nWe consider candidates for the Standard position if they have a PhD or an equivalent working experience (meaning for instance a few years of work in a data scientist or software engineer position, in close contact with research projects).\nIf you think you have enough experience you can apply to the Standard position and stress this in your cover letter.\nYou can also apply to the Junior position and mention in your cover letter that you are also considering the Standard position, in case you feel your profile or working experience is in between the two.\nFeel free to get in touch with the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) about this point.\n\n### For the Junior post it states as Essential: \"Fluency in one or more modern programming languages used in research in data science and artificial intelligence\". In my case I am familiar with X, would my computing knowledge satisfy this criteria?\n\nDepending on the project, we work with different programming languages and different settings, so more than the programming languages you currently know the important aspect for us is the willingness and ability to pick up new languages along the way, based on your previous experience (especially in research and data analysis contexts).\n\n### Given the job profile in the REG team, what do REG members tend to transition to (for eg. Academics, or Industry, or something else)?\n\nWe have only had a few members of the team who have moved on with their career trajectory so far.\nTo read about some of their career paths you can visit our [REG alumni page](https://www.turing.ac.uk/research/research-engineering/reg-alumni).", "start_char_idx": 8290, "end_char_idx": 10580, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9263bd46-63d4-4010-9ef1-ebeb7c96e88f": {"__data__": {"id_": "9263bd46-63d4-4010-9ef1-ebeb7c96e88f", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "655b725c-a52f-4e96-93c4-361714c23830", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "b7fe511968fee4bf03eb1f24c59cc8593036cb5f3e7531fe90819f3dbcb9721e", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5d83bd6f-d55e-4a3d-b92a-95d2730cec7e", "node_type": "1", "metadata": {}, "hash": "945e0ac95a8bcd009dd3d012ee9754258f91681ff7fb49d6c2ca46bd19cee3f1", "class_name": "RelatedNodeInfo"}}, "text": "### For the Junior post it states as Essential: \"Fluency in one or more modern programming languages used in research in data science and artificial intelligence\". In my case I am familiar with X, would my computing knowledge satisfy this criteria?\n\nDepending on the project, we work with different programming languages and different settings, so more than the programming languages you currently know the important aspect for us is the willingness and ability to pick up new languages along the way, based on your previous experience (especially in research and data analysis contexts).\n\n### Given the job profile in the REG team, what do REG members tend to transition to (for eg. Academics, or Industry, or something else)?\n\nWe have only had a few members of the team who have moved on with their career trajectory so far.\nTo read about some of their career paths you can visit our [REG alumni page](https://www.turing.ac.uk/research/research-engineering/reg-alumni).\nSome of them moved to a traditional academic position (for instance pursuing a PhD, taking a professorship), and others to industry and government positions.\nOthers have progressed to more senior roles within the team.\n\n### Would the candidate be able to bring their own projects with them and/or seek funding while in the role ?\n\nThe team's role at the Turing is to collaborate with institute researchers to support their research agendas, rather than pursuing our own.\nIt is much, much more usual for team members to be working on other people's projects than their own.\nIf you would like to prioritise pursuing your own research agenda at the Turing there are [other types of positions](https://www.turing.ac.uk/opportunities-turing) at the Institute that allow you to do this.\n\nHowever, we are keen to support team members in pursuing their careers, including developing and leading projects.\nWe would generally expect these to be projects that would be a sufficiently good fit for the team and the Turing that we would take them on even if the particular team member was not involved.\n\nWe would also expect team members that do lead their own projects to continue to work collaboratively with others in REG and the Institute.", "start_char_idx": 9609, "end_char_idx": 11811, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5d83bd6f-d55e-4a3d-b92a-95d2730cec7e": {"__data__": {"id_": "5d83bd6f-d55e-4a3d-b92a-95d2730cec7e", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "9263bd46-63d4-4010-9ef1-ebeb7c96e88f", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "d2b89aae551c1edbbee3e3a80619c97fd7911beb5c38117dfc738a252366ea46", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "214b63dc-17e6-4093-9efe-b50dac8331a0", "node_type": "1", "metadata": {}, "hash": "894b37322b4233678c148ff8e532bf4381accd0ecce14b33e58d9c65495fcb43", "class_name": "RelatedNodeInfo"}}, "text": "### Would the candidate be able to bring their own projects with them and/or seek funding while in the role ?\n\nThe team's role at the Turing is to collaborate with institute researchers to support their research agendas, rather than pursuing our own.\nIt is much, much more usual for team members to be working on other people's projects than their own.\nIf you would like to prioritise pursuing your own research agenda at the Turing there are [other types of positions](https://www.turing.ac.uk/opportunities-turing) at the Institute that allow you to do this.\n\nHowever, we are keen to support team members in pursuing their careers, including developing and leading projects.\nWe would generally expect these to be projects that would be a sufficiently good fit for the team and the Turing that we would take them on even if the particular team member was not involved.\n\nWe would also expect team members that do lead their own projects to continue to work collaboratively with others in REG and the Institute. We would therefore not expect people to be bought out more than 50% on their own project and would expect the project to involve other members of the team or the Institute.\n\nNevertheless, while a permanent role within REG is not meant for pursuing an independent research agenda, we highly encourage initiatives and are open to hear new ideas and contributions.\nSo, if you think your project would benefit our group and the Turing (and vice-versa) you should highlight it in your cover letter.\n\n## Interview\n\n### Could you describe the interview process?\n\nAs we mentioned in the job description, all interviews are currently held remotely.\n\nWe operate a two-stage interview process. If you are successful at the screening stage, you will be asked to attend the first interview via video call.\nIn this interview, you will be expected to give a ten-minute presentation on code you have written that either demonstrates an algorithm that you consider important in data science or illustrates your use of good research software engineering practices.\nExisting examples of work are encouraged as long as the code was substantially written by you.\nYou should be prepared to answer questions about both the code and the research challenge it addresses or the algorithm it demonstrates.\nAny source code shared for the interview will be treated in the strictest of confidence.", "start_char_idx": 10801, "end_char_idx": 13179, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "214b63dc-17e6-4093-9efe-b50dac8331a0": {"__data__": {"id_": "214b63dc-17e6-4093-9efe-b50dac8331a0", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5d83bd6f-d55e-4a3d-b92a-95d2730cec7e", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "d079d79bc73a60da75152728c79f9bfa0bf24dc9168d518dfb62057688c0416c", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "d04e9b9a-1e28-42c4-ac2a-e59e84cf019b", "node_type": "1", "metadata": {}, "hash": "8a7f1aa5dd30b90edbfed60a88b008d992b4306dcf47e1f4f7fc62f48f44793e", "class_name": "RelatedNodeInfo"}}, "text": "So, if you think your project would benefit our group and the Turing (and vice-versa) you should highlight it in your cover letter.\n\n## Interview\n\n### Could you describe the interview process?\n\nAs we mentioned in the job description, all interviews are currently held remotely.\n\nWe operate a two-stage interview process. If you are successful at the screening stage, you will be asked to attend the first interview via video call.\nIn this interview, you will be expected to give a ten-minute presentation on code you have written that either demonstrates an algorithm that you consider important in data science or illustrates your use of good research software engineering practices.\nExisting examples of work are encouraged as long as the code was substantially written by you.\nYou should be prepared to answer questions about both the code and the research challenge it addresses or the algorithm it demonstrates.\nAny source code shared for the interview will be treated in the strictest of confidence.\n\nThe second interview is usually held about one week later for successful candidates. This interview focusses on your previous experience and competencies for the role.\nThere will also be a problem-solving discussion with the interview panel, where you may want to use paper and pen to arrive at an understanding of a proposed data analysis challenge.\n\nIn both interviews, there will be the opportunity to ask questions about the role and the team.\n\n### Does the algorithm need to be written entirely by me?\n\nIn the first interview we expect you to demonstrate your ability to write code for addressing a data science task in a reproducible manner and/or to use good software engineering practices for developing research software.\nThis could be done by presenting a data analysis script or piece of research software written entirely by you, or by presenting a larger piece of research software you have made a significant contribution to.\nUsing existing libraries that implement algorithms you are using is fine (and good practice), but in all cases there must be a significant amount of code you have written yourself, it must be clear what code you have written yourself, and we expect you to talk about and answer questions on:\n\n1. the code you have written,\n1. the underlying algorithms or methods your code is implementing or using, and\n1. the research domain or question for which you are applying them.", "start_char_idx": 12174, "end_char_idx": 14591, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d04e9b9a-1e28-42c4-ac2a-e59e84cf019b": {"__data__": {"id_": "d04e9b9a-1e28-42c4-ac2a-e59e84cf019b", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "214b63dc-17e6-4093-9efe-b50dac8331a0", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "b64ddc0259b3a7279dc154f598b9e4379071a28ffaed531d75b4d094b95d1bc4", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f4a27cf4-d22f-47b2-bd5e-7cfd3cf286e4", "node_type": "1", "metadata": {}, "hash": "0530099152761ac95be599b7df8ec22ede22f488e7f4ded297d3c2eb21d8a340", "class_name": "RelatedNodeInfo"}}, "text": "In both interviews, there will be the opportunity to ask questions about the role and the team.\n\n### Does the algorithm need to be written entirely by me?\n\nIn the first interview we expect you to demonstrate your ability to write code for addressing a data science task in a reproducible manner and/or to use good software engineering practices for developing research software.\nThis could be done by presenting a data analysis script or piece of research software written entirely by you, or by presenting a larger piece of research software you have made a significant contribution to.\nUsing existing libraries that implement algorithms you are using is fine (and good practice), but in all cases there must be a significant amount of code you have written yourself, it must be clear what code you have written yourself, and we expect you to talk about and answer questions on:\n\n1. the code you have written,\n1. the underlying algorithms or methods your code is implementing or using, and\n1. the research domain or question for which you are applying them.\n\n### Can you give any more detail on what the panel expect from the algorithm which will be presented - does it need to meet any certain criteria, for example have a certain level of complexity?\n\nIn the first interview we expect you to demonstrate your ability to write code for addressing a data science task and/or to use good research software engineering practices for ensuring research reproducibility.\nThis could be done by presenting for instance a data analysis script written by you, where you would describe us the implementation and answer some general questions.\nThe adopted algorithms can be ones available in existing software libraries, integrated for instance in a data analysis workflow, but they can also be written by you if you prefer.\n\nWe will not evaluate you on the complexity of the algorithm, but in your understanding of its implementation, awareness of its potential downfall and its overall appropriateness to solve the problem you are trying to address.\n\n### Will I be only explaining the algorithm / analysis / software, or running it and discussing the results?\n\nYou will be presenting your chosen analysis, algorithm or software and its implementation, but you should be able to talk about the results of the analysis or the performance of the software.", "start_char_idx": 13533, "end_char_idx": 15877, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f4a27cf4-d22f-47b2-bd5e-7cfd3cf286e4": {"__data__": {"id_": "f4a27cf4-d22f-47b2-bd5e-7cfd3cf286e4", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "d04e9b9a-1e28-42c4-ac2a-e59e84cf019b", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "2402221722e5467380571b520d084f96e19459f0b3508c3bd2f0792871053a34", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "4139f092-9cd1-46db-ba5a-5a5759ae4bb1", "node_type": "1", "metadata": {}, "hash": "c429fa01bfc9c746c6c6932cab6d7fcfca1314b29f0107a2d1555bf7a573e211", "class_name": "RelatedNodeInfo"}}, "text": "In the first interview we expect you to demonstrate your ability to write code for addressing a data science task and/or to use good research software engineering practices for ensuring research reproducibility.\nThis could be done by presenting for instance a data analysis script written by you, where you would describe us the implementation and answer some general questions.\nThe adopted algorithms can be ones available in existing software libraries, integrated for instance in a data analysis workflow, but they can also be written by you if you prefer.\n\nWe will not evaluate you on the complexity of the algorithm, but in your understanding of its implementation, awareness of its potential downfall and its overall appropriateness to solve the problem you are trying to address.\n\n### Will I be only explaining the algorithm / analysis / software, or running it and discussing the results?\n\nYou will be presenting your chosen analysis, algorithm or software and its implementation, but you should be able to talk about the results of the analysis or the performance of the software.\nIt would be useful to be able to run the code to generate results, but we recognise this is not always reasonable in an interview setting. If this is not not feasible then you should bring some previously generated results so that we can have a conversation around inputs and outputs.\nThe discussion will likely touch on the performance of the algorithm in terms of e.g. speed, accuracy or range of applicability, but our goal is to evaluate your understanding of the algorithm, analysis or software and its implementation rather than to assess you on the performance of your particular implementation.\n\n### Should the presentation be verbal only or should I produce some materials alongside it - would the committee prefer presentation directly from an IDE, or using a presentation software?\n\nYou should be able to show and (potentially) run the code (this could be using Jupyter notebooks, Rmarkdown, a mix of an IDE and the command line).\nYou are welcome to include any other material that could be useful for supporting your presentation.\n\n### What will the problem solving aspect of the second interview involve? Will I be asked about particular data science or machine learning approaches? Will I need to write code or explain the details of an algorithm's implementation?\n\nIn the second interview we expect you to discuss a question related to the analysis of an example dataset.", "start_char_idx": 14788, "end_char_idx": 17264, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "4139f092-9cd1-46db-ba5a-5a5759ae4bb1": {"__data__": {"id_": "4139f092-9cd1-46db-ba5a-5a5759ae4bb1", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f4a27cf4-d22f-47b2-bd5e-7cfd3cf286e4", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "b9c4835a8b10cbb48e3133fb27b2363a6fefe49d1012a69c9c403f7a67139dde", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2c693769-1efc-4102-8d1e-253fcede566b", "node_type": "1", "metadata": {}, "hash": "78b9f5aee22adc7dc0091b95a335e28ae85314b8814a3199fba3311580fbe4d6", "class_name": "RelatedNodeInfo"}}, "text": "speed, accuracy or range of applicability, but our goal is to evaluate your understanding of the algorithm, analysis or software and its implementation rather than to assess you on the performance of your particular implementation.\n\n### Should the presentation be verbal only or should I produce some materials alongside it - would the committee prefer presentation directly from an IDE, or using a presentation software?\n\nYou should be able to show and (potentially) run the code (this could be using Jupyter notebooks, Rmarkdown, a mix of an IDE and the command line).\nYou are welcome to include any other material that could be useful for supporting your presentation.\n\n### What will the problem solving aspect of the second interview involve? Will I be asked about particular data science or machine learning approaches? Will I need to write code or explain the details of an algorithm's implementation?\n\nIn the second interview we expect you to discuss a question related to the analysis of an example dataset.\nAll information required to answer the question will be given to you in beginning of the interview.\n**No preparation or specific technical knowledge is expected or required.**\nThe goal of this exercise is not to test your understanding of any particular algorithm or data science method, but rather to assess your general approach to tackling a data science question.\n\nThis exercise will not require you to write any code or explain the details of any algorithm.\nThe exercise isn't assessed on whether or not you know the \"right\" answer to a particular data science question, but on the process you go through to understand the question and the data and your reasoning for the approaches you suggest.\nTaking some time to think through the question is fine, as is asking questions to the interviewers.\nConsider them as subject matter experts coming to you to answer a question using their data.\n\n### Is there anything in particular I should prepare for the problem solving interview?\n\nNothing at all.\nThe best thing you could do to be prepared is to eat well, take some long walks far away from the computer and get a good night's sleep.\n\n## Offer\n\n### What are the reasons of your job offer? Is the salary negotiable?", "start_char_idx": 16249, "end_char_idx": 18482, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2c693769-1efc-4102-8d1e-253fcede566b": {"__data__": {"id_": "2c693769-1efc-4102-8d1e-253fcede566b", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "4139f092-9cd1-46db-ba5a-5a5759ae4bb1", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "618473b8aefa0a020e66408ec70d4f19b9b8143a1f1de7762301b2ac5c6a19ce", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "202a5ff9-6f89-4301-a80f-0577d6989b87", "node_type": "1", "metadata": {}, "hash": "ab09cd9fc89db47aefb9d5ea23a1c65156f3140d7da95edc86695b74d5798f4f", "class_name": "RelatedNodeInfo"}}, "text": "This exercise will not require you to write any code or explain the details of any algorithm.\nThe exercise isn't assessed on whether or not you know the \"right\" answer to a particular data science question, but on the process you go through to understand the question and the data and your reasoning for the approaches you suggest.\nTaking some time to think through the question is fine, as is asking questions to the interviewers.\nConsider them as subject matter experts coming to you to answer a question using their data.\n\n### Is there anything in particular I should prepare for the problem solving interview?\n\nNothing at all.\nThe best thing you could do to be prepared is to eat well, take some long walks far away from the computer and get a good night's sleep.\n\n## Offer\n\n### What are the reasons of your job offer? Is the salary negotiable?\n\nFairness and transparency are key values for us and our goal is to avoid inequity by ensuring we are paying people the same salary for the same level of contribution to the team.\n**Our offers are not negotiable** and set by considering the level and applicability of each candidate\u2019s experience relative to their future peers already working in the team.\n\nWe have annual pay reviews, where we actively review the performance and development of each team member against the expectations of their position within their seniority band and make salary adjustments where these expectations are being exceeded.\nAdditionally, the team is growing and there will be opportunities for people to progress in their career.\n\nBecoming a member of REG will also bring the following benefits:\n\n- Flexible working: we are currently trialling being a \u201cremote-first\u201d group for the entire decision-making process, but as the office is now fully re-opened members are free to choose how often they come into the office.\n\n- The opportunity to work and collaborate on cutting-edge data science projects with high-profile institutions, but with the benefits of working in a full-time position (better than a post-doc, better than many data science roles in industry).\n\n- Strong input into which projects you work on.\n  We value providing the experience of learning something new in a collaborative setting, and prioritise staffing people onto projects they would like to work on rather than maximising the fit to people's prior experience.", "start_char_idx": 17634, "end_char_idx": 19999, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "202a5ff9-6f89-4301-a80f-0577d6989b87": {"__data__": {"id_": "202a5ff9-6f89-4301-a80f-0577d6989b87", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2c693769-1efc-4102-8d1e-253fcede566b", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "7bbfd649e7ed062217b812ab04ce9ed86561b64963028e4429768c7e0eed4658", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "2977a99a-b5bc-4548-8003-77766a64b591", "node_type": "1", "metadata": {}, "hash": "97a8d311c8d3357f752e8172fc8f7df54543d526e82f727891f1baa2249ede45", "class_name": "RelatedNodeInfo"}}, "text": "Additionally, the team is growing and there will be opportunities for people to progress in their career.\n\nBecoming a member of REG will also bring the following benefits:\n\n- Flexible working: we are currently trialling being a \u201cremote-first\u201d group for the entire decision-making process, but as the office is now fully re-opened members are free to choose how often they come into the office.\n\n- The opportunity to work and collaborate on cutting-edge data science projects with high-profile institutions, but with the benefits of working in a full-time position (better than a post-doc, better than many data science roles in industry).\n\n- Strong input into which projects you work on.\n  We value providing the experience of learning something new in a collaborative setting, and prioritise staffing people onto projects they would like to work on rather than maximising the fit to people's prior experience.\n  Our group is made up of people with a wide range of skills and experiences and we generally find we can put together project teams with a suitable mix of skills from those who are enthusiastic about each project.\n\n- Formal support for professional development via learning and development plans each team member develops with their line manager during the probation process, annual objective setting and regular 1-2-1s.\n  These learning plans feed into the projects people are allocated to and the roles and responsibilities they undertake within these.\n\n- Support to team members with their development outside of projects, providing 20% time that is split evenly between self-directed development and getting involved in one of the group's or institute's service areas.\n  We support team members to attend conferences, workshops and courses, and there are many seminar series and interest groups at the Turing that team members are encouraged to get involved with, including the team\u2019s own weekly [tech talks]({{< relref \"docs/regular_events/lunchtime_tech_talks.md\" >}}).\n  We also support team members making contributions to the wider ecosystem as part of their 20% time.", "start_char_idx": 19089, "end_char_idx": 21178, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2977a99a-b5bc-4548-8003-77766a64b591": {"__data__": {"id_": "2977a99a-b5bc-4548-8003-77766a64b591", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, 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"hash": "23e3b2031ab2b13689d42bccc5f9760ce76a113c6042295bdf31945ff5c16772", "class_name": "RelatedNodeInfo"}}, "text": "- Formal support for professional development via learning and development plans each team member develops with their line manager during the probation process, annual objective setting and regular 1-2-1s.\n  These learning plans feed into the projects people are allocated to and the roles and responsibilities they undertake within these.\n\n- Support to team members with their development outside of projects, providing 20% time that is split evenly between self-directed development and getting involved in one of the group's or institute's service areas.\n  We support team members to attend conferences, workshops and courses, and there are many seminar series and interest groups at the Turing that team members are encouraged to get involved with, including the team\u2019s own weekly [tech talks]({{< relref \"docs/regular_events/lunchtime_tech_talks.md\" >}}).\n  We also support team members making contributions to the wider ecosystem as part of their 20% time.\n  Examples include contributing to external [Open Source Hacksessions]({{< relref \"docs/regular_events/open_source_hacksessions.md\" >}}) and contributing to community groups like the [Society for Research Software Engineering]({{< relref \"docs/onboarding/society_of_research_software_engineering.md\" >}}).\n\n- The opportunity to get involved in non-project work through one of the team's service areas, some of which support the running of the team and some of which support the wider research community at the institute (for instance Recruitment, Planning and finance, Training, EDI, Communications, Research computing support, Research programme liaison).\n  Some service areas require significant support and will have core team members who have chosen these service areas instead of a full project workload, but all team members have a 10% allocation to volunteer for a service area of their choice.\n\n- The opportunity to contribute to how the group works and grows.\n  We run the team in an open, consultative and collaborative manner and almost all of our decision making on how we organise ourselves and develop the team is done in the open on our group GitHub with the opportunity for anyone in the team to contribute.", "start_char_idx": 20216, "end_char_idx": 22402, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9950c23a-f73d-423f-b120-fc886fdc8843": {"__data__": {"id_": "9950c23a-f73d-423f-b120-fc886fdc8843", "embedding": null, "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "334a4c021df569ebc7001eeac252ffa542373d53", "node_type": "4", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "76b4a5af0f7b39e5ab11bf6cf1be1966d7c3d7a323e60a4951bb4ccd5ef0d900", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2977a99a-b5bc-4548-8003-77766a64b591", "node_type": "1", "metadata": {"file_path": "content/docs/join_us/recruitment_FAQs.md", "file_name": "recruitment_FAQs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/join_us/recruitment_FAQs.md"}, "hash": "7c93e6b9a52d97e1a6e4311a2c634963c7a484c3af2466cfbf4d8ff603406585", "class_name": "RelatedNodeInfo"}}, "text": "- The opportunity to get involved in non-project work through one of the team's service areas, some of which support the running of the team and some of which support the wider research community at the institute (for instance Recruitment, Planning and finance, Training, EDI, Communications, Research computing support, Research programme liaison).\n  Some service areas require significant support and will have core team members who have chosen these service areas instead of a full project workload, but all team members have a 10% allocation to volunteer for a service area of their choice.\n\n- The opportunity to contribute to how the group works and grows.\n  We run the team in an open, consultative and collaborative manner and almost all of our decision making on how we organise ourselves and develop the team is done in the open on our group GitHub with the opportunity for anyone in the team to contribute.\n\n- [Other benefits](https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits) include private healthcare and a 13% contribution to a defined contribution pension scheme (3% from the employee, 10% from the Turing).\n\n## My question is not answered here\n\nPlease contact the REG recruitment team ([reg-recruitment-info@turing.ac.uk](mailto:reg-recruitment-info@turing.ac.uk)) with any additional questions you may have.\nWe also run monthly drop-in sessions where you can meet some of the current team and ask them questions face to face.\nThese sessions will be hosted by team members who won't be involved in that month's interview process, and questions you ask at these sessions will have no influence on how your application is treated.\nTo be informed of the details for these drop-in sessions, please register for the team's [recruitment newsletter](https://tinyletter.com/turing-reg-recruitment).", "start_char_idx": 21486, "end_char_idx": 23314, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "35db1781-d829-46cd-b2df-e30df000dc0d": {"__data__": {"id_": "35db1781-d829-46cd-b2df-e30df000dc0d", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8da5941d9e553fec1734e42037111a37668471e4", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/_index.md"}, "hash": "77c87947a74280ecae4e0af0bb9d29567f10057c0407b4729b48df3572204ba0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Onboarding\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Onboarding\n\nThis section contains information about onboarding process for new members of REG.\n\nOnboardees: Please go through these pages as one of the very first things when you join.\n\n- [Buddy system]({{< relref \"/docs/onboarding/buddy_system.md\" >}})\n- [New Joiners]({{< relref \"/docs/onboarding/new_joiners/_index.md\" >}})\n\nOnboarders: Please see the following wiki page for everything you need to know about becoming an onboarder.\n\n- [REG Onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/Onboarding)", "start_char_idx": 0, "end_char_idx": 932, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9ef54c37-488c-41f4-b826-cf988eb9e138": {"__data__": {"id_": "9ef54c37-488c-41f4-b826-cf988eb9e138", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3eb1917b4ebf3c304d06bf25d131ead26c9856bb", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "hash": "dcd7c76f633d17ec3fbae2ab77dac71679ce12d56af6326da3fd3de2e78e99aa", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "584f6865-e808-41c0-8032-05e75d746feb", "node_type": "1", "metadata": {}, "hash": "49b2744fdddc7369cd1261897218d97b9bb57736f0f90cdadd429967172854af", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Buddy System\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Buddy System\n\nIt can be daunting starting a new job, especially remotely where there is sometimes less opportunity for casual questions.\nTo help with this every new employee should be assigned buddies.\nThe buddies are responsible for providing informal friendly faces to offer advice, guidance, and encouragement on any aspect of working within REG and ARC.\n\nWe also emphasise that the buddies should be a safe space where new-starters can get help with any technical questions that they feel is silly or trivially easy and may not feel comfortable admitting to the wider group.\n\n## The Process\n\nBefore the new starter starts at the Turing they should be assigned **two buddies** from our [buddy pool](https://github.com/alan-turing-institute/research-engineering-group/wiki/Buddy-Sign-up-and-Matches).\nIf possible, the buddies should not be assigned to the projects on which the new starter is likely to work, and ideally at a similarly seniority to the new starter.\n\nWe would also like for each new starter to have a third buddy from some other team.\nWe think that in a remote & hybrid setting it will help new-starters meet people and get a feel for the wider Turing ecosystem.\nThis has been trialed in a few instances, to positive feedback, but to make this be standard practice we would need it to be more bidirectional, with REGers volunteering to buddy with people outside of REG.\n\nThe new-starter should be made aware of the buddies' role before they start, and meeting their buddies should typically be the first appointment on their first day.\n\nThe buddy system in managed by\n[the person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n## Buddy Guidelines\n\n- Have a coffee with the new employee on their first day.", "start_char_idx": 0, "end_char_idx": 2201, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "584f6865-e808-41c0-8032-05e75d746feb": {"__data__": {"id_": "584f6865-e808-41c0-8032-05e75d746feb", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3eb1917b4ebf3c304d06bf25d131ead26c9856bb", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "hash": "dcd7c76f633d17ec3fbae2ab77dac71679ce12d56af6326da3fd3de2e78e99aa", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "9ef54c37-488c-41f4-b826-cf988eb9e138", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "hash": "b65ae1c1b1dad25491334288dee6a8917ec65e1ca3b80602072e86168c8305e7", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "36f72676-0d86-4d9b-b3c5-18d5cbe0ad06", "node_type": "1", "metadata": {}, "hash": "83c602396d2c54b222ec7e90017a24faf83558ad5068d31e2469afb4ee9a3ebb", "class_name": "RelatedNodeInfo"}}, "text": "We would also like for each new starter to have a third buddy from some other team.\nWe think that in a remote & hybrid setting it will help new-starters meet people and get a feel for the wider Turing ecosystem.\nThis has been trialed in a few instances, to positive feedback, but to make this be standard practice we would need it to be more bidirectional, with REGers volunteering to buddy with people outside of REG.\n\nThe new-starter should be made aware of the buddies' role before they start, and meeting their buddies should typically be the first appointment on their first day.\n\nThe buddy system in managed by\n[the person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n## Buddy Guidelines\n\n- Have a coffee with the new employee on their first day.\n- If needed, introduce them to the team at welcome coffee.\n- Be as informal as possible, make the new starter feel welcome and comfortable.\n- Make sure the new starter is familiar with the [New Starter page]({{< relref \"/docs/onboarding/new_joiners\" >}}). Check for sticking points.\n- Meet the new starter where they are at.\n- Make it clear that the new employee can get in touch any time for questions and concerns, regardless of triviality.\n- Emphasise that you're also there for technical pointers. This is to encourage the new-starter to be comfortable asking for help when they are stuck and hopefully lessening imposter syndrome anxiety.\n- Have a check-in chat with the new starter at least once in the first couple of weeks, and again a few weeks after. During these check-ins, uncover pain points and concerns.\n- Help socialise the new employee to the group's culture and processes.\n- Be open, honest, and respect confidentiality.\n- If needed, help make introductions.\n\n## Tips for Buddies\n\n- Don\u2019t worry about being perceived as the expert. Focus your attention on the new employee.\n- Be patient: It takes time to develop a relationship. Don\u2019t try to cover everything right away.\n- Be positive: New employees will grow into their roles if given proper reinforcement.\n- Don\u2019t try to force a relationship.", "start_char_idx": 1363, "end_char_idx": 3514, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "36f72676-0d86-4d9b-b3c5-18d5cbe0ad06": {"__data__": {"id_": "36f72676-0d86-4d9b-b3c5-18d5cbe0ad06", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3eb1917b4ebf3c304d06bf25d131ead26c9856bb", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "hash": "dcd7c76f633d17ec3fbae2ab77dac71679ce12d56af6326da3fd3de2e78e99aa", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "584f6865-e808-41c0-8032-05e75d746feb", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/buddy_system.md", "file_name": "buddy_system.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/buddy_system.md"}, "hash": "ab39b77b4f6a8b6e643d8df6c4eff7993ef85dc76ecf3f3ab55cdc10e04981e8", "class_name": "RelatedNodeInfo"}}, "text": "- Emphasise that you're also there for technical pointers. This is to encourage the new-starter to be comfortable asking for help when they are stuck and hopefully lessening imposter syndrome anxiety.\n- Have a check-in chat with the new starter at least once in the first couple of weeks, and again a few weeks after. During these check-ins, uncover pain points and concerns.\n- Help socialise the new employee to the group's culture and processes.\n- Be open, honest, and respect confidentiality.\n- If needed, help make introductions.\n\n## Tips for Buddies\n\n- Don\u2019t worry about being perceived as the expert. Focus your attention on the new employee.\n- Be patient: It takes time to develop a relationship. Don\u2019t try to cover everything right away.\n- Be positive: New employees will grow into their roles if given proper reinforcement.\n- Don\u2019t try to force a relationship. New employees will vary in how much they turn to a buddy.\n- Try to identify the new employee\u2019s personality and communication style and adapt accordingly.\n- Don\u2019t be judgemental. Simply offer feedback.\n- Maintain a good attitude and a teaching spirit.\n\n## How to be a Buddy\n\nBeing a buddy can be a rewarding process.\nYou will help avoid new starters feeling at sea and reaffirm their decision to join the group.\nTo sign up please add your name to our [buddy pool](https://github.com/alan-turing-institute/research-engineering-group/wiki/Buddy-Sign-up-and-Matches).", "start_char_idx": 2645, "end_char_idx": 4078, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "1fdb1412-26d8-4077-955c-b338014f395c": {"__data__": {"id_": "1fdb1412-26d8-4077-955c-b338014f395c", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "eea6eb21a653228f12ee4e03bc10e04179d63ea5", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/_index.md"}, "hash": "02b71d07a87bab02be22e3fc1ee99e330f02f1d7f1ea30af07e3aa1c0e3f5116", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"New Joiners\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# New Joiners\n\nThis section helps new members of REG get up to speed.\nPlease go through these page as one of the very first things when you join.\n\n- [First Few Days]({{< relref \"/docs/onboarding/new_joiners/first_few_days.md\" >}})\n- [Systems Set Up]({{< relref \"/docs/onboarding/new_joiners/systems_set_up.md\" >}})", "start_char_idx": 0, "end_char_idx": 705, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "139ccd73-c4b8-4a69-9349-3584a45b27f6": {"__data__": {"id_": "139ccd73-c4b8-4a69-9349-3584a45b27f6", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "478492af-909f-4927-afa3-a22f69624c18", "node_type": "1", "metadata": {}, "hash": "7da123a59b8f1e823e8b6e8a8298fe5cce5bf6c05e709f1e705375cf6e579633", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Checklist\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 4\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Checklist\n\nThis page attempts to list all the things that a new starter should remember to do.\nIt makes no guarantees of completeness, and attempts to list both compulsory things and optional ones.\nIt has been written by new starters as they go.\nIf you've recently started in REG and find yourself doing anything related to getting started, that others might want to do as well but that is not on this list, please add it.\nIf anything is unclear, please figure it out by e.g. asking your buddies, and then clarify it here.\n\n## Meetings\n\n- [ ] Attend buddy meeting\n- [ ] Attend HR induction\n- [ ] Attend IT induction\n- [ ] Meet with line manager\n- [ ] [Shadow meetings](https://hackmd.io/nnsWP4i_SiiwlT4meOeq6g) (if you don't have access to the HackMD, ask someone in charge of [onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#documents))\n- [ ] Join in for welcome coffee(s)\n\n## HR tasks\n\n- [ ] Complete Agenda screening (hopefully this happened already before you started, but if you haven't received an email about this, ask HR)\n- [ ] Do a bazillion things on [Cezanne](https://w3.cezanneondemand.com/CezanneHR/):\n    - [ ] Enter specific personal details, most important are:\n        - DOB\n        - Home address\n        - Emergency contact details\n        - Bank details\n        - NI\n    - [ ] Health form\n    - [ ] Additional characteristics form\n    - [ ] Complete and reupload in Cezanne documents area:\n        - [ ] \u201cBL partners - Health,", "start_char_idx": 0, "end_char_idx": 1887, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "478492af-909f-4927-afa3-a22f69624c18": {"__data__": {"id_": "478492af-909f-4927-afa3-a22f69624c18", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "139ccd73-c4b8-4a69-9349-3584a45b27f6", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "dd66e9696ce420cafa014844df1ce0e0325c8e1e8b15c0282bfab4f430b1e6bd", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "80a01e0f-9042-4493-92d6-a07003cd87a9", "node_type": "1", "metadata": {}, "hash": "a67e302d23b9b620142d530813f31536e7c72a5ab13673a2085828c66f7b71b8", "class_name": "RelatedNodeInfo"}}, "text": "ask someone in charge of [onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#documents))\n- [ ] Join in for welcome coffee(s)\n\n## HR tasks\n\n- [ ] Complete Agenda screening (hopefully this happened already before you started, but if you haven't received an email about this, ask HR)\n- [ ] Do a bazillion things on [Cezanne](https://w3.cezanneondemand.com/CezanneHR/):\n    - [ ] Enter specific personal details, most important are:\n        - DOB\n        - Home address\n        - Emergency contact details\n        - Bank details\n        - NI\n    - [ ] Health form\n    - [ ] Additional characteristics form\n    - [ ] Complete and reupload in Cezanne documents area:\n        - [ ] \u201cBL partners - Health, Safety and Security\u201d form\n        - [ ] HMRC new starters form\n        - [ ] \"Right to work\" document\n        - [ ] Signed and dated scanned contract (can just upload the version signed with DocuSign before joining)\n- [ ] British Library pass:\n    - [ ] \"BL partners - Health, Safety and Security\" form: complete and reupload in Cezanne documents area\n    - [ ] Upload photo to Documents section on Cezanne (where other starter forms are provided)\n    - [ ] HR require Agenda Screening report (no action if already completed Agenda Screening before starting)\n- [ ] Temporary pass, line manager emails reception to request\n- [ ] Send P45 from previous job to HR contact directly by email\n- [ ] Read about health and dental insurance, decide whether to sign up\n- [ ] Check the Turing Benefits site, to see if there are useful discounts there\n- [ ] Send to HR for the next Turing Town Hall meeting:\n    - [ ] Casual photo\n    - [ ] Fun fact(s)\n- [ ] Provide a description for the [ATI webpage](https://turingcomplete.topdesk.", "start_char_idx": 1143, "end_char_idx": 2910, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "80a01e0f-9042-4493-92d6-a07003cd87a9": {"__data__": {"id_": "80a01e0f-9042-4493-92d6-a07003cd87a9", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "478492af-909f-4927-afa3-a22f69624c18", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "a283bf87393d12159f032ee419cb0ed62d7b1a2dfd27f9c402146e1696f21ed2", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "95e4556b-baf0-40be-a66b-4d2b0eebafb3", "node_type": "1", "metadata": {}, "hash": "a189f6c4a74bee03d6837e32dd146467e27e111ba9d09718f149407cec2d1111", "class_name": "RelatedNodeInfo"}}, "text": "Safety and Security\" form: complete and reupload in Cezanne documents area\n    - [ ] Upload photo to Documents section on Cezanne (where other starter forms are provided)\n    - [ ] HR require Agenda Screening report (no action if already completed Agenda Screening before starting)\n- [ ] Temporary pass, line manager emails reception to request\n- [ ] Send P45 from previous job to HR contact directly by email\n- [ ] Read about health and dental insurance, decide whether to sign up\n- [ ] Check the Turing Benefits site, to see if there are useful discounts there\n- [ ] Send to HR for the next Turing Town Hall meeting:\n    - [ ] Casual photo\n    - [ ] Fun fact(s)\n- [ ] Provide a description for the [ATI webpage](https://turingcomplete.topdesk.net/tas/public/ssp/content/serviceflow?unid=35b8d40067004f9484c9fb06ade41d65&openedFromService=true) (https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up#create-a-public-profile-on-the-turing-website)\n- [ ] Verify your MoorePay account (for payslips). Verification email will be sent to you in your first month of employment. See [Payroll and Payslip enquiries on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2277&SearchId=190970&utm_source=interact&utm_medium=quick_search&utm_term=payslip) for more information.\n- [ ] Verify your Certify account (for expenses). The [Certify Mathison page](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2169) contains a Travel & Expenses Policy Compliance Form which has to be filled in and emailed to Finance to activate your account.", "start_char_idx": 2165, "end_char_idx": 3783, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "95e4556b-baf0-40be-a66b-4d2b0eebafb3": {"__data__": {"id_": "95e4556b-baf0-40be-a66b-4d2b0eebafb3", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "80a01e0f-9042-4493-92d6-a07003cd87a9", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "ea2b1417058d5548b4f93f5097d323770c6850466dab86c34935ad83705e0982", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "32f9cd78-e1aa-43d6-ad05-0c482996ffb4", "node_type": "1", "metadata": {}, "hash": "85571cced43578112ae2390b4e9e0ca3b3f39eae69f5fe1eea147ddbfedcf983", "class_name": "RelatedNodeInfo"}}, "text": "github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up#create-a-public-profile-on-the-turing-website)\n- [ ] Verify your MoorePay account (for payslips). Verification email will be sent to you in your first month of employment. See [Payroll and Payslip enquiries on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2277&SearchId=190970&utm_source=interact&utm_medium=quick_search&utm_term=payslip) for more information.\n- [ ] Verify your Certify account (for expenses). The [Certify Mathison page](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2169) contains a Travel & Expenses Policy Compliance Form which has to be filled in and emailed to Finance to activate your account.\n\n## GitHub and communications\n\n- [ ] Get access to private GitHub repos (fill in [this form](https://forms.office.com/e/9rwSjBdfQp) and send your GitHub username to the person responsible for GitHub (see [The REGistry](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#documents)) to join the Alan Turing Organisation GitHub\n- [ ] Join Turing Slack\n    - [ ] Go through public Slack channels, decide which ones to join\n- [ ] Request account on Harvest and Forecast by asking the the person in charge of researcher tool management, or your buddies to get you set up (to clarify, it is one account but allows you sign in to both) https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/systems_set_up#harvest-and-forecast\n- [ ] Read through the [new joiners pages of the REG handbook]({{< relref \"/docs/onboarding/new_joiners/_index.md\" >}})\n- [ ] Have a look at REG projects on the [Project tracker](https://github.com/alan-turing-institute/Hut23/projects/2) in the Hut23 GitHub Repo.", "start_char_idx": 3042, "end_char_idx": 4828, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "32f9cd78-e1aa-43d6-ad05-0c482996ffb4": {"__data__": {"id_": "32f9cd78-e1aa-43d6-ad05-0c482996ffb4", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "95e4556b-baf0-40be-a66b-4d2b0eebafb3", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "61bc50ac7a7636340f5217aed8148ee46a477420ce9dfd336ae2f16d3dbaa259", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b0534a2e-7f2a-493d-895f-0ba89a3d940b", "node_type": "1", "metadata": {}, "hash": "535cd0892706cec6770468de8ac588862ed95e98c30b31380164b0845314b574", "class_name": "RelatedNodeInfo"}}, "text": "Read through the [Project Tracking]({{< relref \"/docs/how_we_work/project_tracking.md\" >}}) page to learn how we set our preferences for projects by using emojis, and start emoji-ing for the projects in the \"Finding people\" and \"Awaiting go/no-go\" columns.\n- [ ] Have a look at the [Service areas](https://github.com/alan-turing-institute/research-engineering-group/wiki/Service-areas). There is no pressure to join one right away so you can take some time talking to people and finding an area that interests you.\n- [ ] Add the [shared REG Calendar](https://github.com/alan-turing-institute/research-engineering-group/wiki/Shared-REG-Calendar) to Outlook\n- [ ] Familiarise yourself with how to [Book Rooms]({{< relref \"/docs/working_at_the_turing/booking_a_meeting_room.md\" >}})\n- [ ] Message [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) to upgrade Zoom account from Basic to Pro\n- [ ] Send a short informal bio to the [REG newsletter owner](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n## Sign up for ongoing activities\n\n- [ ] Feel free to put yourself down for a [tech talk]({{< relref \"/docs/regular_events/lunchtime_tech_talks.md\" >}}) (12:30 pm on Tuesdays): you can see if any open dates in [the schedule](https://github.com/alan-turing-institute/DataScienceSkills/wiki/Lunchtime-Tech-Talks) work for you, and then let [the person in charge of tech talks](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) know.\n- [ ] Read up about (and perhaps join---we recommend you do!)", "start_char_idx": 4829, "end_char_idx": 6489, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b0534a2e-7f2a-493d-895f-0ba89a3d940b": {"__data__": {"id_": "b0534a2e-7f2a-493d-895f-0ba89a3d940b", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3452b76140b3029de5af4930db29caa5e943513d", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "0f8587da24ec5340e668b26212876769ac035d237a8d4cd2a03ee1ff7620bb13", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "32f9cd78-e1aa-43d6-ad05-0c482996ffb4", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/checklist.md", "file_name": "checklist.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/checklist.md"}, "hash": "35a5870fa1aaa7db0685f187258de2d502617614466917984f2722c5cb580b83", "class_name": "RelatedNodeInfo"}}, "text": "## Sign up for ongoing activities\n\n- [ ] Feel free to put yourself down for a [tech talk]({{< relref \"/docs/regular_events/lunchtime_tech_talks.md\" >}}) (12:30 pm on Tuesdays): you can see if any open dates in [the schedule](https://github.com/alan-turing-institute/DataScienceSkills/wiki/Lunchtime-Tech-Talks) work for you, and then let [the person in charge of tech talks](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) know.\n- [ ] Read up about (and perhaps join---we recommend you do!) the [Society of Research Software Engineering]({{< relref \"../society_of_research_software_engineering.md\" >}})\n- [ ] Sign up for any of the [EDI network groups]({{< relref \"/docs/working_at_the_turing/edi.md#network-groups\" >}}) which might interest you", "start_char_idx": 5943, "end_char_idx": 6744, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a84349e5-52b8-4073-9487-497ca07f95ee": {"__data__": {"id_": "a84349e5-52b8-4073-9487-497ca07f95ee", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "59036fe2-a102-4943-b9bf-17e525a18467", "node_type": "1", "metadata": {}, "hash": "3df06ab8a27b5b735bfe8c37529a08c46eb44f3df87d16a1c9b2a5aea98b7c6e", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"First Few Days\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# First Few Days\n\n**Welcome!**\n\nWe are super pleased that you're joining our group.\nThis page gives you an overview of what to expect from your first few days.\nThey are maintained by the group, we hope that new-starters feel empowered to edit them if there are useful changes to be made.\n\n## Introductions\n\nEach new starter gets assigned two buddies to be friendly points of contact.\nThe [person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities)\nshould let you know who these are a bit before you start;\nIf that doesn't happen, please email them about it.\nYour buddies will welcome you on the first day.\nIn the first few days there will be a welcome coffee to give you a chance to introduce yourself to the whole REG team, and within the first few weeks you will also have a 1-on-1 meeting with REG's Director.\n\nYou will have a week or so before you will be assigned to a project.\nPlease use this time to do admin stuff, set up your laptop and tools, get to know people, read this handbook and our internal wiki, and shadow meetings (see below).\nHere we go over the main points you should remember to do, but for an attempt at listing all the small tasks that occupy a new starter, please see (and improve!) [this checklist](https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/checklist/).\n\nWe also have a [page of tables](https://github.com/alan-turing-institute/research-engineering-group/wiki/Useful-Links-for-REG) available with information about links and resources related to REG and the Alan Turing Institute.", "start_char_idx": 0, "end_char_idx": 2018, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "59036fe2-a102-4943-b9bf-17e525a18467": {"__data__": {"id_": "59036fe2-a102-4943-b9bf-17e525a18467", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a84349e5-52b8-4073-9487-497ca07f95ee", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "453574eddfd8dcc973ca13a9d1212dff65d8cfdd61632b6d7d0cb38da8258fe6", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "f3d35cee-3ccb-41d8-ad38-ff273e598cb5", "node_type": "1", "metadata": {}, "hash": "7c85379f9e8c472a8b9e7a771d4a534a7a16710f55b8832ce67870240a6e3e23", "class_name": "RelatedNodeInfo"}}, "text": "You will have a week or so before you will be assigned to a project.\nPlease use this time to do admin stuff, set up your laptop and tools, get to know people, read this handbook and our internal wiki, and shadow meetings (see below).\nHere we go over the main points you should remember to do, but for an attempt at listing all the small tasks that occupy a new starter, please see (and improve!) [this checklist](https://alan-turing-institute.github.io/REG-handbook/docs/onboarding/new_joiners/checklist/).\n\nWe also have a [page of tables](https://github.com/alan-turing-institute/research-engineering-group/wiki/Useful-Links-for-REG) available with information about links and resources related to REG and the Alan Turing Institute.\n\n## Administrivia\n\nHR will set up a meeting with you to discuss general information *e.g.* pay, health, leaves, benefits.\nIT will also meet you to discuss accounts and Turing wide systems.\nBoth of these will typically take place in the first day or two.\n\nWe have a\n[dedicated page]({{< relref \"/docs/onboarding/new_joiners/systems_set_up.md\" >}})\nfor various systems REG uses.\n**Have a look at the high priority section on your first day**.\n\nWe also have a\n[Remote Working](https://github.com/alan-turing-institute/research-engineering-group/wiki/Remote-Working)\npage for Zoom, Teams, and Gather.\n\nREG offers new starters the opportunity to shadow meetings across the group to meet people and get a feel for how we work.\nThe\n[shadowing document](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#documents)\nshould be updated with meetings you are free to come and listen in on, if it hasn't been please bug the\n[person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\nTo access the office you will need a British Library pass.\nTo get a British Library pass you will need to go through the background screening check.", "start_char_idx": 1285, "end_char_idx": 3253, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f3d35cee-3ccb-41d8-ad38-ff273e598cb5": {"__data__": {"id_": "f3d35cee-3ccb-41d8-ad38-ff273e598cb5", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "59036fe2-a102-4943-b9bf-17e525a18467", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "a6d34c5587c39cf634ac19b560e4ab9126887d2de0766dfad0d099f51fb56aa5", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b6473046-4e60-4cac-89fb-ccb09390f1ec", "node_type": "1", "metadata": {}, "hash": "4e620fd1830956caf680a4fe79eab006eae2cac91f664d3fc111b914194027b8", "class_name": "RelatedNodeInfo"}}, "text": "**Have a look at the high priority section on your first day**.\n\nWe also have a\n[Remote Working](https://github.com/alan-turing-institute/research-engineering-group/wiki/Remote-Working)\npage for Zoom, Teams, and Gather.\n\nREG offers new starters the opportunity to shadow meetings across the group to meet people and get a feel for how we work.\nThe\n[shadowing document](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#documents)\nshould be updated with meetings you are free to come and listen in on, if it hasn't been please bug the\n[person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\nTo access the office you will need a British Library pass.\nTo get a British Library pass you will need to go through the background screening check.\nHopefully that process will have been completed by the time you start, but if it hasn't even been started yet (this happens), please talk to\n[HR](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact)\nto make sure the process gets going.\nOnce the screening is done, HR should let you know within a few days that your pass is ready.\n\nIn the meanwhile, you can either enter the British Library through the general public entrance and come talk to the Turing's reception, or, preferably, arrange for a visitor pass for yourself for the days when you're coming in.\nFor the first day or two, ask your line manager to take care of this, once you've got your Turing account going you can directly ask [reception](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n\nThe password for the \"ATI Guest\" Wi-Fi network can be seen hanging around the office.\n[IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact)\ncan arrange access to the [Eduroam](https://eduroam.org/) network.", "start_char_idx": 2396, "end_char_idx": 4379, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b6473046-4e60-4cac-89fb-ccb09390f1ec": {"__data__": {"id_": "b6473046-4e60-4cac-89fb-ccb09390f1ec", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": 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"0792dedad1e3c6ae85cabedee168def39fc44653b95f8a849ee25ac6e252b09a", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "3abae02f-f871-4edb-b3f1-81da237825da", "node_type": "1", "metadata": {}, "hash": "165a7d0e4e468c01fcca391e503899f345983fa151949a4abf3119fca2e04497", "class_name": "RelatedNodeInfo"}}, "text": "Once the screening is done, HR should let you know within a few days that your pass is ready.\n\nIn the meanwhile, you can either enter the British Library through the general public entrance and come talk to the Turing's reception, or, preferably, arrange for a visitor pass for yourself for the days when you're coming in.\nFor the first day or two, ask your line manager to take care of this, once you've got your Turing account going you can directly ask [reception](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n\nThe password for the \"ATI Guest\" Wi-Fi network can be seen hanging around the office.\n[IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact)\ncan arrange access to the [Eduroam](https://eduroam.org/) network.\nEduroam credentials can be used to access Wi-Fi at many academic institutions.\n\nPlease also write a short, informal paragraph about your background for to be put into the next REG newsletter,\nand send it to\n[the newsletter owner](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities).\n\n## Overview of How REG Operates\n\nWork at REG revolves around projects and service areas.\nYou will typically be assigned to two different projects at any time, with at least one other REG person and usually some external collaborators.\nYou may want to browse REG's portfolio of projects in the\n[project tracker](https://github.com/alan-turing-institute/Hut23/projects/2)\nto get an idea of what we're up to.\nThe project tracker is also used for expressing preferences on which upcoming projects you would like to work on, but you don't have to worry about that for first few weeks.\nWe very much encourage REG members to learn new things, so feel free to choose projects that sound interesting to you even if they are unfamiliar.\nCheck\n[this section]({{< relref \"/docs/how_we_work/project_tracking.md\" >}})\nof the handbook for more details.", "start_char_idx": 3542, "end_char_idx": 5553, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3abae02f-f871-4edb-b3f1-81da237825da": {"__data__": {"id_": "3abae02f-f871-4edb-b3f1-81da237825da", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "b6473046-4e60-4cac-89fb-ccb09390f1ec", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "4600a138913f86bd8fa02e6a0e1ff308c1644b3ff2a4a23a0542cec528f00bf8", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "decbaa19-7313-463e-babe-625487666fb0", "node_type": "1", "metadata": {}, "hash": "6d2be271c2b78b6cc5db52b63092b6adc9d5bc8690ea3de867015b8236f3fcb0", "class_name": "RelatedNodeInfo"}}, "text": "## Overview of How REG Operates\n\nWork at REG revolves around projects and service areas.\nYou will typically be assigned to two different projects at any time, with at least one other REG person and usually some external collaborators.\nYou may want to browse REG's portfolio of projects in the\n[project tracker](https://github.com/alan-turing-institute/Hut23/projects/2)\nto get an idea of what we're up to.\nThe project tracker is also used for expressing preferences on which upcoming projects you would like to work on, but you don't have to worry about that for first few weeks.\nWe very much encourage REG members to learn new things, so feel free to choose projects that sound interesting to you even if they are unfamiliar.\nCheck\n[this section]({{< relref \"/docs/how_we_work/project_tracking.md\" >}})\nof the handbook for more details.\n\nService areas are REG-internal work, such as looking after this handbook, organising recruitment, or managing our computational resources.\nUsually people contribute to one service area, which should take approximately half a day a week.\nThis, too, is something you don't have to think about for now, but come back to it once you've settled into your workflow with a project.\n\nREG also gives its members some time, known as 22 days time, to work on side projects they find interesting.\nYour line manager can give you the details on this.\n\nIf you like, you can see our\n[team reporting structure](https://github.com/alan-turing-institute/research-engineering-group/wiki/Team-reporting-structure).\nNote, however, that we do not have a hierarchical culture, and line managing relations are not about assigning work to people.\nYour work tasks will be coordinated with the people in your projects.\n\nYou should talk to your line manager about things such as\n\n- How project allocation works, and what kinds of projects you would like to work on\n- Managing your time and growth\n- How we work (practices, tools, languages, *etc.*)\n- How you would like to grow and develop in your role and what opportunities exist to support that.", "start_char_idx": 4716, "end_char_idx": 6774, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "decbaa19-7313-463e-babe-625487666fb0": {"__data__": {"id_": "decbaa19-7313-463e-babe-625487666fb0", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "3abae02f-f871-4edb-b3f1-81da237825da", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "92ac498da55c13f7b309858e9c2ab813cf3b54423a0069822fcc32fec2812632", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "e8cd1844-095b-484b-9dad-c95abf3c4373", "node_type": "1", "metadata": {}, "hash": "8d51b7eaa40fb307e593c1c9c7e400d8b40e3055ea56805794288a99c8afdec6", "class_name": "RelatedNodeInfo"}}, "text": "REG also gives its members some time, known as 22 days time, to work on side projects they find interesting.\nYour line manager can give you the details on this.\n\nIf you like, you can see our\n[team reporting structure](https://github.com/alan-turing-institute/research-engineering-group/wiki/Team-reporting-structure).\nNote, however, that we do not have a hierarchical culture, and line managing relations are not about assigning work to people.\nYour work tasks will be coordinated with the people in your projects.\n\nYou should talk to your line manager about things such as\n\n- How project allocation works, and what kinds of projects you would like to work on\n- Managing your time and growth\n- How we work (practices, tools, languages, *etc.*)\n- How you would like to grow and develop in your role and what opportunities exist to support that.\n\nPlease also take a some time to browse this handbook, it's got plenty of useful information about what we do and how we do it.\n\n## Set up Your Machine and Coding Environment\n\nSee [systems setup]({{< relref \"/docs/onboarding/new_joiners/systems_set_up.md\" >}}).\n\n## Regular Events\n\nCheck out our [regular events and coffee breaks]({{< relref \"/docs/regular_events/_index.md\" >}}).\nPlease consider signing up for a\n[tech talk]({{< relref \"/docs/regular_events/lunchtime_tech_talks.md\" >}})\nsometime in your first six weeks.\nIf you don't see a calendar invite for the weekly tech talks, ask the\n[person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities)\nto add you to the relevant mailing list.\n\n## What to do next\n\nIf you've done all the above, read through the handbook and the repo and are still waiting for your first project to start, go and talk to the person in charge of onboarding.", "start_char_idx": 5931, "end_char_idx": 7741, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e8cd1844-095b-484b-9dad-c95abf3c4373": {"__data__": {"id_": "e8cd1844-095b-484b-9dad-c95abf3c4373", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "decbaa19-7313-463e-babe-625487666fb0", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "dbae013adf47a37b2c327529f1a4e8e4669331bc2d2fe9c9a0770191c100bc18", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "470778ec-4e10-4cb1-9924-88d00b6c258c", "node_type": "1", "metadata": {}, "hash": "3b786468bd5c0a1d29025245971feac37e9da61ae6a19f8e108bd485ebcbe05a", "class_name": "RelatedNodeInfo"}}, "text": "## Regular Events\n\nCheck out our [regular events and coffee breaks]({{< relref \"/docs/regular_events/_index.md\" >}}).\nPlease consider signing up for a\n[tech talk]({{< relref \"/docs/regular_events/lunchtime_tech_talks.md\" >}})\nsometime in your first six weeks.\nIf you don't see a calendar invite for the weekly tech talks, ask the\n[person in charge of onboarding](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities)\nto add you to the relevant mailing list.\n\n## What to do next\n\nIf you've done all the above, read through the handbook and the repo and are still waiting for your first project to start, go and talk to the person in charge of onboarding.\nThey will find something meaningful for you to do which might be something like:\n\n- Browse the [Project tracker](https://github.com/alan-turing-institute/Hut23/projects/2) and emoji any projects in the `Finding people` and `Awaiting go/no-go` columns.\n  - Note: you will be notified to do this before being assigned to a project.\n- Browse the [Service areas list](https://github.com/alan-turing-institute/research-engineering-group/wiki/Service-areas), discuss the different areas with people involved and emoji those that are looking for people.\n- Work on a Turing Data Story. There's always a story to join or a new one to start. You can then keep working on it on your 22 days time once allocated to a project. This option has been very popular.\n- Tackle a GitHub issue marked with the `good first issue` tag in a repo that REG works on. Whenever a new starter is looking for things to do, the person in charge of onboarding will send a message on Slack asking everyone to tag suitable issues.\n- Work on a REG-internal project that is easy to rotate in/out of.\n- Any ad hoc ideas that come up.", "start_char_idx": 7038, "end_char_idx": 8838, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "470778ec-4e10-4cb1-9924-88d00b6c258c": {"__data__": {"id_": "470778ec-4e10-4cb1-9924-88d00b6c258c", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6efba9701175d0c3c1d62dd01694f3ca71d984d9", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "ecb3e19c8240dccf49f21c61c93b2b839dc634beef23d9d32c758e489472beb7", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "e8cd1844-095b-484b-9dad-c95abf3c4373", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/first_few_days.md", "file_name": "first_few_days.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/first_few_days.md"}, "hash": "2e41989d2563ad4537dcda6674fdbf9d30767ec5d7b079bd6248e5f420b5a12a", "class_name": "RelatedNodeInfo"}}, "text": "- Note: you will be notified to do this before being assigned to a project.\n- Browse the [Service areas list](https://github.com/alan-turing-institute/research-engineering-group/wiki/Service-areas), discuss the different areas with people involved and emoji those that are looking for people.\n- Work on a Turing Data Story. There's always a story to join or a new one to start. You can then keep working on it on your 22 days time once allocated to a project. This option has been very popular.\n- Tackle a GitHub issue marked with the `good first issue` tag in a repo that REG works on. Whenever a new starter is looking for things to do, the person in charge of onboarding will send a message on Slack asking everyone to tag suitable issues.\n- Work on a REG-internal project that is easy to rotate in/out of.\n- Any ad hoc ideas that come up. Maybe you found a project that really interests you and want to help, or maybe there's some team organisational thing that comes up that you volunteer for.\n\n## Points of Contact\n\nYou can find useful points of contact\n[here](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n[This page](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry)\nalso holds other useful information such as remote meeting details and team responsibilities.", "start_char_idx": 7996, "end_char_idx": 9361, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f6d6b5c8-11e1-49ba-9933-781cd71025df": {"__data__": {"id_": "f6d6b5c8-11e1-49ba-9933-781cd71025df", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "93ec1759-4667-466e-8bea-d4739b5b8f0b", "node_type": "1", "metadata": {}, "hash": "35c7b9cfe319d650ade41ea82e079e3f7ce236ea75150edd55993f12b50c0f17", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Systems Set Up\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 3\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Systems Set Up\n\n{{< hint info >}}\nThese are not hand-holding instructions.\nRather they are a reference to help with your setup.\nIf they are wrong or incomplete, please edit them.\n{{< /hint >}}\n\nThe page has been roughly organised into high priority things (do in your first couple of days), low priority (sometimes in the first couple of weeks), and reference.\n\nIf any of the material is unfamiliar (*e.g.* you haven't seen a GitHub project board before), then please ask your buddies (or anyone else you meet in the group) to have an informal chat about it.\n\n## High priority\n\n### Getting a computer\n\nReceive (or be assigned) new computer from IT.\nIt will have an admin account for IT, and probably an account for you with a default password.\nContact your line manager as a matter of urgency if you are not able to get a laptop on your first day at the Turing.\n\nLog in with the password provided by IT.\n\n**Change your password.**\n\nYour new password should be strong (12 random characters), and ideally randomly generated by a password manager.\nFeel free to use a diceware-style passphrase.\n\nYour password should be:\n\n- unique to the machine/system\n- randomly generated (use a password generator)\n- have an entropy of at least 64 bits, see [wikipedia](https://en.wikipedia.org/wiki/Password_strength)\n   - At least 11 characters if alphanumeric (use 12, though)\n   - At least 10 characters if alphanumeric plus symbols\n   - At least 5 words if using dice ware\n\nAt this point you can also configure fingerprint authentication.\n\nMake sure you have [FileVault](https://support.apple.com/en-gb/guide/deployment/dep82064ec40/web) on.", "start_char_idx": 0, "end_char_idx": 2024, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "93ec1759-4667-466e-8bea-d4739b5b8f0b": {"__data__": {"id_": "93ec1759-4667-466e-8bea-d4739b5b8f0b", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "f6d6b5c8-11e1-49ba-9933-781cd71025df", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "d2966c6eba17c4c453e8a49659de1bc9d5c8287030ab3fc48967f65a760bb90c", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c1000392-16cf-421a-b25f-5c240de81979", "node_type": "1", "metadata": {}, "hash": "4cbbb5f87c30d100543eb27d6542aa6b14a4d48cd10904100c468275b845aae8", "class_name": "RelatedNodeInfo"}}, "text": "Contact your line manager as a matter of urgency if you are not able to get a laptop on your first day at the Turing.\n\nLog in with the password provided by IT.\n\n**Change your password.**\n\nYour new password should be strong (12 random characters), and ideally randomly generated by a password manager.\nFeel free to use a diceware-style passphrase.\n\nYour password should be:\n\n- unique to the machine/system\n- randomly generated (use a password generator)\n- have an entropy of at least 64 bits, see [wikipedia](https://en.wikipedia.org/wiki/Password_strength)\n   - At least 11 characters if alphanumeric (use 12, though)\n   - At least 10 characters if alphanumeric plus symbols\n   - At least 5 words if using dice ware\n\nAt this point you can also configure fingerprint authentication.\n\nMake sure you have [FileVault](https://support.apple.com/en-gb/guide/deployment/dep82064ec40/web) on.\n\nYou should be setup with local admin privileges.\nContact [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) if this isn't the case.\n\n### Install Homebrew\n\nWe recommend using [Homebrew](https://brew.sh/) as your Mac package manager.\nCurrently IT installs Homebrew by default on our laptops under `/usr/local/bin`.\nHowever, in the long run this might create problems with the new M1 Macs we use.\nTo see if this problem applies to you, run `which brew` in the terminal and check if that's the location.\nUntil this is fixed by IT, what we recommend is the following:\n\n1. Uninstall the current version of Homebrew by running the following command in the terminal: `sudo /bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/uninstall.sh)\"`\n1. Install Homebrew again following the instructions on the [website](https://brew.sh/).\n   This will install Homebrew under `/opt/homebrew/bin/brew` (you can check by entering `which brew` in the terminal to see the new location).\n\n### Communication Channels\n\nMost communication happens through Slack.", "start_char_idx": 1140, "end_char_idx": 3153, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c1000392-16cf-421a-b25f-5c240de81979": {"__data__": {"id_": "c1000392-16cf-421a-b25f-5c240de81979", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "93ec1759-4667-466e-8bea-d4739b5b8f0b", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "c68159702ea833cbb86f98b3e2710151832df3410abeb6b65df5cf5bbb89bb26", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "91a3f525-e557-44a9-8cff-fffa910a2207", "node_type": "1", "metadata": {}, "hash": "d3c5bd9fbcfa87b5d317255acea56f522410e324d3cde9b587051b444fc0ecd7", "class_name": "RelatedNodeInfo"}}, "text": "Currently IT installs Homebrew by default on our laptops under `/usr/local/bin`.\nHowever, in the long run this might create problems with the new M1 Macs we use.\nTo see if this problem applies to you, run `which brew` in the terminal and check if that's the location.\nUntil this is fixed by IT, what we recommend is the following:\n\n1. Uninstall the current version of Homebrew by running the following command in the terminal: `sudo /bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/uninstall.sh)\"`\n1. Install Homebrew again following the instructions on the [website](https://brew.sh/).\n   This will install Homebrew under `/opt/homebrew/bin/brew` (you can check by entering `which brew` in the terminal to see the new location).\n\n### Communication Channels\n\nMost communication happens through Slack.\nMore structured communication happens through GitHub.\nThese two systems are the first things you should setup (once you're on Slack it's a lot easier to ask for help).\n\n### Slack\n\nInstall Slack:\n\n```zsh\nbrew install --cask slack\n```\n\nOnce installed join the [Turing Slack](https://alan-turing-institute.slack.com).\nYou can self-subscribe with your @turing.ac.uk email address.\n\nImportant channels to join:\n\n- You should be added to `#general` and `#random` by default.\n- Ask anyone in the team to add you to the private channel `#hut23`.\n- If you are a senior, please ask another senior to invite you to the REG senior\u2019s Slack channel (`#line-managers`).\n\n### GitHub\n\nWe use GitHub for most coordination.\n\nTo get access to Turing GitHub, create a GitHub account (or use an existing one), and fill out [this MS Form](https://forms.office.com/e/9rwSjBdfQp) with your GitHub handle (if you have access to Turing Complete).\nThen let [the GitHub org controller](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) know that you have done this and they can add you straight away.", "start_char_idx": 2317, "end_char_idx": 4279, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "91a3f525-e557-44a9-8cff-fffa910a2207": {"__data__": {"id_": "91a3f525-e557-44a9-8cff-fffa910a2207", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "c1000392-16cf-421a-b25f-5c240de81979", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "505bc68308cb31d3f5208696f52a6a4c6ac3656814ad3bc4f0482971eec00dbc", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ea6366c9-d607-441d-b084-f601bb402466", "node_type": "1", "metadata": {}, "hash": "e8192dcd47b51520e9cabdd78ecacae2b661886dbed0e0eccfbf851aae59242a", "class_name": "RelatedNodeInfo"}}, "text": "You can self-subscribe with your @turing.ac.uk email address.\n\nImportant channels to join:\n\n- You should be added to `#general` and `#random` by default.\n- Ask anyone in the team to add you to the private channel `#hut23`.\n- If you are a senior, please ask another senior to invite you to the REG senior\u2019s Slack channel (`#line-managers`).\n\n### GitHub\n\nWe use GitHub for most coordination.\n\nTo get access to Turing GitHub, create a GitHub account (or use an existing one), and fill out [this MS Form](https://forms.office.com/e/9rwSjBdfQp) with your GitHub handle (if you have access to Turing Complete).\nThen let [the GitHub org controller](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) know that you have done this and they can add you straight away.\n\nAlso check the [Project tracker](https://github.com/alan-turing-institute/Hut23/projects/2?) to get a feeling on what goes on. We use it to track official projects we are tasked with. This is where we express preferences for projects, with emojis. Project leads should be assigned to the relevant issue and are responsible for keeping the issue up to date. Check [this section]({{< relref \"/docs/how_we_work/project_tracking.md\" >}}) of the handbook for more details.\n\n### Office 365 account\n\nYour Turing main account rests [here](https://www.office.com).\nYou get access to mail (Exchange), calendar, a shared drive, Office apps.\nCheck that you can login, and change your password and configure MFA.\nAsk [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) if in need for help.\n\n## Lower Priority\n\nHere we have more information about how we use the high priority systems, and also other systems to explore.\n\n### Slack Channels\n\nTuring is a large organisation and there are many other channels you may want to join.\nSome are purely social, some are related to programmes or projects.", "start_char_idx": 3469, "end_char_idx": 5420, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ea6366c9-d607-441d-b084-f601bb402466": {"__data__": {"id_": "ea6366c9-d607-441d-b084-f601bb402466", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "91a3f525-e557-44a9-8cff-fffa910a2207", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "f85a4e04694be7c2ed3c24a7bd6cfbe1e3cadf3e9992d50d8f8fba2827c8961f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a45cd662-89ef-4e64-9475-caae4a08d1f7", "node_type": "1", "metadata": {}, "hash": "4fb47b13c40ad414e609daaa7d09baea67b60c2eac36d9b9c2028f7c67eb08c8", "class_name": "RelatedNodeInfo"}}, "text": "Project leads should be assigned to the relevant issue and are responsible for keeping the issue up to date. Check [this section]({{< relref \"/docs/how_we_work/project_tracking.md\" >}}) of the handbook for more details.\n\n### Office 365 account\n\nYour Turing main account rests [here](https://www.office.com).\nYou get access to mail (Exchange), calendar, a shared drive, Office apps.\nCheck that you can login, and change your password and configure MFA.\nAsk [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) if in need for help.\n\n## Lower Priority\n\nHere we have more information about how we use the high priority systems, and also other systems to explore.\n\n### Slack Channels\n\nTuring is a large organisation and there are many other channels you may want to join.\nSome are purely social, some are related to programmes or projects.\nFor example, `#tps` (for the closely aligned Tools, Practices and Systems programme), `#kitchendropin`, `#social`, `#pubclub`, `#askaway`, `#research-computing`, `#interesting-events`, `#football`.\nIf you get interested in something just ask if there is a channel about it.\n\nAll of the REG-specific channels are prefixed with `hut23`, so that's a good thing to search for in the channel list.\n\nYou may also want to add yourself to `#hut23standup`, which some of us use for automated standups of \"About Us\" questions.\nThe purpose is to get to know other team members by answering a question three times a week.\nGeekbot should send you the next question at 10:30am on Mondays, Wednesdays and Fridays.\n\nTips for using Geekbot:\n\n- You can report before/after Geekbot asks you by sending it a message from Slack, e.g. `report About Us`.\n- The Slack channels containing the responses can generate a lot of notifications. You might want to turn off notifications, or alternatively mute the channel and check in on it manually instead. (Right-click on the channel name to do this.)", "start_char_idx": 4529, "end_char_idx": 6494, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a45cd662-89ef-4e64-9475-caae4a08d1f7": {"__data__": {"id_": "a45cd662-89ef-4e64-9475-caae4a08d1f7", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ea6366c9-d607-441d-b084-f601bb402466", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "945c29bb90b6014f807ab1cb23de02738ef562e7705af948d2b2eedc0188e1ec", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "65522fe5-fb80-43d0-8032-255f08e75db4", "node_type": "1", "metadata": {}, "hash": "2abf7cde2d4d371ad4524093a2597ab9aba4671568bb7e8f4776b5d2705f5ce2", "class_name": "RelatedNodeInfo"}}, "text": "All of the REG-specific channels are prefixed with `hut23`, so that's a good thing to search for in the channel list.\n\nYou may also want to add yourself to `#hut23standup`, which some of us use for automated standups of \"About Us\" questions.\nThe purpose is to get to know other team members by answering a question three times a week.\nGeekbot should send you the next question at 10:30am on Mondays, Wednesdays and Fridays.\n\nTips for using Geekbot:\n\n- You can report before/after Geekbot asks you by sending it a message from Slack, e.g. `report About Us`.\n- The Slack channels containing the responses can generate a lot of notifications. You might want to turn off notifications, or alternatively mute the channel and check in on it manually instead. (Right-click on the channel name to do this.)\n- You can customise the time of Geekbot's questions. Send `dashboard` as a message to Geekbot and click the link it returns. From there, you can select a standup and the \"My preferences\" button will take you to the customisation page. (This setting may not be enabled on all standups.)\n- You can export a list of your previous responses to a standup from the dashboard. Click on the \"timeline\" tab of the relevant standup, and from there you can select the participants and questions to export to a csv file. This is particularly useful when you have forgotten to fill in Harvest for a while and need the information from the daily standup!\n\nOther workspaces you could join:\n\n- The Society of Research Software Engineering has a [Slack worspace](https://ukrse.slack.com/join/signup). For information about joining the Society see the [SocRSE page]({{< relref \"../society_of_research_software_engineering.md\" >}}).", "start_char_idx": 5696, "end_char_idx": 7408, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "65522fe5-fb80-43d0-8032-255f08e75db4": {"__data__": {"id_": "65522fe5-fb80-43d0-8032-255f08e75db4", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a45cd662-89ef-4e64-9475-caae4a08d1f7", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "da65a1dc97073b8b86d943462f3d29f1925541633f7849b55744c2458c1dfc9e", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "201432b8-c6a7-4e53-9ae1-5e5e225aaca1", "node_type": "1", "metadata": {}, "hash": "89e4b8086599c98043598c6c0319a7fb1912a061b2f4a34959b51b0f06cf156e", "class_name": "RelatedNodeInfo"}}, "text": "Send `dashboard` as a message to Geekbot and click the link it returns. From there, you can select a standup and the \"My preferences\" button will take you to the customisation page. (This setting may not be enabled on all standups.)\n- You can export a list of your previous responses to a standup from the dashboard. Click on the \"timeline\" tab of the relevant standup, and from there you can select the participants and questions to export to a csv file. This is particularly useful when you have forgotten to fill in Harvest for a while and need the information from the daily standup!\n\nOther workspaces you could join:\n\n- The Society of Research Software Engineering has a [Slack worspace](https://ukrse.slack.com/join/signup). For information about joining the Society see the [SocRSE page]({{< relref \"../society_of_research_software_engineering.md\" >}}).\n\n### Mailing Lists\n\nThe [Newsletters & Mailings](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/service?unid=986c9e4b9a354a06a587917fd3e0f481) page on Turing Complete has a \"Update Mailing Preferences\" link to sign up to receive some internal newsletters/communication.\n\n### Harvest and Forecast\n\nWe use [Harvest](https://thealanturinginstitute.harvestapp.com/time/week) to (loosely) track our time, and [Forecast](https://forecastapp.com/974183/schedule/projects?startDate=2020-12-28) to manage allocations to projects.\nGet in touch with [the person in charge of researcher tool management](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) to setup a meeting sometime in your first week.\n\nHarvest is a time tracking app used to monitor the amount of time being dedicated to each project.\nThe easiest way to get set up is to ask your Line Manager or another member of the team to invite you and make you admin.", "start_char_idx": 6548, "end_char_idx": 8392, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "201432b8-c6a7-4e53-9ae1-5e5e225aaca1": {"__data__": {"id_": "201432b8-c6a7-4e53-9ae1-5e5e225aaca1", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "65522fe5-fb80-43d0-8032-255f08e75db4", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "65b3fe18d0a0300d47ec3c17270feb6a380708078f4a9bbac4163883ccca2fbe", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5c092d4a-1a6a-42fa-9c88-2c89a652a0c3", "node_type": "1", "metadata": {}, "hash": "836c8fb04423eb4495483a050be92b92351fe4fddd2d71b0d5a4a8dd6626b23a", "class_name": "RelatedNodeInfo"}}, "text": "### Harvest and Forecast\n\nWe use [Harvest](https://thealanturinginstitute.harvestapp.com/time/week) to (loosely) track our time, and [Forecast](https://forecastapp.com/974183/schedule/projects?startDate=2020-12-28) to manage allocations to projects.\nGet in touch with [the person in charge of researcher tool management](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities) to setup a meeting sometime in your first week.\n\nHarvest is a time tracking app used to monitor the amount of time being dedicated to each project.\nThe easiest way to get set up is to ask your Line Manager or another member of the team to invite you and make you admin.\nYou can then add yourself as a team member to whichever projects you're working on and start tracking your time.\nThere is a page [here](https://github.com/alan-turing-institute/Hut23/wiki/Timesheets) which goes into more detail about where you should log different activities.\n\n### Cezanne HR\n\nOur HR system is [Cezanne HR](https://w3.cezanneondemand.com/CezanneOnDemand/-/turing/).\nYou can log onto this with your Turing credentials using the \"Enterprise login\" option.\nIf \"Enterprise login\" does not work, email\n[HR](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) to fix it.\n\nThis system is used for:\n\n- Requesting annual leave.\n- Recording days off due to illness *etc.*.\n- Managing the objective setting and performance review process (after your probation period).\n\n### Create a Public Profile on the Turing Website\n\nThere are several ways to do this:\n\n1. You can fill in [this form on Turing Complete](https://turingcomplete.topdesk.net/tas/public/ssp/content/serviceflow?unid=35b8d40067004f9484c9fb06ade41d65) to request a public profile.\n1.", "start_char_idx": 7698, "end_char_idx": 9499, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5c092d4a-1a6a-42fa-9c88-2c89a652a0c3": {"__data__": {"id_": "5c092d4a-1a6a-42fa-9c88-2c89a652a0c3", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "201432b8-c6a7-4e53-9ae1-5e5e225aaca1", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "fb3fa3646b9ee9a2e462190f6e9d303f2f4d574585abfb8005bbbd15b27ff52d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "0670d79d-270b-40e6-8027-86d4c763781c", "node_type": "1", "metadata": {}, "hash": "01f9ef024dbd48bdbd6bcd16302e0ac80f30a82f2e9d83ed665bffa2ef4ceaaa", "class_name": "RelatedNodeInfo"}}, "text": "You can log onto this with your Turing credentials using the \"Enterprise login\" option.\nIf \"Enterprise login\" does not work, email\n[HR](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) to fix it.\n\nThis system is used for:\n\n- Requesting annual leave.\n- Recording days off due to illness *etc.*.\n- Managing the objective setting and performance review process (after your probation period).\n\n### Create a Public Profile on the Turing Website\n\nThere are several ways to do this:\n\n1. You can fill in [this form on Turing Complete](https://turingcomplete.topdesk.net/tas/public/ssp/content/serviceflow?unid=35b8d40067004f9484c9fb06ade41d65) to request a public profile.\n1. Alternatively, contact [the people in charge of REG communications](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#responsibilities), providing the following info:\n   - Your title and full name\n   - A short personal bio\n   - A brief description of your Turing-related research\n   - *(optional)* A brief description of your achievements and award\n   - *(optional)* Photo\n   - *(optional)* Webpage\n1. You can also edit the website yourself directly. To do so, you must complete [a round of online training](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/knowledgeitem?unid=45626012-2413-4371-8de1-8aa056d35381), after which you will be granted edit permissions.\n\n### Office 365 Groups\n\nMake sure you are in the following Office365 / email groups (check with\n[IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact)):\n\n- Research engineering\n- Business team (for non-Research Associate staff)\n- Research staff\n- All Staff\n- If you are a senior member of staff, ask a principal to get you added to the REG-senior-staff email list.", "start_char_idx": 8776, "end_char_idx": 10636, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0670d79d-270b-40e6-8027-86d4c763781c": {"__data__": {"id_": "0670d79d-270b-40e6-8027-86d4c763781c", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5c092d4a-1a6a-42fa-9c88-2c89a652a0c3", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "6941fc08c36e72b055825dfdeb59dbea6d8affda95547debecbbaaf8d3422c2f", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "9339280a-09ea-42e5-b8f0-5ae819759d6f", "node_type": "1", "metadata": {}, "hash": "7b42ce4a9961f891f031096aacd995daa332c23bdf37c0b7d9820fe5f6e25956", "class_name": "RelatedNodeInfo"}}, "text": "You can also edit the website yourself directly. To do so, you must complete [a round of online training](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/knowledgeitem?unid=45626012-2413-4371-8de1-8aa056d35381), after which you will be granted edit permissions.\n\n### Office 365 Groups\n\nMake sure you are in the following Office365 / email groups (check with\n[IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact)):\n\n- Research engineering\n- Business team (for non-Research Associate staff)\n- Research staff\n- All Staff\n- If you are a senior member of staff, ask a principal to get you added to the REG-senior-staff email list.\n- End-of-month all business team meetings (Turing Catch-ups). These are sent out as recurring calendar events. You can either email [governance](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) to forward you the invite or find a member of the REG team that is invited and ask them to forward the invite.\n- Turing Bulletin: This internal newsletter is sent out every Thursday evening. If you don't receive it, you can sign up manually via [Turing Complete](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/service?unid=986c9e4b9a354a06a587917fd3e0f481).\n\n### GitHub Pro and Professional IDEs\n\nTuring employees can apply for free GitHub Pro accounts, and use these to get free educational licences for professional versions of many JetBrains products (*e.g.*, PyCharm, IntelliJ, \u2026)\n\n1. Go to [education.github.com](https://education.github.com) and apply for the GitHub Teacher Toolbox\n1. GitHub will ask you to verify your affiliation with an academic institution. The Turing is not on their list, but you can still use it.", "start_char_idx": 9938, "end_char_idx": 11732, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9339280a-09ea-42e5-b8f0-5ae819759d6f": {"__data__": {"id_": "9339280a-09ea-42e5-b8f0-5ae819759d6f", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "0670d79d-270b-40e6-8027-86d4c763781c", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "3acdcaab956f524f9789f8f5590eb839c13883f666f14923d8b5aef81e041177", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "6cb2c28c-4858-4f80-b7af-c763074c72db", "node_type": "1", "metadata": {}, "hash": "2f0e0e1140957c865f981c4cee3dd7238df151ed6e8a68fb080707154bbe7642", "class_name": "RelatedNodeInfo"}}, "text": "- Turing Bulletin: This internal newsletter is sent out every Thursday evening. If you don't receive it, you can sign up manually via [Turing Complete](https://turingcomplete.topdesk.net/tas/public/ssp/content/detail/service?unid=986c9e4b9a354a06a587917fd3e0f481).\n\n### GitHub Pro and Professional IDEs\n\nTuring employees can apply for free GitHub Pro accounts, and use these to get free educational licences for professional versions of many JetBrains products (*e.g.*, PyCharm, IntelliJ, \u2026)\n\n1. Go to [education.github.com](https://education.github.com) and apply for the GitHub Teacher Toolbox\n1. GitHub will ask you to verify your affiliation with an academic institution. The Turing is not on their list, but you can still use it. You will need proof of employment - a screenshot of Cezanne or the top of your Turing contract (including start date) have worked for people in the past.\n1. Once GitHub have approved your Pro account, go to the JetBrains website and create an account there (sign in via GitHub)\n1. You can now apply for a free educational licence (\"Apply for a free student or teacher license\") for professional versions of various IDEs.\n\n{{< hint info >}}\nThere is no obligation to use these IDEs, this is just an FYI in case you are interested.\n{{< /hint >}}\n\n### OneDrive\n\nSync the Research Engineering documents: From [SharePoint](https://thealanturininstitute.sharepoint.com/sites/ResearchEngineering), under \"Research Engineering\" private group, go to \"Documents\", then \"Sync\".\n\nPlease avoid locally editing files that are common and might be edited concurrently.\nIn that case, opt to use the online version through Office 365.\n\n### HackMD\n\nWe sometimes use HackMD for shared documents.\nIf you don't have an account, signup for [a free account here](https://hackmd.io/).\nWe do not have a paid account for HackMD.\n\n### Skype for business\n\nWe don't use skype that often, but this can be installed through the Self Service application.\nYou will need an application-specific password.", "start_char_idx": 10998, "end_char_idx": 13002, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "6cb2c28c-4858-4f80-b7af-c763074c72db": {"__data__": {"id_": "6cb2c28c-4858-4f80-b7af-c763074c72db", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "9339280a-09ea-42e5-b8f0-5ae819759d6f", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8de5876bec36c1cc4197147328ec828baa5797ed8ca4ee90aaeb6d843d4b15ec", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "8cb68461-551d-44b1-bd45-789feee27927", "node_type": "1", "metadata": {}, "hash": "3d1cf8b3396e1c220cec8a3a6717a58cd8da7609cc5ef3c5558a0c292b963315", "class_name": "RelatedNodeInfo"}}, "text": "{{< hint info >}}\nThere is no obligation to use these IDEs, this is just an FYI in case you are interested.\n{{< /hint >}}\n\n### OneDrive\n\nSync the Research Engineering documents: From [SharePoint](https://thealanturininstitute.sharepoint.com/sites/ResearchEngineering), under \"Research Engineering\" private group, go to \"Documents\", then \"Sync\".\n\nPlease avoid locally editing files that are common and might be edited concurrently.\nIn that case, opt to use the online version through Office 365.\n\n### HackMD\n\nWe sometimes use HackMD for shared documents.\nIf you don't have an account, signup for [a free account here](https://hackmd.io/).\nWe do not have a paid account for HackMD.\n\n### Skype for business\n\nWe don't use skype that often, but this can be installed through the Self Service application.\nYou will need an application-specific password.\n\nIf you want a phone number ask [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact).\n\n### Mathison\n\n[Mathison](https://mathison.turing.ac.uk/) is the Institute's intranet.\nYou should have access to this on your first day.\nA few things still need to be done on the old intranet, [Turing Complete](https://turingcomplete.topdesk.net).\n\n### Gray Dawes\n\nGray Dawes is the Turing's system for booking travel and accommodation.\nInstructions on how to get set up can be found [on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2738).\n\nWhen making bookings on Gray Dawes, you will have to additionally submit an Excel approval form (available at the right-hand sidebar of the same Mathison page).\nThis form should be filled in and sent to the Principal you have a direct link to in the line management chain for approval.", "start_char_idx": 12155, "end_char_idx": 13907, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "8cb68461-551d-44b1-bd45-789feee27927": {"__data__": {"id_": "8cb68461-551d-44b1-bd45-789feee27927", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "6cb2c28c-4858-4f80-b7af-c763074c72db", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "0be5e83486f5e23d3011ffa7e14b9a58c4fa39a87763412b5cf615786e42fc5e", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "72157987-ccc4-4b00-b6a9-4206c1635c62", "node_type": "1", "metadata": {}, "hash": "e1b0f95b969edcf0071c8a535b6bb4e361f138c770b2aa8f38e8bf5920dfe8b0", "class_name": "RelatedNodeInfo"}}, "text": "### Mathison\n\n[Mathison](https://mathison.turing.ac.uk/) is the Institute's intranet.\nYou should have access to this on your first day.\nA few things still need to be done on the old intranet, [Turing Complete](https://turingcomplete.topdesk.net).\n\n### Gray Dawes\n\nGray Dawes is the Turing's system for booking travel and accommodation.\nInstructions on how to get set up can be found [on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2738).\n\nWhen making bookings on Gray Dawes, you will have to additionally submit an Excel approval form (available at the right-hand sidebar of the same Mathison page).\nThis form should be filled in and sent to the Principal you have a direct link to in the line management chain for approval.\n\n### Certify\n\nOther work-related expenses can be reimbursed via Certify: see [the Mathison page](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2169) for more information on how to get set up.\nBriefly, you will have to fill in a Travel and Expenses Policy Compliance Form and email it to Finance.\nOn this form, the 'approver' should be the REG Director.\n\nThe [REG Wiki](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses) contains more information on the reimbursement process, including information about the specific department and project codes you should be using.\nWhen you get access to Certify, it's a good idea to check that you've been set up with the correct codes.\n\nIf you applied for a visa to work at the Turing, you are also allowed to claim up to \u00a34000 back.\nMore details on this can be found in [Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2270) as well as the [REG Wiki](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses#visa-costs).", "start_char_idx": 13147, "end_char_idx": 15029, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "72157987-ccc4-4b00-b6a9-4206c1635c62": {"__data__": {"id_": "72157987-ccc4-4b00-b6a9-4206c1635c62", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "9800b627994c5343abeec748c5811347d67f934a", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "8487d42c66e67263da23bea683f839aadbd1ac8488de7fdb2c29a2e8d9a4d28b", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "8cb68461-551d-44b1-bd45-789feee27927", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/new_joiners/systems_set_up.md", "file_name": "systems_set_up.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/new_joiners/systems_set_up.md"}, "hash": "80b7cc694e56706855d834988e08316e8938fd785ca58ca449a1e05607cdbb84", "class_name": "RelatedNodeInfo"}}, "text": "On this form, the 'approver' should be the REG Director.\n\nThe [REG Wiki](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses) contains more information on the reimbursement process, including information about the specific department and project codes you should be using.\nWhen you get access to Certify, it's a good idea to check that you've been set up with the correct codes.\n\nIf you applied for a visa to work at the Turing, you are also allowed to claim up to \u00a34000 back.\nMore details on this can be found in [Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2270) as well as the [REG Wiki](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses#visa-costs).\n\n## Reference\n\nTake a look at the following sections:\n\n- [Regular Events]({{< relref \"/docs/regular_events/_index.md\" >}})\n- [Common Technical Practices]({{< relref \"/docs/technical_practices/_index.md\" >}})\n- [Equipment]({{< relref \"/docs/working_at_the_turing/equipment.md\" >}})", "start_char_idx": 14228, "end_char_idx": 15310, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d0309d4e-01b1-4bc1-94c9-af1c07a0a294": {"__data__": {"id_": "d0309d4e-01b1-4bc1-94c9-af1c07a0a294", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/society_of_research_software_engineering.md", "file_name": "society_of_research_software_engineering.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/society_of_research_software_engineering.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "86410ea05e1cb71a248b3b7c33a57ed0fdb1b6a6", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/society_of_research_software_engineering.md", "file_name": "society_of_research_software_engineering.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/society_of_research_software_engineering.md"}, "hash": "4676d4342348d41fa33343a45a9009cd0fc1bd9d77deb1bb866a5dd8febcd842", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "05a15588-4b09-40af-8e2e-e3cfc444a25d", "node_type": "1", "metadata": {}, "hash": "bdf815987dd8d1c4c3ba82b1b3cce1b7b89e351b85a8eefa55f10a35edb565ea", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"SocRSE\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Society of Research Software Engineering\n\nThe [Society of Research Software Engineering](https://society-rse.org/) (SocRSE) is a charitable incorporated organisation\nwith the mission to establish a research environment that recognises the vital role of software in research.\n\nREG supports and encourages its members to join the SocRSE, so it's worth reviewing the society's website and deciding whether to join during your onboarding period.\n\n## Benefits of SocRSE\n\nThere are many benefits to joining the SocRSE. By joining the Society you will:\n\n- Contribute to [the Society's work](https://society-rse.org/community/) supporting the research software engineering community\n- Gain access to the Society's [Events & Initiatives fund](https://society-rse.org/policy-for-socrse-events-and-initiatives-grant/)\n- Get discounted access to the Society's annual conference\n- Be eligible to vote and stand for election at the Society's annual AGM\n- Have your say in one of the Society's [special interest, regional or working groups](https://society-rse.org/community/get-involved/)\n- Be able to apply for the Society\u2019s [mentorship scheme](https://society-rse.org/get-involved/mentoring-scheme/)\n- Gain access to discounts from the Society's corporate sponsors\n\n## Communication Channels\n\nThe Society has a website, Slack and email list.\nTheir [Slack workspace](https://ukrse.slack.com/join/signup) contains plenty of details about relevant events.\n\n## Claiming Back Subscriptions\n\nSocRSE membership is considered a professional subscription, which you are entitled to claim back as a REG member.\n\nYou should claim it back quickly after payment.", "start_char_idx": 0, "end_char_idx": 2025, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "05a15588-4b09-40af-8e2e-e3cfc444a25d": {"__data__": {"id_": "05a15588-4b09-40af-8e2e-e3cfc444a25d", "embedding": null, "metadata": {"file_path": "content/docs/onboarding/society_of_research_software_engineering.md", "file_name": "society_of_research_software_engineering.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/society_of_research_software_engineering.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "86410ea05e1cb71a248b3b7c33a57ed0fdb1b6a6", "node_type": "4", "metadata": {"file_path": "content/docs/onboarding/society_of_research_software_engineering.md", "file_name": "society_of_research_software_engineering.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/society_of_research_software_engineering.md"}, "hash": "4676d4342348d41fa33343a45a9009cd0fc1bd9d77deb1bb866a5dd8febcd842", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "d0309d4e-01b1-4bc1-94c9-af1c07a0a294", "node_type": "1", "metadata": {"file_path": "content/docs/onboarding/society_of_research_software_engineering.md", "file_name": "society_of_research_software_engineering.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/onboarding/society_of_research_software_engineering.md"}, "hash": "4b8639d0085b4b63ab1984d76c6ea3995b43f647a9e5831751ac062c21acbcbd", "class_name": "RelatedNodeInfo"}}, "text": "Their [Slack workspace](https://ukrse.slack.com/join/signup) contains plenty of details about relevant events.\n\n## Claiming Back Subscriptions\n\nSocRSE membership is considered a professional subscription, which you are entitled to claim back as a REG member.\n\nYou should claim it back quickly after payment.\nUse Certify to claim it back following [these instructions](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses#allocating-your-expenses-to-the-right-budget).\nNote that this expense is not directly related to a project.", "start_char_idx": 1718, "end_char_idx": 2300, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "af06e711-3177-4400-bb92-291df03e30dd": {"__data__": {"id_": "af06e711-3177-4400-bb92-291df03e30dd", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3572fbb8f0bfccfb953996dc8599e300641c5280", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/_index.md"}, "hash": "3e4b4034100af16cde8ca1c6f891111f7db3d54338aa267f0c04ab9441132fa0", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Regular Events\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exlude this page from the search\n# bookSearchExclude = true\n---\n\n# Regular Events\n\n## REG Events\n\n- [Lunchtime tech talks]({{< relref \"/docs/regular_events/lunchtime_tech_talks.md\" >}})\n- [Lightning talks]({{< relref \"/docs/regular_events/lightning_talks.md\" >}})\n- [Reading groups]({{< relref \"/docs/regular_events/reading_groups.md\" >}})\n- [Open source hack sessions]({{< relref \"/docs/regular_events/open_source_hacksessions.md\" >}})\n\n## Outward-facing Events\n\n- [Drop-in Sessions]({{< relref \"/docs/regular_events/drop-in_sessions.md\" >}})\n\n## Coffee Chats\n\n- [Randomised coffee chats]({{< relref \"/docs/regular_events/coffee_chats.md\" >}})", "start_char_idx": 0, "end_char_idx": 972, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b9b977cf-0c14-4e1e-bc45-e7620f4d37f2": {"__data__": {"id_": "b9b977cf-0c14-4e1e-bc45-e7620f4d37f2", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/coffee_chats.md", "file_name": "coffee_chats.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/coffee_chats.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3db69996a4f564759314b78f68e574789fa6e81f", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/coffee_chats.md", "file_name": "coffee_chats.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/coffee_chats.md"}, "hash": "34e811683154c6dd3946d6f3dd94d7ff9833292403543cdf00e1aa96e641958f", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Randomised Coffee Chats\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 60\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exlude this page from the search\n# bookSearchExclude = true\n---\n\n# Randomised Coffee Chats\n\nApproximately every 2 weeks, we partition REG into groups of 4 or 5 for informal chats.\nThe groups are randomly generated (see the code [here](https://github.com/alan-turing-institute/randoffee/)) but generally try to ensure that everyone meets different people each week.\n\nIf you have not signed up already and would like to join (or conversely if you would like to opt out), please inform the [people in charge of the random coffees](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry).\n\n{{% hint info %}}\nSeveral years ago, during the periods of lockdown, we used to have daily coffee chats at 08:45 and 13:45.\nThese are very rarely used nowadays but the vestiges of these can still be seen in (for example) the daily notifications on Slack every morning.\n{{% /hint %}}", "start_char_idx": 0, "end_char_idx": 1237, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a2d3b1f4-ad42-4e85-9984-240e9e1ad08d": {"__data__": {"id_": "a2d3b1f4-ad42-4e85-9984-240e9e1ad08d", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "246a8e452680dcd30e6b1492db1ece74b14b8843", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "hash": "13315e94b9c5e3979e48d153d40a8b384948da67eaaf085281e803575d3a60e3", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "a130bafd-4da5-4b34-8ec6-a49e3f6f5d97", "node_type": "1", "metadata": {}, "hash": "5433e7c245b8695c0404caa46a3d4fbf7dd6f32b4f9ff1691ee8368f685cdc14", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Drop-in Sessions\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 50\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Drop-in Sessions\n\nThe group runs regular, informal, community-led sessions for students and researchers.\nWhether you have general questions on a topic, or are seeking specific technical guidance on, or just want to chat with friendly faces, our drop-in session is the perfect place for you!\nThe sessions offer a friendly, welcoming, non-judgemental environment to receive advice or assistance without concern about being judged on technical knowledge, expertise or understanding.\n\nAs our organisers and helpers know, many of us working with software in the research community have been trained in domains where computing has not traditionally been a key element.\nThere can be lots to learn in terms of new jargon and technical skills and we all have to start somewhere.", "start_char_idx": 0, "end_char_idx": 1168, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "a130bafd-4da5-4b34-8ec6-a49e3f6f5d97": {"__data__": {"id_": "a130bafd-4da5-4b34-8ec6-a49e3f6f5d97", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "246a8e452680dcd30e6b1492db1ece74b14b8843", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "hash": "13315e94b9c5e3979e48d153d40a8b384948da67eaaf085281e803575d3a60e3", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a2d3b1f4-ad42-4e85-9984-240e9e1ad08d", "node_type": "1", "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "hash": "df5464bef0c933a73978c1c5f27106af8edf534c17d5976d4b0ad141c36722b5", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "4a91eaf3-2fa4-4374-a2aa-f9bc3cd25988", "node_type": "1", "metadata": {}, "hash": "4a88cbea08f11946bdbd1a29be4bcc453e72b4a2d12b81163f65022501496d38", "class_name": "RelatedNodeInfo"}}, "text": "Whether you have general questions on a topic, or are seeking specific technical guidance on, or just want to chat with friendly faces, our drop-in session is the perfect place for you!\nThe sessions offer a friendly, welcoming, non-judgemental environment to receive advice or assistance without concern about being judged on technical knowledge, expertise or understanding.\n\nAs our organisers and helpers know, many of us working with software in the research community have been trained in domains where computing has not traditionally been a key element.\nThere can be lots to learn in terms of new jargon and technical skills and we all have to start somewhere.\n\nA few examples of what we can help out with are:\n\n- General programming problems, including:\n    - Identifying and fixing bugs\n    - Setting up Python packages and environments\n- Cloud computing questions, including:\n    - How to get started on Microsoft Azure\n    - How to deploy code to the cloud\n    - Cloud computing and storage costs\n    - Getting access to GPU VMs on Microsoft Azure\n- Research Data Science issues, including:\n    - Data collection and selection process\n    - Experimental design (methods, baselines, metrics, ablation studies)\n    - Moving from a general idea to a machine learning pipeline\n- Software sustainability\n    - Best practices for testing, managing and packaging your code\n- Reproducible research\n    - Ensuring software is developed to support long-term reproducibility\n    - Including techniques for packaging and archiving code and data\n- High Performance Computing (HPC) support:\n    - HPC services resources available at the Turing\n    - HPC performance (parallellism, libraries, compilers)\n\n## Joining sessions\n\nOur sessions are on [Zoom](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#video-call-links) and sometimes hybrid.\nWe run several sessions a week (typically on Tuesdays and Wednesdays), so please see our [schedule](https://github.com/alan-turing-institute/research-engineering-group/wiki/Drop%E2%80%90in-Sessions#schedule) on the REG wiki.\nAlso feel free to reach out to either Ryan Chan, Iain Stenson or Fede Nanni on Slack.", "start_char_idx": 504, "end_char_idx": 2686, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "4a91eaf3-2fa4-4374-a2aa-f9bc3cd25988": {"__data__": {"id_": "4a91eaf3-2fa4-4374-a2aa-f9bc3cd25988", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "246a8e452680dcd30e6b1492db1ece74b14b8843", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "hash": "13315e94b9c5e3979e48d153d40a8b384948da67eaaf085281e803575d3a60e3", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "a130bafd-4da5-4b34-8ec6-a49e3f6f5d97", "node_type": "1", "metadata": {"file_path": "content/docs/regular_events/drop-in_sessions.md", "file_name": "drop-in_sessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/drop-in_sessions.md"}, "hash": "8c06d8700975e9135d746a2c41de93e788f66d407f5b97b3428b5373223d0e4d", "class_name": "RelatedNodeInfo"}}, "text": "We run several sessions a week (typically on Tuesdays and Wednesdays), so please see our [schedule](https://github.com/alan-turing-institute/research-engineering-group/wiki/Drop%E2%80%90in-Sessions#schedule) on the REG wiki.\nAlso feel free to reach out to either Ryan Chan, Iain Stenson or Fede Nanni on Slack.\n\nOn the schedule, you can see who is hosting the session and a short bio about them.\n\nFeel free to message the [organisers](https://github.com/alan-turing-institute/research-engineering-group/wiki/Drop%E2%80%90in-Sessions#organisers) ahead of time with your problem on Slack, or just come by!\nThere is no need to register in advance.", "start_char_idx": 2376, "end_char_idx": 3020, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "8e3d6776-d318-4d99-bd67-b95466d95eb5": {"__data__": {"id_": "8e3d6776-d318-4d99-bd67-b95466d95eb5", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/lightning_talks.md", "file_name": "lightning_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lightning_talks.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8c29ad5d6c90b79fb4c04af861d5b5985a2f1609", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/lightning_talks.md", "file_name": "lightning_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lightning_talks.md"}, "hash": "545c258677951d8648e2fddfffe08afefaa32c1e047a55804b3f0a65fc54abca", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "13693d9f-add0-4831-b24e-18baf9b8be19", "node_type": "1", "metadata": {}, "hash": "bb57864cb01aa0ec4acbc718962ab1449ac41fdcdb1ed81dbcf5408e84bc06b9", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Lightning Talks\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 20\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Lightning Talks\n\nLightning Talks are a meeting series within the tech talk slots in which people give short 10 minute talks.\nThe main goals are to:\n\n1. Regularly share what we're working on.\n1. Find overlaps between projects or problems someone else in the team could help with.\n1. Generate ideas for future tech talks.\n1. Do all this without a big time commitment, both for presenters and attendees.\n\nThe talks can be on any topic, including but not limited to:\n\n- A project (including one that hasn't started yet).\n- A tool or technique you use.\n- Something you'd like help with.\n\nInformal talks or discussions without slides or much preparation are encouraged and very welcome!\n\n## Talk Format\n\nEach slot will be **10 minutes** with a few minutes after for questions.\nWe will be fairly strict with time to make sure everyone on the agenda gets to present.\n\nYou can choose whatever format you like (slides, markdown, notebook, freestyle\u2026), but remember that it's a short slot which lends itself to concise, high-level overviews of a topic.\nFor example, if your talk is on a project you may wish to cover the following:\n\n1. Background: The domain area, context, problem _etc._.\n1. Goals: What are the aims of the project?\n1. Status: What's been done so far, example results (if any).\n1. Tools & Techniques: What you're using, what you've learnt, what you'd like help with.\n1. Summary: who's working on the project, dates, GitHub link _etc._.", "start_char_idx": 0, "end_char_idx": 1840, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "13693d9f-add0-4831-b24e-18baf9b8be19": {"__data__": {"id_": "13693d9f-add0-4831-b24e-18baf9b8be19", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/lightning_talks.md", "file_name": "lightning_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lightning_talks.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8c29ad5d6c90b79fb4c04af861d5b5985a2f1609", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/lightning_talks.md", "file_name": "lightning_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lightning_talks.md"}, "hash": "545c258677951d8648e2fddfffe08afefaa32c1e047a55804b3f0a65fc54abca", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "8e3d6776-d318-4d99-bd67-b95466d95eb5", "node_type": "1", "metadata": {"file_path": "content/docs/regular_events/lightning_talks.md", "file_name": "lightning_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lightning_talks.md"}, "hash": "cbff7b904c72ac0426e5a9aa8023d316ec2c51a37a13131e3d4ce3126f1ca378", "class_name": "RelatedNodeInfo"}}, "text": "## Talk Format\n\nEach slot will be **10 minutes** with a few minutes after for questions.\nWe will be fairly strict with time to make sure everyone on the agenda gets to present.\n\nYou can choose whatever format you like (slides, markdown, notebook, freestyle\u2026), but remember that it's a short slot which lends itself to concise, high-level overviews of a topic.\nFor example, if your talk is on a project you may wish to cover the following:\n\n1. Background: The domain area, context, problem _etc._.\n1. Goals: What are the aims of the project?\n1. Status: What's been done so far, example results (if any).\n1. Tools & Techniques: What you're using, what you've learnt, what you'd like help with.\n1. Summary: who's working on the project, dates, GitHub link _etc._.\n\nIf you'd like to prepare slides, there's a PowerPoint template and examples from previous talks under [here](https://thealanturininstitute.sharepoint.com/:f:/s/ResearchEngineering/EvFtcBhG1RhKofdqD3HNE48BIG443DyTMeRoEwuY453Vcg?e=zv1aZT) that can help you get started.\n\n## Sign Up\n\nTo sign up to present a Lightning Talk, please add yourself to the [signup\nlist](https://github.com/alan-turing-institute/research-engineering-group/wiki/Lightning-Talks#signup-list)\n\n## Previous Talks\n\nYou can find a list of previous talks and their slides [here](https://github.com/alan-turing-institute/research-engineering-group/wiki/Lightning-Talks%3A-History-of-Talks).", "start_char_idx": 1080, "end_char_idx": 2498, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "70b4a312-c669-4439-89e1-6db8aaadb025": {"__data__": {"id_": "70b4a312-c669-4439-89e1-6db8aaadb025", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/lunchtime_tech_talks.md", "file_name": "lunchtime_tech_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lunchtime_tech_talks.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "2eb3fa13f1a8f4a51bb717fb851b558b1c945567", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/lunchtime_tech_talks.md", "file_name": "lunchtime_tech_talks.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/lunchtime_tech_talks.md"}, "hash": "424e48b3a601a0a49b790ae6ace0531af753cdf3b2547f895c562fdc8b99a750", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Lunchtime Tech Talks\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 10\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Lunchtime Tech Talks\n\nThe Research Engineering team meets for Lunchtime Tech Talks most Tuesdays over lunch.\nThe talks start at 12:30.\n\nAt a tech talk someone (usually someone in REG) presents or discusses something, such as about a project or data science/computer science/software engineering topic.\nThey can also be opportunities to look for help and input.\nWe encourage talks at the beginning of projects (before it's certain what exactly you'll be doing) or to discuss a problem you're unsure how to solve.\nWe also have shorter format [lightning talks]({{< relref \"docs/regular_events/lightning_talks.md\" >}}), where several people give a shorter talk.\n\n[The Data Science Skills wiki](https://github.com/alan-turing-institute/DataScienceSkills/wiki/Lunchtime-Tech-Talks) contains the talks schedule, calendar invitation instructions and information on signing up for a talk.", "start_char_idx": 0, "end_char_idx": 1282, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "50dccdcb-69c2-41fa-9490-9234095132f3": {"__data__": {"id_": "50dccdcb-69c2-41fa-9490-9234095132f3", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/open_source_hacksessions.md", "file_name": "open_source_hacksessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/open_source_hacksessions.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "40e5ef8b88305799557e778421610c28154b50cb", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/open_source_hacksessions.md", "file_name": "open_source_hacksessions.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/open_source_hacksessions.md"}, "hash": "64b12ac914d193cdb160797095de17ed92dc79aeccd5aa7e24e39bd7d4941bee", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Open Source Hacksessions\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 40\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Open Source Hacksessions\n\nREG runs monthly sessions where we come together and contribute to upstream open-source repositories. The idea is to fence off time dedicated to contributing upstream and to do it together for joy and sociality. We hope to give back to many of the tools we use and learn about new packages by helping them out. Everyone is welcome to come and work on something they want to contribute to or join an effort someone else has already started.\n\nThe sessions take place from **2-5 pm every third Friday of the month** and are organised by REG\u2019s Open Source Service Area. If you have any questions, or want to join but don't have the calendar invite you can contact them by [email](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#regular-events) or on Slack (`#open-source-hacksessions`). If you come to an OS hacksession and end up opening a PR or otherwise making a contribution please tell us about it, either on Slack or by email.\n\nYou can check the list of contributions these hacksessions have led to so far [here](https://github.com/alan-turing-institute/research-engineering-group/wiki/Open-source-hacksessions).", "start_char_idx": 0, "end_char_idx": 1585, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "56f95194-e0bf-4d86-bef9-c4112ef570db": {"__data__": {"id_": "56f95194-e0bf-4d86-bef9-c4112ef570db", "embedding": null, "metadata": {"file_path": "content/docs/regular_events/reading_groups.md", "file_name": "reading_groups.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/reading_groups.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "f9f40be4347c262e85e6a9312f4253889db82a4b", "node_type": "4", "metadata": {"file_path": "content/docs/regular_events/reading_groups.md", "file_name": "reading_groups.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/regular_events/reading_groups.md"}, "hash": "c35d4921574091fd7cb26841365c7aad1b4f67e86ba145e97e5a42f64e76a187", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Reading Groups\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 35\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Reading Groups\n\nAs part of projects or for general interest, members of the team create reading groups to learn about a topic.\nReading groups may meet on weekly basis to discuss a chapter from a book, a paper, or to work together on implementing something.\n\nA list of current reading groups and information about them can be found on the [Data Science Skills wiki](https://github.com/alan-turing-institute/DataScienceSkills/wiki/Reading-Groups).\n\nMaterial for the reading groups is stored in the [Data Science Skills repository](https://github.com/alan-turing-institute/DataScienceSkills)\n\nThere are also [Turing Interest Groups](https://www.turing.ac.uk/research/interest-groups) and many other informal groups around the Turing.", "start_char_idx": 0, "end_char_idx": 1127, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e8716438-9709-458f-84d4-b7d3fbde4130": {"__data__": {"id_": "e8716438-9709-458f-84d4-b7d3fbde4130", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b00d7c4c21f6e6a325f524411155361fe4ffe408", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/_index.md"}, "hash": "4e861a1c991b1eb12ae3a052e865975f6c05c51e4fd4c3dd6f729fdd58b69d75", "class_name": "RelatedNodeInfo"}}, "text": "---\ntitle: \"Common Technical Practices\"\nweight: 6\n---\n\n# Common Technical Practices\n\nThis section contains a section of common technical practices within REG.\nFor many of the topics in this section, there may be many possible ways to achieve the same goal \u2014 which are debated at length elsewhere.\nThe intention of this section is not to claim that any of the methods here are the \"best\" (however defined).\nMerely that they are good enough to allow the reader to focus on other, more interesting, unique or important, aspects of their project.", "start_char_idx": 0, "end_char_idx": 542, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5152f868-b29a-47d3-b9d8-b00239673c47": {"__data__": {"id_": "5152f868-b29a-47d3-b9d8-b00239673c47", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/change_logs.md", "file_name": "change_logs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/change_logs.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "3ce2d5776d7d6d036e7c745b3343f6720d64905c", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/change_logs.md", "file_name": "change_logs.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/change_logs.md"}, "hash": "49494fa24b5705e745ec0629640452b92b8102073e7de6f0f955ff4bff323bbf", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Changelogs and Semantic Versioning\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n---\n\n# Changelogs\n\nFor any non-trivial code, it is worth maintaining a Changelog.\nThis is valuable as it is a record of changes _from the perspective of users_ (rather than git commit messages which are often only meaningful to the developers involved).\nA recommended format for Changelog files is given at [Keep a changelog](https://keepachangelog.com).\nAll notable changes to a project should be documented in this file.\n\n## Semantic Versioning\n\nA related concept is [semantic versioning](https://semver.org/spec/v2.0.0.html).\nIn most cases, semantic versioning is recommended.\nYou should state explicitly whether or not you are using semantic versioning in both your project's README and CHANGELOG files.", "start_char_idx": 0, "end_char_idx": 882, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "998a3b4c-d069-4bed-a7aa-9874021d7572": {"__data__": {"id_": "998a3b4c-d069-4bed-a7aa-9874021d7572", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "d692095e-53cf-4583-969f-34d0abcb3eb9", "node_type": "1", "metadata": {}, "hash": "229beeda9963483c6356e8f4b2f93ce85be56505805abc524cdafcf0c2d6f2aa", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Configuring Your Editor\"\n# Adjust the weight to reorder menu items (lower numbers appear first)\nweight: 4\n---\n\n# Configuring Your Editor\n\nMany people will configure their editor to suit their own needs and preferences.\n\nIt is possible to do this so that there are common standards between collaborators in the code base, while allowing for differences in individuals' configurations.\n\n{{< hint warning >}}\nSome of the topics discussed below are heavily focused on python development. In due course we should generalise this to cover other languages.\n{{< /hint >}}\n\n## General\n\nSome common, particular formatting/encoding options can be configured for a range of editors/IDEs by using [EditorConfig](https://editorconfig.org) in your repo.\nA `.editorconfig` needs to be placed in the root of your repo.\n\nMany (but not all) editors support EditorConfig files allowing different members of your project team to use their own favourite editor while maintaining common coding standards.\nSome editors support EditorConfig natively; others require a plugin to support EditorConfig.\n\nThere is some overlap in functionality between EditorConfig and Flake8.\nCrudely EditorConfig applies standards to files as they are created/edited, while Flake8 checks all files (irrespective of origin) on demand.\n\nBoth tools need to be set up en ensure that they are mutually compatible.\nHowever, since the configurations for both Flake8 and EditorConfig rarely change after the initial setup of the project, this is not a problem in practice.\n\n## VS code\n\n- [VS Code Python documentation](https://code.visualstudio.com/docs/python/python-tutorial)\n- [ARC suggested config (Turing members only)](https://github.com/alan-turing-institute/ARC/blob/master/IDE/vscode/README.md)\n\n### Extensions\n\nFocusing on Python only, useful extensions include:\n\n- [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) \u2014 adds core functionality for linting/navigating/debugging/etc.", "start_char_idx": 0, "end_char_idx": 2028, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "d692095e-53cf-4583-969f-34d0abcb3eb9": {"__data__": {"id_": "d692095e-53cf-4583-969f-34d0abcb3eb9", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "998a3b4c-d069-4bed-a7aa-9874021d7572", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "336c1f48a45a3500620550fac6fba93eae68682ad015139fc374fc8508bbaf10", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "5d95abb7-0902-4779-8d44-fedef73fd58d", "node_type": "1", "metadata": {}, "hash": "047c0dcab1c3691b45ce1efce720e5954756383c6c89e72d77a4d92fdf4cb0e8", "class_name": "RelatedNodeInfo"}}, "text": "There is some overlap in functionality between EditorConfig and Flake8.\nCrudely EditorConfig applies standards to files as they are created/edited, while Flake8 checks all files (irrespective of origin) on demand.\n\nBoth tools need to be set up en ensure that they are mutually compatible.\nHowever, since the configurations for both Flake8 and EditorConfig rarely change after the initial setup of the project, this is not a problem in practice.\n\n## VS code\n\n- [VS Code Python documentation](https://code.visualstudio.com/docs/python/python-tutorial)\n- [ARC suggested config (Turing members only)](https://github.com/alan-turing-institute/ARC/blob/master/IDE/vscode/README.md)\n\n### Extensions\n\nFocusing on Python only, useful extensions include:\n\n- [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) \u2014 adds core functionality for linting/navigating/debugging/etc.\n  - [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)\n  - [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)\n- [Python Indent](https://marketplace.visualstudio.com/items?itemName=KevinRose.vsc-python-indent) \u2014 improves editor behaviour for matching indentation across lines\n- [autoDocstring](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring) \u2014 auto-generate template docstrings\n- [IntelliCode](https://marketplace.visualstudio.com/items?itemName=VisualStudioExptTeam.vscodeintellicode) \u2014 improved auto-completion/code suggestions\n- [Black formatter (pre-release)](https://marketplace.visualstudio.com/items?itemName=ms-python.black-formatter) \u2014 auto-format code with `black`\n- [isort (pre-release)](https://marketplace.visualstudio.com/items?itemName=ms-python.isort) \u2014 auto-format code with `isort`\n- [Python Type Hint](https://marketplace.visualstudio.com/items?itemName=njqdev.vscode-python-typehint) \u2014 helps with completing type-hints\n\n{{< hint info >}}\nThe `black` and `isort` pre-release packages are currently designed to work with the Python pre-release version.", "start_char_idx": 1138, "end_char_idx": 3195, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "5d95abb7-0902-4779-8d44-fedef73fd58d": {"__data__": {"id_": "5d95abb7-0902-4779-8d44-fedef73fd58d", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "d692095e-53cf-4583-969f-34d0abcb3eb9", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "1540f52d1d32dd2cfcc8a455e9f141739c2da129033355185306885762747133", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ef58638f-76af-4f77-a459-560c3d95561b", "node_type": "1", "metadata": {}, "hash": "37acd1a1346d9f66b51e5a7ec621f53283534ac3213159c0776573e4cc150717", "class_name": "RelatedNodeInfo"}}, "text": "This is likely to be consolidated/improved in the future.\n{{< /hint >}}\n\n### Settings\n\nVS Code Settings are stored in:\n\n- Global user settings: `~/Library/Application Support/Code/User/settings.json`.\n- Project-specific (Workspace) settings: `<PROJECT_ROOT>/.vscode/settings.json`.\n\nYou can also customise most settings via the `Code -> Preferences -> Settings` menu.\nFor Python specifically note the `Extensions -> Python` section.\n\nHere's an example config for Python (reliant on installing the extensions above):\n\n```json\n{\n    \"python.linting.flake8Enabled\": true,\n    \"python.linting.mypyEnabled\": true,\n    \"python.testing.pytestEnabled\": true,\n    \"python.linting.pylintEnabled\": true,\n    \"autoDocstring.docstringFormat\": \"numpy\",\n    \"[python]\": {\n        \"editor.rulers\": [88],\n        \"editor.defaultFormatter\": \"ms-python.black-formatter\",\n        \"editor.formatOnSave\": true,\n        \"editor.codeActionsOnSave\": {\n            \"source.organizeImports\": true\n        },\n    },\n}\n```\n\nIt does the following:\n\n- Activates linting with `flake8`, `mypy` and `pylint`.\n  - We suggest using `flake8` at a minimum, but you may not want all three activated as it gets quite noisy and there's some overlap between them.\n- Sets `pytest` to be the default test framework.\n- Sets `numpy` to be the default format for docstrings generated with autoDocstring.\n- Sets `black` to be the default formatter, and automatically runs `black` and `isort` when saving a file.\n- Adds a vertical ruler to the editor at 88 characters (default line length used by `black`) for Python files only.\n\nNote that:\n\n- It's possible to specify arguments for the linters/formatters in `settings.json`. We suggest using each tool's own configuration files (_e.g._ `.flake8`), as these will then be picked up by other tools and editors as well. (_e.g._ Should configure `flake8` and `isort` to be compatible with `black`).", "start_char_idx": 3196, "end_char_idx": 5091, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ef58638f-76af-4f77-a459-560c3d95561b": {"__data__": {"id_": "ef58638f-76af-4f77-a459-560c3d95561b", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "5d95abb7-0902-4779-8d44-fedef73fd58d", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "edbd5ae3b0c64459fad143dd546ecaa3dceb5a69ab71792852eb4940566de133", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "91aec09e-c299-451a-bee5-36cc906bf2ce", "node_type": "1", "metadata": {}, "hash": "f3325af63762636ad5ab00e696518ce6d76bb7b034f8f6ee3eb095d10934d5ba", "class_name": "RelatedNodeInfo"}}, "text": "- We suggest using `flake8` at a minimum, but you may not want all three activated as it gets quite noisy and there's some overlap between them.\n- Sets `pytest` to be the default test framework.\n- Sets `numpy` to be the default format for docstrings generated with autoDocstring.\n- Sets `black` to be the default formatter, and automatically runs `black` and `isort` when saving a file.\n- Adds a vertical ruler to the editor at 88 characters (default line length used by `black`) for Python files only.\n\nNote that:\n\n- It's possible to specify arguments for the linters/formatters in `settings.json`. We suggest using each tool's own configuration files (_e.g._ `.flake8`), as these will then be picked up by other tools and editors as well. (_e.g._ Should configure `flake8` and `isort` to be compatible with `black`).\n- If enabled tools are not installed in your Python environment VS Code should prompt you to install them.\n- You are likely to have some non-Python specific configuration as well, so add the settings above to your `settings.json` file rather than overwriting it completely.\n\n### Selecting the Python Interpreter\n\n[VSC Python Interpreters Docs](https://code.visualstudio.com/docs/python/environments#_working-with-python-interpreters)\n\nFor each workspace VS Code can be configured to use a specific Python interpreter (environment).\nThat interpreter is then used by default when doing anything with your code (running scripts, linting, tests _etc._)\n\nTo set the interpreter:\n\n- Open a `.py` file.\n- Click on the details of the currently used environment on the status bar (bottom right).\n- Select an interpreter from the list that appears (or write the path to it).\n\nOr from the command palette:\n\n- Cmd-Shift-P -> Search for \"Python: Select Interpreter\".", "start_char_idx": 4273, "end_char_idx": 6045, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "91aec09e-c299-451a-bee5-36cc906bf2ce": {"__data__": {"id_": "91aec09e-c299-451a-bee5-36cc906bf2ce", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ef58638f-76af-4f77-a459-560c3d95561b", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "3838e55cc6befef57a7b68252f9e816ef48ecc67d3d79842704875795c82df76", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "0f6df599-a7d7-4b74-99f7-cddd754c7717", "node_type": "1", "metadata": {}, "hash": "1f64c57f902cd38eeb30b16f098837da4d8baad1c07dfe8e8aa31293a3dd033e", "class_name": "RelatedNodeInfo"}}, "text": "- If enabled tools are not installed in your Python environment VS Code should prompt you to install them.\n- You are likely to have some non-Python specific configuration as well, so add the settings above to your `settings.json` file rather than overwriting it completely.\n\n### Selecting the Python Interpreter\n\n[VSC Python Interpreters Docs](https://code.visualstudio.com/docs/python/environments#_working-with-python-interpreters)\n\nFor each workspace VS Code can be configured to use a specific Python interpreter (environment).\nThat interpreter is then used by default when doing anything with your code (running scripts, linting, tests _etc._)\n\nTo set the interpreter:\n\n- Open a `.py` file.\n- Click on the details of the currently used environment on the status bar (bottom right).\n- Select an interpreter from the list that appears (or write the path to it).\n\nOr from the command palette:\n\n- Cmd-Shift-P -> Search for \"Python: Select Interpreter\".\n\n### Linting\n\n[VSC Linting Docs](https://code.visualstudio.com/docs/python/linting)\n\nIf linters are activated (see Settings) they will be run after saving a file, and any problems the enabled tools encounter will be highlighted in-line in the editor, and be listed in the \"Problems\" tab of the panel at the bottom (or select View -> Problems from the menu).\n\nHovering over a piece of code with a problem in the editor will give you more details about it, and may have a \"Quick Fix\" available, which will edit the code for you to fix it.\n\n### Formatting\n\n[VSC Formatting Docs](https://code.visualstudio.com/docs/python/formatting)\n\nVS code can use a few different file formatters (e.g. `black`, `yapf`, `autopep8` \u2014 we recommend `black`), which can be configured to run automatically when saving files (see settings above) or with the following shortcuts:\n\n- Option-Shift-F.\n- Save the file (if auto-formatting is configured).\n- Right-click in a Python editor -> Format Document.\n- Cmd-Shift-P (Opens Command Palette) -> Search for \"format\" -> \"Format Document\".", "start_char_idx": 5092, "end_char_idx": 7107, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0f6df599-a7d7-4b74-99f7-cddd754c7717": {"__data__": {"id_": "0f6df599-a7d7-4b74-99f7-cddd754c7717", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "91aec09e-c299-451a-bee5-36cc906bf2ce", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "131bea10008b4b3ae1952d7de88ba3e3ff74ab9dd8813c5173eb327c0f9d5892", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "ce949a35-c420-4642-8f65-2ad80b27fe15", "node_type": "1", "metadata": {}, "hash": "69fd34c3881bfe1d4e658eb0920522e569551cf6fc4624f8c80b1520737571d7", "class_name": "RelatedNodeInfo"}}, "text": "Hovering over a piece of code with a problem in the editor will give you more details about it, and may have a \"Quick Fix\" available, which will edit the code for you to fix it.\n\n### Formatting\n\n[VSC Formatting Docs](https://code.visualstudio.com/docs/python/formatting)\n\nVS code can use a few different file formatters (e.g. `black`, `yapf`, `autopep8` \u2014 we recommend `black`), which can be configured to run automatically when saving files (see settings above) or with the following shortcuts:\n\n- Option-Shift-F.\n- Save the file (if auto-formatting is configured).\n- Right-click in a Python editor -> Format Document.\n- Cmd-Shift-P (Opens Command Palette) -> Search for \"format\" -> \"Format Document\".\n\n### Running Tests\n\n[VSC Testing Docs](https://code.visualstudio.com/docs/python/testing)\n\nIf you have enabled `pytest` (or another framework, e.g. `unittest`), tests will be discovered and appear in the `Testing` menu (lab bottle on the left side menu).\n\nFrom here you can run all/individual tests.\nYou can also search for \"Tests\" in the command palette (Cmd-Shift-P) to find options for running tests.\nIf a test fails, clicking on it will show the error message. You can also debug tests (see debugging).\n\nIf you have a tests file open in an editor the latest pass/fail status of a test will be shown (or a play button if the test hasn't run before) \u2014 running tests can be triggered from there too.\n\n### Debugging\n\n[VSC Debugging Docs](https://code.visualstudio.com/docs/python/debugging)\n\n### Other Tips and Tricks\n\n- **Auto-import:** If you start typing a function name that hasn't been imported, VS Code will pop up a list of suggested functions and \"Auto-Import\" them (add an import statement to the top of the script) if you select one of them.\n- **Rename symbol:** Right-clicking on a (variable/function/class/etc.)", "start_char_idx": 6405, "end_char_idx": 8231, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ce949a35-c420-4642-8f65-2ad80b27fe15": {"__data__": {"id_": "ce949a35-c420-4642-8f65-2ad80b27fe15", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "0f6df599-a7d7-4b74-99f7-cddd754c7717", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "d4bd21d49fc5d9c18b0c5d82b93dc35149b2aee121f48fbd0a02ecc0f7bf11a1", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "0067c7fe-dbac-4676-b633-bbd243898fd9", "node_type": "1", "metadata": {}, "hash": "4baa3490b2299c79d5da6ab21cd71e628724cb26b7a72dc8adfece8f60b5c82b", "class_name": "RelatedNodeInfo"}}, "text": "You can also search for \"Tests\" in the command palette (Cmd-Shift-P) to find options for running tests.\nIf a test fails, clicking on it will show the error message. You can also debug tests (see debugging).\n\nIf you have a tests file open in an editor the latest pass/fail status of a test will be shown (or a play button if the test hasn't run before) \u2014 running tests can be triggered from there too.\n\n### Debugging\n\n[VSC Debugging Docs](https://code.visualstudio.com/docs/python/debugging)\n\n### Other Tips and Tricks\n\n- **Auto-import:** If you start typing a function name that hasn't been imported, VS Code will pop up a list of suggested functions and \"Auto-Import\" them (add an import statement to the top of the script) if you select one of them.\n- **Rename symbol:** Right-clicking on a (variable/function/class/etc.) name in a file, selecting \"Rename symbol\", and entering a new name will change the name of all instances of that across your codebase (but respecting the scope of the renamed entity).\n- **Jupyter:** The Python extension comes with a Jupyter extension so you can run and edit notebooks in VS Code. Personally, I prefer the native Jupyter notebook/lab interface to the VS Code one.\n- **AutoDocstring:** If the AutoDocstring extension is installed, typing `\"\"\"` then hitting enter will generate a template docstring for you containing placeholders for all arguments (including their types if type hints are used in the function definition).\n- **Go to definition:** Right-clicking on a (variable/function/class/etc.) name and selecting \"Go to definition\" will take you to where that thing is defined in the codebase.\n- **Git:** The editor highlights modifications to the code (by the line numbers), and you can also commit etc. from VS Code using the Source Control menu on the left.", "start_char_idx": 7408, "end_char_idx": 9211, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "0067c7fe-dbac-4676-b633-bbd243898fd9": {"__data__": {"id_": "0067c7fe-dbac-4676-b633-bbd243898fd9", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "d7f37bb57151052794a69aeb894cc8dd8fe54541", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "e7027ca907e40b82ffeb8883cce20fbeb31822f2727aa64c6a3341ffb5fc34af", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "ce949a35-c420-4642-8f65-2ad80b27fe15", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/configuring_editors.md", "file_name": "configuring_editors.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/configuring_editors.md"}, "hash": "c15da63a05a46ae3bec3674a59dac6db669542c27697cb3353ee98c1a8957302", "class_name": "RelatedNodeInfo"}}, "text": "- **Jupyter:** The Python extension comes with a Jupyter extension so you can run and edit notebooks in VS Code. Personally, I prefer the native Jupyter notebook/lab interface to the VS Code one.\n- **AutoDocstring:** If the AutoDocstring extension is installed, typing `\"\"\"` then hitting enter will generate a template docstring for you containing placeholders for all arguments (including their types if type hints are used in the function definition).\n- **Go to definition:** Right-clicking on a (variable/function/class/etc.) name and selecting \"Go to definition\" will take you to where that thing is defined in the codebase.\n- **Git:** The editor highlights modifications to the code (by the line numbers), and you can also commit etc. from VS Code using the Source Control menu on the left.\n- **Docker:** There are extensions ([1](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker), [2](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)) for running code in containers/attaching a VS code window to a container.", "start_char_idx": 8416, "end_char_idx": 9500, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "93687e9e-a133-4f4a-97eb-6a7c6561d8b8": {"__data__": {"id_": "93687e9e-a133-4f4a-97eb-6a7c6561d8b8", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a15c80d71605c1b52b365259257c75172230e2b3", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "a635d575e98d106ea317730373e98cd2f5d5b2b9c5abdcfab0f4347d50bf9c91", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "e71be8f0-fe30-480b-9e5c-86141c78d8a4", "node_type": "1", "metadata": {}, "hash": "2cd4c4775d1a7b024e19e74b487b1403781b1d38ec6ccfc40bfa3730c7f98bca", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Python Tools and Practices\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 3\n---\n\n# Python Tools and Practices\n\nPython is a widely used language within REG.\nBelow are some useful tools to help you configure your repo (with links to examples).\n\n{{< hint warning >}}\nSome of the topics discussed below are not exclusive to Python. As more content is added to the manual they should be separated out into their own pages.\n{{< /hint >}}\n\n## Autoformatters / Linters\n\nThere are many widely used autoformatter and linters for Python, which can be used as standalone tools or call from `.pre-commit`, continuous integration tools _etc._:\n\n- [isort](https://pypi.org/project/isort/) Sorts your import statements correctly.\n  - The order `isort` \"sorts\" imports in the following order:\n    1. standard library imports (_e.g._ `import os`).\n    1. related third party imports (_e.g._ `import pandas`).\n    1. local application/library specific imports (_e.g._ `from .my_python_file import MyClass`).\n  - You should put a blank line between each group of imports.\n  - the configuration of `isort` can be specified in a `pyproject.toml` file, where `isort` is using the `[tool.isort]` section.\n- [black](https://pypi.org/project/black) A highly opinionated code formatter, which enforces control of minutiae details of your code.\n  - The configuration of `black` can be specified in a `pyproject.toml` file, where `black` is using the ``[tool.black]`` section.\n- [flake8](https://pypi.org/project/flake8) A wrapper for three other tools:\n  - PyFlakes \u2014 checks syntax, without checking the style.\n  - pycodestyle \u2014 checks compliance with [PEP8](https://peps.python.org/pep-0008/)\n  - Ned Batchelder\u2019s McCabe script \u2014 checks the cyclomatic complexity of code.", "start_char_idx": 0, "end_char_idx": 1835, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "e71be8f0-fe30-480b-9e5c-86141c78d8a4": {"__data__": {"id_": "e71be8f0-fe30-480b-9e5c-86141c78d8a4", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a15c80d71605c1b52b365259257c75172230e2b3", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "a635d575e98d106ea317730373e98cd2f5d5b2b9c5abdcfab0f4347d50bf9c91", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "93687e9e-a133-4f4a-97eb-6a7c6561d8b8", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "a4bd2aefab476bad19df5d9a7904eea9195a6f5c3bdd5e8a651e0e4ac2f38ed7", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "8f323bf4-8b81-4f6f-a61f-ac1ca7407c49", "node_type": "1", "metadata": {}, "hash": "8e28091118c4ba557f26d17629a7bbabe55a02e8636cb33168e443f903f4daf6", "class_name": "RelatedNodeInfo"}}, "text": "- You should put a blank line between each group of imports.\n  - the configuration of `isort` can be specified in a `pyproject.toml` file, where `isort` is using the `[tool.isort]` section.\n- [black](https://pypi.org/project/black) A highly opinionated code formatter, which enforces control of minutiae details of your code.\n  - The configuration of `black` can be specified in a `pyproject.toml` file, where `black` is using the ``[tool.black]`` section.\n- [flake8](https://pypi.org/project/flake8) A wrapper for three other tools:\n  - PyFlakes \u2014 checks syntax, without checking the style.\n  - pycodestyle \u2014 checks compliance with [PEP8](https://peps.python.org/pep-0008/)\n  - Ned Batchelder\u2019s McCabe script \u2014 checks the cyclomatic complexity of code.\n  - Configuration can be specified in a `.flake8` configuration file in the root directory of your project.\n\nIt is possible to use all of these in combination.\nThe ARC group have a [template repo with suggested configuration files](https://github.com/alan-turing-institute/ARC-project-template) which you can copy/adapt as required.\n\n## Type Checkers\n\nType checking is optional in Python but is generally recommended.\nThere are several tools which can perform type checking:\n\n- [mypy](https://mypy.readthedocs.io) \u2014 This type-checker has the distinction of including [Guido van Rossum](https://python.org/~guido) in its core development team.\n- [pyright](https://github.com/Microsoft/pyright) \u2014 By Microsoft.\n- [pytype](https://github.com/google/pytype) \u2014 By Google.\n\n## Automation (Pre-Commit & Actions)\n\nA convenient way to incorporate many of these tools and checks into your workflow is to use `pre-commit`.\nBelow is an example.\nYou could adapt this to your needs, or create your own from the many [hooks described in the documentation](https://pre-commit.com/).", "start_char_idx": 1082, "end_char_idx": 2902, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "8f323bf4-8b81-4f6f-a61f-ac1ca7407c49": {"__data__": {"id_": "8f323bf4-8b81-4f6f-a61f-ac1ca7407c49", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a15c80d71605c1b52b365259257c75172230e2b3", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "a635d575e98d106ea317730373e98cd2f5d5b2b9c5abdcfab0f4347d50bf9c91", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "e71be8f0-fe30-480b-9e5c-86141c78d8a4", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "2b4da223b6497b54ec1a4f80728699bbb4dc687547a8eb5aaabef1b413565843", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "9357024f-1e70-4c6e-b046-cc395bfe494d", "node_type": "1", "metadata": {}, "hash": "e46f00e43aeb78549afe39f3b9cba8fbb0ce2b04f5d869289222466038dac773", "class_name": "RelatedNodeInfo"}}, "text": "## Type Checkers\n\nType checking is optional in Python but is generally recommended.\nThere are several tools which can perform type checking:\n\n- [mypy](https://mypy.readthedocs.io) \u2014 This type-checker has the distinction of including [Guido van Rossum](https://python.org/~guido) in its core development team.\n- [pyright](https://github.com/Microsoft/pyright) \u2014 By Microsoft.\n- [pytype](https://github.com/google/pytype) \u2014 By Google.\n\n## Automation (Pre-Commit & Actions)\n\nA convenient way to incorporate many of these tools and checks into your workflow is to use `pre-commit`.\nBelow is an example.\nYou could adapt this to your needs, or create your own from the many [hooks described in the documentation](https://pre-commit.com/).\n\n```yaml\n# See https://pre-commit.com for more information\n# See https://pre-commit.com/hooks.html for more hooks\nrepos:\n-   repo: https://github.com/pre-commit/pre-commit-hooks\n    rev: v4.2.0\n    hooks:\n    -   id: trailing-whitespace\n    -   id: end-of-file-fixer\n    -   id: check-yaml\n    -   id: check-xml\n    -   id: check-json\n    -   id: pretty-format-json\n        args: [--no-ensure-ascii, --no-sort-keys, --autofix]\n        files: \".+json\"\n    -   id: check-added-large-files\n-   repo: https://github.com/psf/black\n    rev: 22.3.0\n    hooks:\n    -   id: black\n#        files: \"*.py\"\n        exclude: \".+/ignore_this_legacy_file.py\"\n-   repo: https://github.com/pre-commit/mirrors-autopep8\n    rev: 'v1.6.0'  # Use the sha / tag you want to point at\n    hooks:\n    -   id: autopep8\n-   repo: https://github.com/pre-commit/mirrors-mypy\n    rev: 'v0.960'  # Use the sha / tag you want to point at\n    hooks:\n    -   id: mypy\n        exclude: \".+/ignore_this_legacy_file.py\"\n-   repo: https://github.com/pre-commit/mirrors-isort\n    rev: v5.10.1\n    hooks:\n    -   id: isort\n```\n\nThis example checks:\n\n- There is no trailing whitespace at the end of each line.", "start_char_idx": 2170, "end_char_idx": 4070, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "9357024f-1e70-4c6e-b046-cc395bfe494d": {"__data__": {"id_": "9357024f-1e70-4c6e-b046-cc395bfe494d", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a15c80d71605c1b52b365259257c75172230e2b3", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "a635d575e98d106ea317730373e98cd2f5d5b2b9c5abdcfab0f4347d50bf9c91", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "8f323bf4-8b81-4f6f-a61f-ac1ca7407c49", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/python.md", "file_name": "python.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/python.md"}, "hash": "535c71e66256d2da424469687380ff0783e6fb117dbce23f58893569f77f3a2e", "class_name": "RelatedNodeInfo"}}, "text": "- There is exactly one blank line at the end of the file.\n- Any `yaml` files in the repo are formatted correctly.\n- Any `xml` files in the repo are formatted correctly.\n- Any `json` files in the repo are formatted correctly and \"pretty printed\".\n- If any large files have been added to the repo without using [git-lfs](https://git-lfs.github.com/)\n- Any python code (except the file named \"ignore_this_legacy_file.py\"), is formatted according to \"[black's](https://black.readthedocs.io/en/stable/)\" _uncompromising_ standard.\n- Any python code (except the file named \"ignore_this_legacy_file.py\"), is formatted according to \"[pep8's](https://pep8.org/)\" standard.\n- Any python code is tested for type safeness (That is declaring types is optional, but if types are declared they must be correct).\n- The import statements in any python code are correctly ordered.\n\nIf any of these tests fail or need to alter any files, the commit will fail.\nThis prevents you from accidentally forgetting to check your changes before you commit them.\n\nIf you wish to run the checks in `pre-commit` without attempting to commit your changes:\n\n```bash\npre-commit run --all-files\n```\n\nThere are many other possible checks available with `pre-commit`, which you can incorporate according to the needs of your project.\nThe [`pre-commit` documentation](https://pre-commit.com/) provides much more detail.\n\n## Testing tools\n\nSome suggested testing tools:\n\n- A test framework [pytest](https://docs.pytest.org/) or the less fashionable [unittest](https://docs.python.org/3/library/unittest.html).\n- Coverage reporting [pytest-cov](https://pypi.org/project/pytest-cov/) (a plugin for `pytest`) or [coverage](https://coverage.readthedocs.io/).\n- A tool for testing code examples in your documentation [doctest](https://docs.python.org/3/library/doctest.html).\n\nThis list of tools does not include a discussion on developing a _testing strategy_.\nDeveloping a testing strategy is a separate discussion, though it is not possible to make optimal use of these tools without a strategy.", "start_char_idx": 4071, "end_char_idx": 6125, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "05060b83-66bd-46d0-b5d4-c556fc94bf29": {"__data__": {"id_": "05060b83-66bd-46d0-b5d4-c556fc94bf29", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/software_dev_best_practice.md", "file_name": "software_dev_best_practice.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/software_dev_best_practice.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "83a937e7909e283792af66260c897d102991d7b9", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/software_dev_best_practice.md", "file_name": "software_dev_best_practice.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/software_dev_best_practice.md"}, "hash": "4509ca6c53d3a37eb5fab94e907e322f73507a4e10ae652aeda84b45f2b1e4ec", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "76fe5b6a-11e4-4441-b16d-26cc1fa953b5", "node_type": "1", "metadata": {}, "hash": "434540d1746c15c63b62b391270fe6da65364edb19aebd544641344408e16cb2", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Software Development\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 5\n---\n\n# Software Development Best Practices\n\n_Status:_ this document is a draft proposal.\n\n## RSE projects\n\n- There should be a GitHub issue for each thing we do.\n  It\u2019s ok for issues to evolve and my preference would be that the top description box always has the most up to date, clearest description of the issue and its definition of done.\n  Feel free to make liberal use of the comments for communication and tracking investigative / exploratory work.\n  The main description box should contain a clear definition of done / acceptance criteria sufficient for the following to be done in principle by different people from the team who are involved in the project.\n  - Implement the functionality\n  - Write tests to validate the implementation meets the definition of done\n  - Review the implementation for correctness\n\n- All code should have automated tests, with all tests runnable by a single command by someone who has checked out a fresh copy of the repo.\n\n- All dependencies, installation, compilation etc should be scripted such that these can be triggered by a single command by someone who has checked out a fresh copy of the repo.\n  Dependencies required only for testing can require a second command or few.\n\n- Each new issue should be developed on a short-lived feature branch split from \"main\".\n  These branches ideally only live around 0.5-2 days before being merged back into main.\n  Merge process should be: merge main into feature branch and resolve any conflicts. Push latest feature branch to GitHub.\n  Open PR to merge into main after review + continuous integration (CI) tests.\n\n- Code should be committed regularly in small chunks.\n  You should generally be committing multiple times per day.\n  Commits on feature branches can have broken functionality or tests, but all merges into main should only occur at commits where the code is correct and has passing tests.\n\n- All code commits should be accompanied by tests verifying it behaves as expected.\n  At a minimum code submitted as a PR should have tests that show it meets the definition of done defined in the related issue(s).", "start_char_idx": 0, "end_char_idx": 2266, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "76fe5b6a-11e4-4441-b16d-26cc1fa953b5": {"__data__": {"id_": "76fe5b6a-11e4-4441-b16d-26cc1fa953b5", "embedding": null, "metadata": {"file_path": "content/docs/technical_practices/software_dev_best_practice.md", "file_name": "software_dev_best_practice.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/software_dev_best_practice.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "83a937e7909e283792af66260c897d102991d7b9", "node_type": "4", "metadata": {"file_path": "content/docs/technical_practices/software_dev_best_practice.md", "file_name": "software_dev_best_practice.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/software_dev_best_practice.md"}, "hash": "4509ca6c53d3a37eb5fab94e907e322f73507a4e10ae652aeda84b45f2b1e4ec", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "05060b83-66bd-46d0-b5d4-c556fc94bf29", "node_type": "1", "metadata": {"file_path": "content/docs/technical_practices/software_dev_best_practice.md", "file_name": "software_dev_best_practice.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/technical_practices/software_dev_best_practice.md"}, "hash": "4b8c2b6394ca3d96f203fbb7e0848300c459b5d6937914cdd134718e706966ab", "class_name": "RelatedNodeInfo"}}, "text": "Dependencies required only for testing can require a second command or few.\n\n- Each new issue should be developed on a short-lived feature branch split from \"main\".\n  These branches ideally only live around 0.5-2 days before being merged back into main.\n  Merge process should be: merge main into feature branch and resolve any conflicts. Push latest feature branch to GitHub.\n  Open PR to merge into main after review + continuous integration (CI) tests.\n\n- Code should be committed regularly in small chunks.\n  You should generally be committing multiple times per day.\n  Commits on feature branches can have broken functionality or tests, but all merges into main should only occur at commits where the code is correct and has passing tests.\n\n- All code commits should be accompanied by tests verifying it behaves as expected.\n  At a minimum code submitted as a PR should have tests that show it meets the definition of done defined in the related issue(s).\n  However, code should have additional unit / regression / integration tests where appropriate (it's almost always appropriate).\n  Writing code then tests is a significantly lower quality assurance measure than writing tests first / in iteration with the code, so you should do the latter.\n\n- All code should have had two eyeballs on it before it is merged into main, either through pair programming or code review within the PR.\n  Please note on the PR who two eyes were.\n  The second pair of eyes can be anyone on the project (not just a coder), anyone in our team or someone from a related team (e.g. the UCL research programming).\n  Whether as pair programming or a code review, the final PR code should be validated against the definition of done detailed in the related issue(s) by the two pairs of eyes.\n\n- The GitHub repository should be set up to run all tests on all pushes to all branches and on all PRs.\n  Currently this can be done for public repos via GitHub Actions.\n  If CI is set up this way, commits to main should be blocked unless all tests pass.\n\n## Data Science projects\n\nTBD", "start_char_idx": 1306, "end_char_idx": 3364, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "f5ee3b52-aa3e-4bd0-8f27-79c40c81d3c7": {"__data__": {"id_": "f5ee3b52-aa3e-4bd0-8f27-79c40c81d3c7", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/_index.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "a40d10bf89b5a47ee2ebb0eaeeb3ad970d275a3c", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/_index.md"}, "hash": "41714ec498bee8c266a8f895d8d0ff2fe076f9a96dd40d6542e549f4598bc59d", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Working at The Turing\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 5\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Working at The Turing", "start_char_idx": 0, "end_char_idx": 424, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "23e852e5-02c3-408e-999f-c6fdea194a32": {"__data__": {"id_": "23e852e5-02c3-408e-999f-c6fdea194a32", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/booking_a_meeting_room.md", "file_name": "booking_a_meeting_room.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/booking_a_meeting_room.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "386340d84f9bce8c984e5f9d344c16803f180779", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/booking_a_meeting_room.md", "file_name": "booking_a_meeting_room.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/booking_a_meeting_room.md"}, "hash": "7811f6dae0d3210747f778d5e656212e84d13c79fc78d9d6b67ccfdd494ea0a6", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Booking a Meeting Room\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Booking a Meeting Room\n\nThere are instructions on how to book a room on [Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2118&SearchId=175781&utm_source=interact&utm_medium=quick_search&utm_term=meeting+rooms).\n\nThe simplest way of finding a room is to use the `Scheduling Assistant` on a meeting invite.\n\n![](https://i.imgur.com/jeeD7Gv.png)\n\nThen scroll down to `Add Location`. This will give you a list of Available Rooms for your specific time. It will update when you move the time window.\n\n![](https://i.imgur.com/WOhkk8G.png)", "start_char_idx": 0, "end_char_idx": 968, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "2219935e-cd84-4325-9c68-e9f3c2e61faa": {"__data__": {"id_": "2219935e-cd84-4325-9c68-e9f3c2e61faa", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8b9cb577cde41b9eb68932ea5dfbe8e1c255ba3a", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "hash": "68330eb2e10e4bbe7a032c73b9949ecc876a181e7fa48b0b305a2239f211e9df", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "63f43023-e476-4c1a-ae99-c7774894fb2f", "node_type": "1", "metadata": {}, "hash": "bd6c7d892d78c5f3a697e41ee5f7848af6f4761d7360b64944e8701c3b7372a6", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Equality, Diversity, and Inclusion\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 3\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Equality, Diversity, and Inclusion\n\n## Overview\n\nThe Turing has a strong commitment towards EDI, which is formally set out in the Institute's EDI [Strategy](https://www.turing.ac.uk/sites/default/files/2023-03/edi-strategy-report_v1.2_updated_16.03.2023_0.pdf) and [Action Plan](https://www.turing.ac.uk/sites/default/files/2021-09/edi-action-plan_final.pdf).\nFor current Turing staff, you can also access an [EDI section on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Section/Default.aspx?section=3684).\n\n## How to get involved\n\nIf you are keen to be involved in the EDI work around REG and the Turing, do feel free to join the `#edi-equality-diversity-inclusion` channel on Slack, and/or contact:\n\n- in the first instance, [the people involved in the REG EDI service area.](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry)\n\n   Besides the people directly allocated to this service area, we also have a group of volunteers who are involved on a 'best-effort' basis.\n   You are welcome to join in any capacity!\n- [the Turing's dedicated EDI team](https://www.turing.ac.uk/about-us/equality-diversity-and-inclusion)\n\nBelow we've described a handful of specific initiatives at the Turing which REG have been involved with.\nThis list is not exhaustive (nor should it be: there is always more to be done!).", "start_char_idx": 0, "end_char_idx": 1767, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "63f43023-e476-4c1a-ae99-c7774894fb2f": {"__data__": {"id_": "63f43023-e476-4c1a-ae99-c7774894fb2f", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8b9cb577cde41b9eb68932ea5dfbe8e1c255ba3a", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "hash": "68330eb2e10e4bbe7a032c73b9949ecc876a181e7fa48b0b305a2239f211e9df", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "2219935e-cd84-4325-9c68-e9f3c2e61faa", "node_type": "1", "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "hash": "5c709b26017ff7c9c4ac14a079467257095417b1f53c7ad2a35c9c4cf75c9152", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "b8c9a66f-86ef-4958-b219-f27093a055b8", "node_type": "1", "metadata": {}, "hash": "87bce34e7b203f6d85002ec2d46db5af3c56e2fb17780f9fccca8236e4644f71", "class_name": "RelatedNodeInfo"}}, "text": "## How to get involved\n\nIf you are keen to be involved in the EDI work around REG and the Turing, do feel free to join the `#edi-equality-diversity-inclusion` channel on Slack, and/or contact:\n\n- in the first instance, [the people involved in the REG EDI service area.](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry)\n\n   Besides the people directly allocated to this service area, we also have a group of volunteers who are involved on a 'best-effort' basis.\n   You are welcome to join in any capacity!\n- [the Turing's dedicated EDI team](https://www.turing.ac.uk/about-us/equality-diversity-and-inclusion)\n\nBelow we've described a handful of specific initiatives at the Turing which REG have been involved with.\nThis list is not exhaustive (nor should it be: there is always more to be done!).\n\n### HDR UK Black Internship\n\nSince 2022 REG has been involved in hosting interns from the [Black Internship programme by Health Data Research UK](https://www.hdruk.ac.uk/study-and-train/train/health-data-science-black-internship-programme/).\nInterns have typically been involved with one health-related project from REG, plus many other activities around the Turing.\nYou can see [a researcher spotlight on Olajumoke](https://www.turing.ac.uk/people/spotlights/olajumoke-olatunji), one of our interns from 2023.\n\n### EDI Annual Report\n\nEach year the Turing publishes an annual report (see [here](https://www.turing.ac.uk/sites/default/files/2023-02/edi_annual_report_october_2021-2022_public_version_002_1.pdf) for the most recent one, covering the period between October 2021 and September 2022), as well as a gender pay gap (see [here](https://www.turing.ac.uk/sites/default/files/2023-03/2023.03.30_gender_pay_gap_report.pdf)).\nREG typically assists the EDI team with analysis of the underlying data and recommendations on how to treat sensitive data.", "start_char_idx": 933, "end_char_idx": 2822, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "b8c9a66f-86ef-4958-b219-f27093a055b8": {"__data__": {"id_": "b8c9a66f-86ef-4958-b219-f27093a055b8", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "8b9cb577cde41b9eb68932ea5dfbe8e1c255ba3a", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "hash": "68330eb2e10e4bbe7a032c73b9949ecc876a181e7fa48b0b305a2239f211e9df", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "63f43023-e476-4c1a-ae99-c7774894fb2f", "node_type": "1", "metadata": {"file_path": "content/docs/working_at_the_turing/edi.md", "file_name": "edi.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/edi.md"}, "hash": "2afaf1790bee899d69054ce17dd09f7a2e0368fa70dbf72d430a997680be0a6a", "class_name": "RelatedNodeInfo"}}, "text": "You can see [a researcher spotlight on Olajumoke](https://www.turing.ac.uk/people/spotlights/olajumoke-olatunji), one of our interns from 2023.\n\n### EDI Annual Report\n\nEach year the Turing publishes an annual report (see [here](https://www.turing.ac.uk/sites/default/files/2023-02/edi_annual_report_october_2021-2022_public_version_002_1.pdf) for the most recent one, covering the period between October 2021 and September 2022), as well as a gender pay gap (see [here](https://www.turing.ac.uk/sites/default/files/2023-03/2023.03.30_gender_pay_gap_report.pdf)).\nREG typically assists the EDI team with analysis of the underlying data and recommendations on how to treat sensitive data.\n\n### Network Groups\n\nThe Turing has set up four EDI network groups centred on specific topics, with the aim of providing feedback to the Institute's EDI team and senior management:\n\n- [Disability and Wellbeing](https://mathison.turing.ac.uk/page/2088)\n- [Gender Equality](https://mathison.turing.ac.uk/page/2089)\n- [LGBTQ+ Equality](https://mathison.turing.ac.uk/page/2090)\n- [Race Equality](https://mathison.turing.ac.uk/page/2091)\n\nREG members are strongly encouraged to participate in these groups.\nAdditionally, group chairpersons (who can be from REG) are awarded an honorarium.\n\n### BCSWomen Lovelace Colloquium\n\nSince 2023, the Turing has been a sponsor for the [BCSWomen Lovelace Colloquium](https://bcswomenlovelace.bcs.org/), a one-day conference for women and non-binary students in computing fields.\nIn 2023 REG sat on the careers panel and also (together with other teams from the Turing) had a stall at the conference.", "start_char_idx": 2136, "end_char_idx": 3755, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "3a824442-4ed5-4847-96d3-dc2ca1324cfa": {"__data__": {"id_": "3a824442-4ed5-4847-96d3-dc2ca1324cfa", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/equipment.md", "file_name": "equipment.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/equipment.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c4771fb05fdcbcee999cbfb17856c814cbfa41f4", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/equipment.md", "file_name": "equipment.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/equipment.md"}, "hash": "6f81ddc9ff7e5e2ba00567bebd25149eebbf554be07f9b1cd65c3b36ceaf8f1d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "47dc984f-45b2-460d-bf79-5ee6e0a84bfd", "node_type": "1", "metadata": {}, "hash": "0ce15c05745742bbc693729bf60e9fdba5828108b5546553b85ea815080260d9", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Equipment\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 2\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Equipment\n\n## Getting a New Computer\n\nThis is a list of the steps taken by a REG member when they got a new Turing laptop from IT to replace their old laptop. Please note that you don't have to follow these steps, it is just advice given by someone who went through the process.\n\n1. Sign in to Apple Cloud on new machine. Activate all accounts (Office365, personal email, ...)\n\n1. `brew cask install emacs`, and anything else you need.\n\n1. Store config files on Apple Cloud, and symbolic link them to their required place, eg:\n   - `~/.emacs.el`\n   - `~/.bash_profile`\n\n1. Office (including OneDrive): install with the new \"self service\" app that IT now bundle with laptops (which apparently installs things via homebrew!)\n\n1. OneDrive setup. You might have a personal shared space on OneDrive, but also multiple shared spaces for projects, set up as \"Groups\" in Office 365. The standard location that Office chooses for your local OneDrive image is long and full of spaces. Put all shared spaces in ~/OneDrive-Actuals and then symbolic link to ~/OneDrive (for my personal space) and ~/Share/X (for project X).\n\n1. SSH tokens for GitHub. You can make new ones or think about moving the old ones, but it turned out to be straightforward to make new ones.\n\n1. Go through every repo with an authoritative remote, make sure you've pushed all local commits, and delete. (I keep all repos in `~/Projects/`, with remotes usually on GitHub.)\n\n1. Double-check other directories within `~/`\n\n1.", "start_char_idx": 0, "end_char_idx": 1875, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "47dc984f-45b2-460d-bf79-5ee6e0a84bfd": {"__data__": {"id_": "47dc984f-45b2-460d-bf79-5ee6e0a84bfd", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/equipment.md", "file_name": "equipment.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/equipment.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "c4771fb05fdcbcee999cbfb17856c814cbfa41f4", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/equipment.md", "file_name": "equipment.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/equipment.md"}, "hash": "6f81ddc9ff7e5e2ba00567bebd25149eebbf554be07f9b1cd65c3b36ceaf8f1d", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "3a824442-4ed5-4847-96d3-dc2ca1324cfa", "node_type": "1", "metadata": {"file_path": "content/docs/working_at_the_turing/equipment.md", "file_name": "equipment.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/equipment.md"}, "hash": "063d5e37ca509ebb63b8d1dba46ca02cb3cc4edfccdedf0ff7395406c11589e2", "class_name": "RelatedNodeInfo"}}, "text": "1. OneDrive setup. You might have a personal shared space on OneDrive, but also multiple shared spaces for projects, set up as \"Groups\" in Office 365. The standard location that Office chooses for your local OneDrive image is long and full of spaces. Put all shared spaces in ~/OneDrive-Actuals and then symbolic link to ~/OneDrive (for my personal space) and ~/Share/X (for project X).\n\n1. SSH tokens for GitHub. You can make new ones or think about moving the old ones, but it turned out to be straightforward to make new ones.\n\n1. Go through every repo with an authoritative remote, make sure you've pushed all local commits, and delete. (I keep all repos in `~/Projects/`, with remotes usually on GitHub.)\n\n1. Double-check other directories within `~/`\n\n1. Install Emacs additions as I learn that I need them ...\n\n## Additional Equipment\n\nThere is a budget to purchase peripherals (monitor, mouse, keyboard, _etc._) as well as other equipment such as an office chair and desk. You can purchase the office chair and desk yourself and claim back up to \u00a3200 in total via Certify. The official process is to assess your home-working set up via a [workstation self-assessment form](https://mathison.turing.ac.uk/page/2810). However, you can try contacting the [facilities team](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) directly. For peripherals, it's better to contact [IT](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact). See the [Turing's Homeworking Policy](https://mathison.turing.ac.uk/page/2218) for more information.\n\nIf there are health and safety reasons why you would want equipment that differs from the standard Apple keyboard/mouse then contact [HR](https://github.com/alan-turing-institute/research-engineering-group/wiki/The-REGistry#points-of-contact) directly, there is a separate health and safety budget for such things.", "start_char_idx": 1115, "end_char_idx": 3074, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "ae06d6a1-33fc-4b36-ae30-afcb8ed34d06": {"__data__": {"id_": "ae06d6a1-33fc-4b36-ae30-afcb8ed34d06", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/expenses.md", "file_name": "expenses.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/expenses.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "dbdb7680a1e21622af2403e7a3739e148903b38a", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/expenses.md", "file_name": "expenses.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/expenses.md"}, "hash": "d4974a7a66f54f85d5442d9db88b540b01c2a507cebf1b3954c7707be71d7176", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Expenses\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 1\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Expenses\n\nOut-of-pocket expenses should be reclaimed using Certify. You should submit an expense claim on Certify for expenses incurred within a given month as soon as possible, and no later than 60 days after the expense is incurred (except for visa costs, which can be claimed up to a year after starting employment).\n\nIn the first instance, look at the information provided by the Finance team about how to use Certify on Mathison:\n\nhttps://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2169\n\n(This also includes links to Certify's own user guides and videos)\n\n## Related content\n\n- [REG-specific information](https://github.com/alan-turing-institute/research-engineering-group/wiki/Reclaiming-out-of-pocket-expenses)\n- [Certify Guidance on Mathison](https://mathison.turing.ac.uk/Interact/Pages/Content/Document.aspx?id=2169)\n- [REG-specific Finance codes](https://github.com/alan-turing-institute/research-engineering-group/wiki/REG-specific-finance-codes)\n- [REG-budget-and-project-codes](https://github.com/alan-turing-institute/research-engineering-group/wiki/REG-budget-and-project-codes)\n- [Cost recovery model](https://github.com/alan-turing-institute/Hut23/wiki/REG-cost-recovery) (REG access only)", "start_char_idx": 0, "end_char_idx": 1616, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c348e27e-07a9-4eb0-96bf-ba067c1b993f": {"__data__": {"id_": "c348e27e-07a9-4eb0-96bf-ba067c1b993f", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/wellbeing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "84273d25a134f3eb42173957fb52d64eb533bc2d", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/wellbeing.md"}, "hash": "fd49d351b350c20d59e414034cac6a9f1be01955e0d62267318bcea4b93cdd25", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "542517e7-3531-412e-8101-736be1fcdfe0", "node_type": "1", "metadata": {}, "hash": "c5df4d03107092d52580f9f027662e0d089369c272b59961ed623733227c614f", "class_name": "RelatedNodeInfo"}}, "text": "---\n# Page title as it appears in the navigation menu\ntitle: \"Wellbeing\"\n# Adjust weight to reorder menu items (lower numbers appear first)\nweight: 4\n# Uncomment to hide nested pages in a collapsed menu\n# bookCollapseSection = true\n# Uncomment to hide this page from the navigation menu\n# bookHidden = false\n# Uncomment to exclude this page from the search\n# bookSearchExclude = true\n---\n\n# Wellbeing\n\nThe Turing provides a number of support mechanisms to protect employees' wellbeing.\n\nThe most up-to-date information can be found [on Mathison](https://mathison.turing.ac.uk/page/2228). A summary follows here:\n\n## Report + Support\n\n[Report + Support](https://reportandsupport.turing.ac.uk/) is an online platform where you can report cases of bullying, harassment, discrimination, assault, hate crime, sexual misconduct, or a breach of the Turing's values (Trust, Inclusivity, Respect, Leadership, Transparency, Integrity).\nReports can be made anonymously if you so choose.\n\n## Care first\n\nAll employees at the Turing can access the [Care first employee assistance programme](https://mathison.turing.ac.uk/page/2272).\nThis includes a 24-hour phone counselling service, online counselling, and advice services.\nContact details and links are available on the Mathison page.\n\nThe employee assistance programme also provides access to the ['My Possible Self' app](https://www.mypossibleself.com/) for monitoring and improving mental health.\n\n## Private medical / dental insurance\n\nThe Turing provides a [private medical insurance plan from Bupa](https://mathison.turing.ac.uk/page/3017).\nYou have to opt in: if you want to sign up, contact HR with the information requested on the above Mathison page.\nYou can choose to have either the medical insurance with or without the dental plan.\n\nThe cost of the medical insurance itself is covered by the Turing, but you have to pay for the first \u00a3100 of treatment (this is called the 'excess'), and because this is a [taxable benefit](https://www.gov.uk/tax-company-benefits) you also have to pay tax on the cost of the medical insurance.", "start_char_idx": 0, "end_char_idx": 2079, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "542517e7-3531-412e-8101-736be1fcdfe0": {"__data__": {"id_": "542517e7-3531-412e-8101-736be1fcdfe0", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": 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"fcc8c8c4f6761cd5aec3ccd9780a36e69e22b8a9f9001d001da9c1e52463d98d", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "c17d5b9f-b642-4903-b56b-8ec46fa02441", "node_type": "1", "metadata": {}, "hash": "2c5ff249e4d618579b0ec4825f6f69a23b293c1cf6b5477523c65cd935d92828", "class_name": "RelatedNodeInfo"}}, "text": "Contact details and links are available on the Mathison page.\n\nThe employee assistance programme also provides access to the ['My Possible Self' app](https://www.mypossibleself.com/) for monitoring and improving mental health.\n\n## Private medical / dental insurance\n\nThe Turing provides a [private medical insurance plan from Bupa](https://mathison.turing.ac.uk/page/3017).\nYou have to opt in: if you want to sign up, contact HR with the information requested on the above Mathison page.\nYou can choose to have either the medical insurance with or without the dental plan.\n\nThe cost of the medical insurance itself is covered by the Turing, but you have to pay for the first \u00a3100 of treatment (this is called the 'excess'), and because this is a [taxable benefit](https://www.gov.uk/tax-company-benefits) you also have to pay tax on the cost of the medical insurance.\nThe dental plan add-on has an annual flat rate charge.\n\nIt is worth noting that the medical insurance includes cover for pre-existing conditions, as well as mental health, which means that you can use it to access therapy with counsellors or psychologists.\nYou can use the [Bupa finder](https://www.finder.bupa.co.uk/) to search for recognised medical professionals.\n\n## Mental health first aiders\n\nThere are a number of mental health first aiders in the Turing whom you can speak to and who can help you obtain support.\nA full list can be obtained [on Mathison](https://mathison.turing.ac.uk/page/2119).\n(Unfortunately, this does not yet include anyone in REG.)\n\n## External supervision\n\nAll Turing staff have access to [individual, once-per-month sessions with an external supervisor, Debbie Dixon](https://mathison.turing.ac.uk/page/3052).\nExternal supervision is meant to provide participants with techniques and methods to navigate difficult situations, particularly at work.\nHowever, it is not a form of therapy and should not be seen as a replacement for that.\n\n## Free Headspace membership\n\nHeadspace is an app for mindfulness and meditation.", "start_char_idx": 1212, "end_char_idx": 3230, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "c17d5b9f-b642-4903-b56b-8ec46fa02441": {"__data__": {"id_": "c17d5b9f-b642-4903-b56b-8ec46fa02441", "embedding": null, "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/wellbeing.md"}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "84273d25a134f3eb42173957fb52d64eb533bc2d", "node_type": "4", "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/wellbeing.md"}, "hash": "fd49d351b350c20d59e414034cac6a9f1be01955e0d62267318bcea4b93cdd25", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "542517e7-3531-412e-8101-736be1fcdfe0", "node_type": "1", "metadata": {"file_path": "content/docs/working_at_the_turing/wellbeing.md", "file_name": "wellbeing.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/working_at_the_turing/wellbeing.md"}, "hash": "eb8cb83c82b57516f2c83234f227d13e4d241c4c5bf9c95e7ecdd1540e6219ef", "class_name": "RelatedNodeInfo"}}, "text": "You can use the [Bupa finder](https://www.finder.bupa.co.uk/) to search for recognised medical professionals.\n\n## Mental health first aiders\n\nThere are a number of mental health first aiders in the Turing whom you can speak to and who can help you obtain support.\nA full list can be obtained [on Mathison](https://mathison.turing.ac.uk/page/2119).\n(Unfortunately, this does not yet include anyone in REG.)\n\n## External supervision\n\nAll Turing staff have access to [individual, once-per-month sessions with an external supervisor, Debbie Dixon](https://mathison.turing.ac.uk/page/3052).\nExternal supervision is meant to provide participants with techniques and methods to navigate difficult situations, particularly at work.\nHowever, it is not a form of therapy and should not be seen as a replacement for that.\n\n## Free Headspace membership\n\nHeadspace is an app for mindfulness and meditation.\nThe Turing is a Headspace partner, meaning that all employees can access [free membership to Headspace](https://mathison.turing.ac.uk/page/2249).\nYou can also add 5 other people to your membership for free.\nInstructions on how to access the Turing subscription can be found on the Mathison link above.", "start_char_idx": 2337, "end_char_idx": 3532, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"93c225056acbb5484fe7994c647f755f93adf958": {"node_ids": ["75cf5ad8-bf0e-4b67-b3dd-ed3272881b09"], "metadata": {"file_path": "content/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/_index.md"}}, "cf380f2bc01891cffe4e2b991e3427cc023b8c89": {"node_ids": ["d0a13454-b117-45a1-a7f3-c088eaae7dce"], "metadata": {"file_path": "content/docs/communications/_index.md", "file_name": "_index.md", "url": "https://github.com/alan-turing-institute/REG-handbook/blob/main/content/docs/communications/_index.md"}}, "fd1b5253d191ca0839c393826ddf12f23b54dd1d": {"node_ids": ["3c7bf40c-c5e4-40db-80b2-dbb4e0f11289"], "metadata": 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new file mode 100644
index 00000000..e7f146e4
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\"ae06d6a1-33fc-4b36-ae30-afcb8ed34d06\", \"c348e27e-07a9-4eb0-96bf-ba067c1b993f\": \"c348e27e-07a9-4eb0-96bf-ba067c1b993f\", \"542517e7-3531-412e-8101-736be1fcdfe0\": \"542517e7-3531-412e-8101-736be1fcdfe0\", \"c17d5b9f-b642-4903-b56b-8ec46fa02441\": \"c17d5b9f-b642-4903-b56b-8ec46fa02441\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}

From 34255bb8a1ae80fffe6e8d4a8a9f05e4966dec3b Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Wed, 12 Jun 2024 23:18:38 +0100
Subject: [PATCH 06/11] fix properly the mapping to azuregpt

---
 reginald/models/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/reginald/models/__init__.py b/reginald/models/__init__.py
index 5e4684d7..1ae7abf6 100644
--- a/reginald/models/__init__.py
+++ b/reginald/models/__init__.py
@@ -48,7 +48,7 @@ def get_model(model_name: str):
                     LlamaIndexGPTAzure,
                 )
 
-                return LlamaIndexGPTOpenAI
+                return LlamaIndexGPTAzure
             case "llama-index-gpt-openai":
                 from reginald.models.llama_index.llama_index_openai import (
                     LlamaIndexGPTOpenAI,

From 3ca1c2ba283522c739cca68d5b3e5f6fba84c4e1 Mon Sep 17 00:00:00 2001
From: KatrionaGoldmann <kgoldmann@turing.ac.uk>
Date: Thu, 13 Jun 2024 10:59:11 +0100
Subject: [PATCH 07/11] chore: fix

---
 poetry.lock | 1 +
 1 file changed, 1 insertion(+)

diff --git a/poetry.lock b/poetry.lock
index 25985655..b68a58e4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3517,6 +3517,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},

From c32ca65d2ca1ea8906be85ed446ffad2e24de28c Mon Sep 17 00:00:00 2001
From: Dr Griffith Rees <griff.rees@gmail.com>
Date: Thu, 13 Jun 2024 11:19:13 +0100
Subject: [PATCH 08/11] chore: update `pyproject.toml` `authors`

---
 pyproject.toml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 11e98a96..ff73adc0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,9 @@ authors = ["Evelina Gabasova <evelina@evelinag.com>",
            "David Beavan <dbeavan@turing.ac.uk>",
            "Levan Bokeria <lbokeria@turing.ac.uk>",
            "Martin O'Reilly <moreilly@turing.ac.uk>",
-           "Oliver Strickson <ostrickson@turing.ac.uk>"]
+           "Oliver Strickson <ostrickson@turing.ac.uk>",
+           "Katriona Goldmann <kgoldmann@turing.ac.uk>",
+           "Griffith Rees <grees@turing.ac.uk>"]
 readme = "README.md"
 
 [tool.poetry.dependencies]

From 5118d1bc01fadd830e9041ad745a863003ea3d89 Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Thu, 13 Jun 2024 12:08:49 +0100
Subject: [PATCH 09/11] fix passing of data-dir and which-index

---
 azure/README.md |  4 ++--
 reginald/run.py | 10 ++++++++--
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/azure/README.md b/azure/README.md
index bda24393..af80a647 100644
--- a/azure/README.md
+++ b/azure/README.md
@@ -48,6 +48,6 @@ You will need to source this file before deploying in the next step.
 4. Deploy with Pulumi
 
 ```bash
-> source .secrets (if this exists)
-> AZURE_KEYVAULT_AUTH_VIA_CLI=true pulumi up
+source .secrets
+AZURE_KEYVAULT_AUTH_VIA_CLI=true pulumi up
 ```
diff --git a/reginald/run.py b/reginald/run.py
index 26bb0993..ef1c6fb1 100644
--- a/reginald/run.py
+++ b/reginald/run.py
@@ -19,6 +19,8 @@ def main(
 
         asyncio.run(
             run_full_pipeline(
+                data_dir=data_dir,
+                which_index=which_index,
                 slack_app_token=slack_app_token,
                 slack_bot_token=slack_bot_token,
                 **kwargs,
@@ -38,7 +40,9 @@ def main(
     elif cli == "app":
         from reginald.models.app import run_reginald_app
 
-        asyncio.run(run_reginald_app(**kwargs))
+        asyncio.run(
+            run_reginald_app(data_dir=data_dir, which_index=which_index, **kwargs)
+        )
     elif cli == "chat":
         import warnings
 
@@ -46,7 +50,9 @@ def main(
 
         from reginald.models.chat_interact import run_chat_interact
 
-        run_chat_interact(streaming=streaming, **kwargs)
+        run_chat_interact(
+            streaming=streaming, data_dir=data_dir, which_index=which_index, **kwargs
+        )
     elif cli == "create_index":
         from reginald.models.create_index import create_index
 

From 21d81657969586a78daadb57ad30afbbe645f612 Mon Sep 17 00:00:00 2001
From: Dr Griffith Rees <griff.rees@gmail.com>
Date: Thu, 13 Jun 2024 14:08:20 +0100
Subject: [PATCH 10/11] feat(ci): add `-h` abbreviation for `--help`

---
 reginald/cli.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/reginald/cli.py b/reginald/cli.py
index 076f3c30..0b9c79d7 100644
--- a/reginald/cli.py
+++ b/reginald/cli.py
@@ -34,7 +34,7 @@
     "slack_bot_token": "Slack bot token for the bot.",
 }
 
-cli = typer.Typer()
+cli = typer.Typer(context_settings={"help_option_names": ["-h", "--help"]})
 
 
 def set_up_logging_config(level: int = 20) -> None:

From 9cad1235f14a0f31f6877d018ab2c2ab821e6646 Mon Sep 17 00:00:00 2001
From: rchan <rchan@turing.ac.uk>
Date: Thu, 13 Jun 2024 14:09:42 +0100
Subject: [PATCH 11/11] app shouldnt be a async func

---
 reginald/models/app.py | 2 +-
 reginald/run.py        | 4 +---
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/reginald/models/app.py b/reginald/models/app.py
index 04808e40..beedae0a 100644
--- a/reginald/models/app.py
+++ b/reginald/models/app.py
@@ -43,7 +43,7 @@ async def channel_mention(query: Query):
     return app
 
 
-async def run_reginald_app(**kwargs) -> None:
+def run_reginald_app(**kwargs) -> None:
     # set up response model
     response_model = setup_llm(**kwargs)
     app: FastAPI = create_reginald_app(response_model)
diff --git a/reginald/run.py b/reginald/run.py
index ef1c6fb1..75a3cd69 100644
--- a/reginald/run.py
+++ b/reginald/run.py
@@ -40,9 +40,7 @@ def main(
     elif cli == "app":
         from reginald.models.app import run_reginald_app
 
-        asyncio.run(
-            run_reginald_app(data_dir=data_dir, which_index=which_index, **kwargs)
-        )
+        run_reginald_app(data_dir=data_dir, which_index=which_index, **kwargs)
     elif cli == "chat":
         import warnings