From 98e3ecaf55e69cbd33853a0235f4d33e84217f64 Mon Sep 17 00:00:00 2001 From: Rosie Wood Date: Wed, 6 Dec 2023 09:13:16 +0000 Subject: [PATCH 1/6] add paper --- .github/workflows/joss-draft-pdf.yml | 23 ++++++ paper/paper.bib | 112 +++++++++++++++++++++++++++ paper/paper.md | 102 ++++++++++++++++++++++++ 3 files changed, 237 insertions(+) create mode 100644 .github/workflows/joss-draft-pdf.yml create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/.github/workflows/joss-draft-pdf.yml b/.github/workflows/joss-draft-pdf.yml new file mode 100644 index 00000000..eecf0d13 --- /dev/null +++ b/.github/workflows/joss-draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper/paper.pdf diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..0cf8e382 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,112 @@ + +@inproceedings{Hosseini_mapreader, + address = {Seattle Washington}, + title = {{MapReader}: a computer vision pipeline for the semantic exploration of maps at scale}, + isbn = {9781450395335}, + shorttitle = {{MapReader}}, + url = {https://dl.acm.org/doi/10.1145/3557919.3565812}, + doi = {10.1145/3557919.3565812}, + language = {en}, + urldate = {2023-11-23}, + booktitle = {Proceedings of the 6th {ACM} {SIGSPATIAL} {International} {Workshop} on {Geospatial} {Humanities}}, + publisher = {ACM}, + author = {Hosseini, Kasra and Wilson, Daniel C. S. and Beelen, Kaspar and McDonough, Katherine}, + month = nov, + year = {2022}, + pages = {8--19}, +} + +@misc{Hosseini_mapreader_data, + title = {{MapReader}\_Data\_SIGSPATIAL\_2022}, + copyright = {Creative Commons Attribution 4.0 International, Open Access}, + url = {https://zenodo.org/record/7147906}, + doi = {10.5281/ZENODO.7147906}, + abstract = {{\textless}strong{\textgreater}MapReader in GeoHumanities workshop (SIGSPATIAL 2022): Gold standards and outputs{\textless}/strong{\textgreater} Refer to: {\textless}br{\textgreater} https://github.com/Living-with-machines/MapReader/wiki/GeoHumanities-workshop-in-SIGSPATIAL-2022}, + urldate = {2023-11-23}, + publisher = {Zenodo}, + author = {Hosseini, Kasra and Wilson, Daniel C.S. and Beelen, Kaspar and McDonough, Katherine}, + month = oct, + year = {2022}, + keywords = {Computer vision, Deep learning, Supervised learning, Classification, Historical maps, Digital libraries and archives}, +} + +@article{Hosseini_maps, + title = {Maps of a {Nation}? {The} {Digitized} {Ordnance} {Survey} for {New} {Historical} {Research}}, + volume = {26}, + issn = {1355-5502, 1750-0133}, + shorttitle = {Maps of a {Nation}?}, + url = {https://academic.oup.com/jvc/article/26/2/284/6232245}, + doi = {10.1093/jvcult/vcab009}, + language = {en}, + number = {2}, + urldate = {2023-11-23}, + journal = {Journal of Victorian Culture}, + author = {Hosseini, Kasra and McDonough, Katherine and Van Strien, Daniel and Vane, Olivia and Wilson, Daniel C S}, + month = may, + year = {2021}, + pages = {284--299}, +} + +@article{Combes, + title = {Urban economics in a historical perspective: {Recovering} data with machine learning}, + volume = {94}, + issn = {01660462}, + shorttitle = {Urban economics in a historical perspective}, + url = {https://linkinghub.elsevier.com/retrieve/pii/S0166046221000715}, + doi = {10.1016/j.regsciurbeco.2021.103711}, + language = {en}, + urldate = {2023-11-23}, + journal = {Regional Science and Urban Economics}, + author = {Combes, Pierre-Philippe and Gobillon, Laurent and Zylberberg, Yanos}, + month = may, + year = {2022}, + pages = {103711}, +} + +@misc{mapkurator, + title = {{mapKurator}}, + url = {https://knowledge-computing.github.io/mapkurator-doc/#/}, + abstract = {Description}, + urldate = {2023-11-23}, + author = {Knowledge Computing Lab}, + publisher = {Github}, + journal = {Github repository}, +} + +@article{Petitpierre, + title = {Neural networks for semantic segmentation of historical city maps: {Cross}-cultural performance and the impact of figurative diversity}, + copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}, + shorttitle = {Neural networks for semantic segmentation of historical city maps}, + url = {https://arxiv.org/abs/2101.12478}, + doi = {10.48550/ARXIV.2101.12478}, + urldate = {2023-11-23}, + author = {Petitpierre, Rémi}, + year = {2021}, +} + +@article{Arnold, + title = {Distant {Viewing} {Toolkit}: {A} {Python} {Package} for the {Analysis} of {Visual} {Culture}}, + volume = {5}, + issn = {2475-9066}, + shorttitle = {Distant {Viewing} {Toolkit}}, + url = {https://joss.theoj.org/papers/10.21105/joss.01800}, + doi = {10.21105/joss.01800}, + abstract = {Arnold et al., (2020). Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture. Journal of Open Source Software, 5(45), 1800, https://doi.org/10.21105/joss.01800}, + language = {en}, + number = {45}, + urldate = {2023-11-23}, + journal = {Journal of Open Source Software}, + author = {Arnold, Taylor and Tilton, Lauren}, + month = jan, + year = {2020}, + pages = {1800}, +} + + +@article{Corcoran, + title = {Automated extraction of dynamic phenotype data from whole plant images collected under controlled conditions}, + journal = {Frontiers in Plant Science}, + author = {Corcoran, E and Hosseini, K and Siles, L and Kurup, S and Ahnert, S}, + year = {2023}, + pages = {In preparation}, +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..9c4c745e --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,102 @@ +--- +title: 'MapReader: v1.xxx' # check version number at time of submission +tags: + - Python + - computational humanities + - maps + - computer vision + - history + - digital humanities + - image classification +authors: + - name: Katherine McDonough + orcid: 0000-0001-7506-1025 + affiliation: "1, 2" # (Multiple affiliations must be quoted) + corresponding: true # (This is how to denote the corresponding author) + - name: Daniel C.S. Wilson + orcid: 0000-0001-6886-775X + affiliation: 1 + - name: Andrew Smith + orcid: 0000-0002-4465-2284 + affiliation: 1 + - name: Kaspar Beelen + orcid: 0000-0001-7331-1174 + affiliation: 3 + - name: Kalle Westerling + orcid: 0000-0002-2014-332X + affiliation: 1 + - name: Rosie Wood + orcid: 0000-0003-1623-1949 + affiliation: 1 + - name: Kasra Hosseini + orcid: 0000-0003-4396-6019 + affiliation: "1, 4" +affiliations: + - name: The Alan Turing Institute, UK + index: 1 + - name: Lancaster University, UK + index: 2 + - name: School of Advanced Study, University of London, UK + index: 3 + - name: Zalando SE, Germany + index: 4 +date: 4 December 2023 +bibliography: paper.bib + + +--- + +# Summary + +MapReader is an interdisciplinary software library for processing digitized maps, but also other types of images, by 'patching' then into small, custom-sized cells, which are then classified according to the user's needs. MapReader thus offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for Computer Vision-based inference of large collections. Its approach was inspired by methods in biomedical imaging, which were adapted for use by historians, and it is suitable for a wide range of applications in image analysis: it has, for example, been applied to an image classification problem in plant phenotype research `[@Corcoran]`. This cross-pollination between the humanities and the natural sciences was made possible by the open and reproducible research methods at the heart of MapReader. + +MapReader pioneers a methodological shift in how historians interact with maps as primary sources. Sustained engagement with big collections of maps rarely moves beyond analysis of cartographic history. To change this, MapReader encourages historians to reflect on the content of maps and is designed to facilitate linking datasets representing visual map content with other historical geospatial data. + +In this paper, we present the MapReader release at the conclusion of the Living with Machines project, which supported the development of the software and associated research. This release represents the culmination of extensive work to improve MapReader's usability, especially through clear documentation and tutorials. + +![MapReader modules and input-outputs. Credit: Rosie Wood.\label{fig:modules}](https://hackmd.io/_uploads/HJWJatQEa.png) + + +# Statement of need + +Since the 1990s, map libraries have been scanning maps and creating digital collections of these images. In 2023, there are more than a million images of maps in digital libraries and archives around the world, and yet it is very difficult for anyone to do more than browse them in a web viewer. + +MapReader makes it possible to ask questions of thousands of digitized maps at a time, a fundamentally different intellectual experience from both the traditional manner of viewing a few maps at a time on a reading room table and the act of visually scanning digital files sequentially. Inspired by the possibility of seeing series maps stitched together in seamless layers, such as the National Library of Scotland Ordnance Survey map viewing interface, MapReader takes the next step by transforming the experience of working with maps from surface exploration to critical investigation `[Hosseini_maps]`. + + +# Related Work + +MapReader is among the first end-to-end pipeline for processing historical maps and other images that was designed to lower barriers to experimenting with computer vision in answering research questions about large image datasets. Other projects are emerging which are performing similar research tasks with the visual content in historical map collections `[@Petitpierre; @Combes]`, and of course other tools, like the Distant Viewing Toolkit `[@Arnold]`, address similar needs for other kinds of media. As part of a collaboration between Machines Reading Maps and the David Rumsey Historical Map Collection, the Knowledge Computing Lab released mapKurator `[@mapkurator]`, which takes map image input and creates geojson files of all text on each map. + + +# Documentation + +MapReader aims to build computational skills among historians. Our extensive work on documentation and training, including substantial updates to MapReader since `@Hosseini_mapreader`, reflect this commitment. As historians explore the possibilities of computational methods for novel historical research, MapReader models how computational tools can unlock difficult to use primary sources and how we can embrace open research practices as a way to encourage learning. We welcome contributions and requests for new documentation or tutorials. + +Our documentation includes: +- About MapReader: A basic introduction to the software and its origins +- Events: Activities where the community can engage with MapReader +- Project CV: Papers, talks, workshops, etc. delivered by the MapReader team +- Coding Basics: Skills and tools that are useful for getting started with MapReader +- Installation Instructions: How to install MapReader +- Input Guidance: What kind of maps and which formats work well in MapReader +- User Guide: Walkthrough of how to run MapReader +- Worked Examples: Jupyter notebooks demonstrating uses of MapReader for specific cases (with data, e.g. `@Hosseini_mapreader_data`) +- API Reference: Auto-generated API reference documentation +- Code of Conduct and Inclusivity: Our approach to ethical and inclusive conduct +- Contribution Guide: How to engage with MapReader +- Developer's Guide: How to update the MapReader version number and upload to package managers + +# Conclusion + +MapReader enables researchers to ask questions of large collections of maps. It represents a novel approach to digitizing map content, one which intentionally prevents the collection of overly precise data from cartographic documents. MapReader embraces a humanistic approach to data creation and curation, offering an alternative or complement to pixel-level image segmentation. + +# Acknowledgements + +This work was supported by Data/Culture (AHRC grant AH/XXXXX), Living with Machines (AHRC grant AH/S01179X/1), and The Alan Turing Institute (EPSRC grant EP/N510129/1). Living with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, was a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London. Maps and their metadata in MapReader are reproduced with the permission of the National Library of Scotland (https://maps.nls.uk/index.html). We also wish to thank participants in events in 2023 who provided feedback on using MapReader. + +# Contribution Statement + +Katherine McDonough wrote and revised this article, with substantial contributions from Daniel C.S. Wilson and Rosie Wood. Please see complete and ongoing documentation of contributions to the MapReader software library at https://github.com/Living-with-machines/MapReader#contributors, including work from all named authors. + +# References From c6ebab7a825c40aadf33d9b3759334b37773c284 Mon Sep 17 00:00:00 2001 From: Kasra Hosseini Date: Mon, 11 Dec 2023 10:50:52 +0100 Subject: [PATCH 2/6] Small changes in the first paragraph; reordered tags and added DL --- paper/paper.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 9c4c745e..5ed8f97c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -2,12 +2,13 @@ title: 'MapReader: v1.xxx' # check version number at time of submission tags: - Python + - image classification + - computer vision + - deep learning - computational humanities + - digital humanities - maps - - computer vision - history - - digital humanities - - image classification authors: - name: Katherine McDonough orcid: 0000-0001-7506-1025 @@ -40,7 +41,7 @@ affiliations: index: 3 - name: Zalando SE, Germany index: 4 -date: 4 December 2023 +date: 11 December 2023 bibliography: paper.bib @@ -48,7 +49,9 @@ bibliography: paper.bib # Summary -MapReader is an interdisciplinary software library for processing digitized maps, but also other types of images, by 'patching' then into small, custom-sized cells, which are then classified according to the user's needs. MapReader thus offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for Computer Vision-based inference of large collections. Its approach was inspired by methods in biomedical imaging, which were adapted for use by historians, and it is suitable for a wide range of applications in image analysis: it has, for example, been applied to an image classification problem in plant phenotype research `[@Corcoran]`. This cross-pollination between the humanities and the natural sciences was made possible by the open and reproducible research methods at the heart of MapReader. +MapReader is an interdisciplinary software library for processing digitized maps `[@Hosseini_mapreader]`, but also other types of images, by 'patching' them into small, custom-sized cells, which are then classified according to the user's needs. MapReader thus offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for Computer Vision-based inference of large collections. As an example, in `@Hosseini_mapreader`, we utilized MapReader's interface to manually annotate 62,020 patches, used its functionalities to train a suite of computer vision models and performed model inference on approximately 30.5 million patches. + +MapReader's approach was inspired by methods in biomedical imaging, which were adapted for use by historians, and it is suitable for a wide range of applications in image analysis: it has, for example, been applied to an image classification problem in plant phenotype research `[@Corcoran]`. This cross-pollination between the humanities and the natural sciences was made possible by the open and reproducible research methods at the heart of MapReader. MapReader pioneers a methodological shift in how historians interact with maps as primary sources. Sustained engagement with big collections of maps rarely moves beyond analysis of cartographic history. To change this, MapReader encourages historians to reflect on the content of maps and is designed to facilitate linking datasets representing visual map content with other historical geospatial data. From b1520868e122f8fda0bc36f6e1da9a1a22ac7824 Mon Sep 17 00:00:00 2001 From: Kasra Hosseini Date: Mon, 11 Dec 2023 11:08:46 +0100 Subject: [PATCH 3/6] Add an example about 16K map sheets --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 5ed8f97c..b5189d0b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -64,7 +64,7 @@ In this paper, we present the MapReader release at the conclusion of the Living Since the 1990s, map libraries have been scanning maps and creating digital collections of these images. In 2023, there are more than a million images of maps in digital libraries and archives around the world, and yet it is very difficult for anyone to do more than browse them in a web viewer. -MapReader makes it possible to ask questions of thousands of digitized maps at a time, a fundamentally different intellectual experience from both the traditional manner of viewing a few maps at a time on a reading room table and the act of visually scanning digital files sequentially. Inspired by the possibility of seeing series maps stitched together in seamless layers, such as the National Library of Scotland Ordnance Survey map viewing interface, MapReader takes the next step by transforming the experience of working with maps from surface exploration to critical investigation `[Hosseini_maps]`. +MapReader makes it possible to ask questions of thousands of digitized maps at a time, a fundamentally different intellectual experience from both the traditional manner of viewing a few maps at a time on a reading room table and the act of visually scanning digital files sequentially. As an example, we used MapReader to process a collection of ~16K nineteenth-century Ordnance Survey map sheets (~30.5M patches) covering England, Wales and Scotland `[@Hosseini_mapreader]`. Inspired by the possibility of seeing series maps stitched together in seamless layers, such as the National Library of Scotland Ordnance Survey map viewing interface, MapReader takes the next step by transforming the experience of working with maps from surface exploration to critical investigation `[Hosseini_maps]`. # Related Work From 621a2089021641672c37ca35dccd6a01d29b3ce1 Mon Sep 17 00:00:00 2001 From: Kasra Hosseini Date: Mon, 11 Dec 2023 11:15:17 +0100 Subject: [PATCH 4/6] minor --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index b5189d0b..51942a8e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -74,7 +74,7 @@ MapReader is among the first end-to-end pipeline for processing historical maps # Documentation -MapReader aims to build computational skills among historians. Our extensive work on documentation and training, including substantial updates to MapReader since `@Hosseini_mapreader`, reflect this commitment. As historians explore the possibilities of computational methods for novel historical research, MapReader models how computational tools can unlock difficult to use primary sources and how we can embrace open research practices as a way to encourage learning. We welcome contributions and requests for new documentation or tutorials. +MapReader aims to build computational skills among historians. Our extensive work on documentation and training, including substantial updates to MapReader since `@Hosseini_mapreader`, reflect this commitment. As historians explore the possibilities of computational methods for novel historical research, MapReader models how computational tools can unlock difficult-to-use primary sources and how we can embrace open research practices as a way to encourage learning. We welcome contributions and requests for new documentation or tutorials. Our documentation includes: - About MapReader: A basic introduction to the software and its origins From 78d78bdd3376c0b01eddb842d2570415bb5fab3c Mon Sep 17 00:00:00 2001 From: Katie McDonough Date: Thu, 14 Dec 2023 08:52:38 +0000 Subject: [PATCH 5/6] Update paper.md per Kasra's comments --- paper/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 51942a8e..811b6aa5 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -55,14 +55,14 @@ MapReader's approach was inspired by methods in biomedical imaging, which were a MapReader pioneers a methodological shift in how historians interact with maps as primary sources. Sustained engagement with big collections of maps rarely moves beyond analysis of cartographic history. To change this, MapReader encourages historians to reflect on the content of maps and is designed to facilitate linking datasets representing visual map content with other historical geospatial data. -In this paper, we present the MapReader release at the conclusion of the Living with Machines project, which supported the development of the software and associated research. This release represents the culmination of extensive work to improve MapReader's usability, especially through clear documentation and tutorials. +In this paper, we present the MapReader release at the conclusion of the Living with Machines project, which supported the development of the software and associated historical research. This release represents the culmination of extensive work to improve MapReader's usability, especially through clear documentation and tutorials. ![MapReader modules and input-outputs. Credit: Rosie Wood.\label{fig:modules}](https://hackmd.io/_uploads/HJWJatQEa.png) # Statement of need -Since the 1990s, map libraries have been scanning maps and creating digital collections of these images. In 2023, there are more than a million images of maps in digital libraries and archives around the world, and yet it is very difficult for anyone to do more than browse them in a web viewer. +Since the 1990s, map libraries have been scanning maps and creating digital collections of these images `[@Hosseini_maps]`. In 2023, there are more than a million images of maps in digital libraries and archives around the world, and yet it is very difficult for anyone to do more than browse them in a web viewer. MapReader makes it possible to ask questions of thousands of digitized maps at a time, a fundamentally different intellectual experience from both the traditional manner of viewing a few maps at a time on a reading room table and the act of visually scanning digital files sequentially. As an example, we used MapReader to process a collection of ~16K nineteenth-century Ordnance Survey map sheets (~30.5M patches) covering England, Wales and Scotland `[@Hosseini_mapreader]`. Inspired by the possibility of seeing series maps stitched together in seamless layers, such as the National Library of Scotland Ordnance Survey map viewing interface, MapReader takes the next step by transforming the experience of working with maps from surface exploration to critical investigation `[Hosseini_maps]`. @@ -92,7 +92,7 @@ Our documentation includes: # Conclusion -MapReader enables researchers to ask questions of large collections of maps. It represents a novel approach to digitizing map content, one which intentionally prevents the collection of overly precise data from cartographic documents. MapReader embraces a humanistic approach to data creation and curation, offering an alternative or complement to pixel-level image segmentation. +Through its conceptual approach, modular structure, documentation, and worked examples, MapReader enables researchers to ask questions of large collections of maps. It represents a novel approach to digitizing map content, one which intentionally prevents the collection of overly precise data from cartographic documents. MapReader embraces a humanistic approach to data creation and curation, offering an alternative or complement to pixel-level image segmentation. # Acknowledgements @@ -100,6 +100,6 @@ This work was supported by Data/Culture (AHRC grant AH/XXXXX), Living with Machi # Contribution Statement -Katherine McDonough wrote and revised this article, with substantial contributions from Daniel C.S. Wilson and Rosie Wood. Please see complete and ongoing documentation of contributions to the MapReader software library at https://github.com/Living-with-machines/MapReader#contributors, including work from all named authors. +Katherine McDonough wrote and revised this article, with substantial contributions from Daniel C.S. Wilson and Rosie Wood. Andy Smith, Kalle Westerling, Kaspar Beelen and Kasra Hosseini reviewed the final manuscript. Please see contributions to the MapReader software library at https://github.com/Living-with-machines/MapReader#contributors, including work from all named authors. # References From edf22d57ae4c0e340b29442000218057cd01f039 Mon Sep 17 00:00:00 2001 From: Rosie Wood Date: Thu, 14 Dec 2023 12:58:25 +0000 Subject: [PATCH 6/6] minor updates + v number --- paper/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 811b6aa5..86df743d 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'MapReader: v1.xxx' # check version number at time of submission +title: 'MapReader: v1.1.0' tags: - Python - image classification @@ -41,7 +41,7 @@ affiliations: index: 3 - name: Zalando SE, Germany index: 4 -date: 11 December 2023 +date: 14 December 2023 bibliography: paper.bib @@ -49,7 +49,7 @@ bibliography: paper.bib # Summary -MapReader is an interdisciplinary software library for processing digitized maps `[@Hosseini_mapreader]`, but also other types of images, by 'patching' them into small, custom-sized cells, which are then classified according to the user's needs. MapReader thus offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for Computer Vision-based inference of large collections. As an example, in `@Hosseini_mapreader`, we utilized MapReader's interface to manually annotate 62,020 patches, used its functionalities to train a suite of computer vision models and performed model inference on approximately 30.5 million patches. +MapReader is an interdisciplinary software library for processing digitized maps `[@Hosseini_mapreader]`, but also other types of images, by 'patching' them into small, custom-sized cells, which are then classified according to the user's needs. MapReader thus offers a flexible pipeline which can be used both for manual annotation of small datasets as well as for Computer Vision-based inference of large collections. As an example, in `@Hosseini_mapreader`, we utilized MapReader's interface to manually annotate 62,020 patches, used its functionalities to train a suite of computer vision models and performed model inference on approximately 30.5 million patches. MapReader's approach was inspired by methods in biomedical imaging, which were adapted for use by historians, and it is suitable for a wide range of applications in image analysis: it has, for example, been applied to an image classification problem in plant phenotype research `[@Corcoran]`. This cross-pollination between the humanities and the natural sciences was made possible by the open and reproducible research methods at the heart of MapReader. @@ -79,7 +79,7 @@ MapReader aims to build computational skills among historians. Our extensive wor Our documentation includes: - About MapReader: A basic introduction to the software and its origins - Events: Activities where the community can engage with MapReader -- Project CV: Papers, talks, workshops, etc. delivered by the MapReader team +- Project Curriculum Vitae: Papers, talks, workshops, etc. delivered by the MapReader team - Coding Basics: Skills and tools that are useful for getting started with MapReader - Installation Instructions: How to install MapReader - Input Guidance: What kind of maps and which formats work well in MapReader