Project

How to use this template

You will need to have datalad installed

Install with Datalad

1. Click the green button Use this template.

2. Give a name to the repository you want to create. Something short that contains the name of your experiment: analysis_fMRI_FaceLocalizer.

3. Decide if you want this new repo to be public or private.

4. Click on Create repository from template

You now have a copy of the template on your Github account. You can then download the code and the pre-set dependencies like this.

1. Click on green Code button and copy the URL_to_your_repo that is shown there.

2. Open a terminal and type this:

datalad install --recursive URL_to_your_repo

This will set up everything automatically in your current directory.

Dataset structure

• All inputs (i.e. building blocks from other sources) are located in inputs/.
• All custom code is located in code/.
• All outputs will go in outputs/

DEMO

Clone input data: raw data set of visual localizer from the CPP lab

datalad clone -d . git@gin.g-node.org:/cpp-lln-lab/CPP_visMotion-raw.git inputs/raw

You will need to set up a proper alias to launch matlab from your mac or linux command line to tdo the next step:

matlab -nodisplay -nosplash -nodesktop -r "run('code/src/demoRunSpatialPrepro.m'); exit;"


# OR

matlab -nodisplay -nosplash -nodesktop -r 'run code/src/demoRunSpatialPrepro.m; exit;'

To make it easier to understand, when can use some bash variables

MATFLAGS="-nodisplay -nosplash -nodesktop"
cmd_to_run="run('code/src/demoRunSpatialPrepro.m'); exit;"
matlab $MATFLAGS -r "$cmd_to_run"

And now we insert in a datalad run command

# THIS DOES NOT WORK
datalad run -m "Try again to create a list of podcast titles" \
 --input "inputs/raw" \
 --output "outputs/derivatives" \
 "matlab -nodisplay -nosplash -nodesktop -r 'run code/src/demoRunSpatialPrepro.m; exit;'"

datalad run -m "Try again to create a list of podcast titles" \
   --input "inputs/raw" \
   --output "outputs/derivatives" \
  'matlab $MATFLAGS -r "$cmd_to_run"'

DataLad datasets and how to use them

This repository is a DataLad dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, DataLad is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and git-annex to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at handbook.datalad.org/en/latest/intro/installation.html.

Get the dataset

A DataLad dataset can be cloned by running

datalad clone <url>

Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual content of the (sometimes large) data files.

Retrieve dataset content

After cloning a dataset, you can retrieve file contents by running

datalad get <path/to/directory/or/file>`

This command will trigger a download of the files, directories, or subdatasets you have specified.

DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run

datalad get -n <path/to/subdataset>

Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with datalad get. If you use datalad get <path/to/subdataset>, all contents of the subdataset will be downloaded at once.

Stay up-to-date

DataLad datasets can be updated. The command datalad update will fetch updates and store them on a different branch (by default remotes/origin/master). Running

datalad update --merge

will pull available updates and integrate them in one go.

Find out what has been done

DataLad datasets contain their history in the git log. By running git log (or a tool that displays Git history) in the dataset or on specific files, you can find out what has been done to the dataset or to individual files by whom, and when.

More information

More information on DataLad and how to use it can be found in the DataLad Handbook at handbook.datalad.org. The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.