Update paper after carpentries lab review process #554
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Thank you!Thank you for your pull request 😃 🤖 This automated message can help you check the rendered files in your submission for clarity. If you have any questions, please feel free to open an issue in {sandpaper}. If you have files that automatically render output (e.g. R Markdown), then you should check for the following:
Rendered Changes🔍 Inspect the changes: https://github.com/carpentries-lab/deep-learning-intro/compare/md-outputs..md-outputs-PR-554 The following changes were observed in the rendered markdown documents: What does this mean?If you have source files that require output and figures to be generated (e.g. R Markdown), then it is important to make sure the generated figures and output are reproducible. This output provides a way for you to inspect the output in a diff-friendly manner so that it's easy to see the changes that occur due to new software versions or randomisation. ⏱️ Updated at 2025-02-11 11:44:51 +0000 |
carschno
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Feb 11, 2025
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carschno
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Feb 11, 2025
tobyhodges
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I have suggested a couple of changes, but do not feel strongly about them so feel free to disregard if you prefer.
I will open a separate PR to update the URLs in the paper to point to the new location of the lesson/repo in @carpentries-lab 🎉
| This course was taught 12 times over the course of 3 years, both online and in-person, by the Netherlands eScience Center | ||
| (Netherlands, https://www.esciencecenter.nl/) and Helmholz-Zentrum Dresden-Rossendorf (Germany, https://www.hzdr.de/). | ||
| This course was taught 13 times over the course of 4 years, both online and in-person, by the Netherlands eScience Center | ||
| (Netherlands, https://www.esciencecenter.nl/) and Helmholtz-Zentrum Dresden-Rossendorf (Germany, https://www.hzdr.de/). | ||
| Apart from the core group of contributors, the workshop was also taught at 3 independent institutes, namely: |
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| Apart from the core group of contributors, the workshop was also taught at 3 independent institutes, namely: | |
| Apart from the core group of contributors, the workshop was also taught at at least 3 independent institutes, namely: |
| Apart from the core group of contributors, the workshop was also taught at 3 independent institutes, namely: | ||
| University of Wisconson-Madison (US, https://www.wisc.edu/), University of Auckland (New Zealand, https://www.auckland.ac.nz/), | ||
| and EMBL Heidelberg (Germany, https://www.embl.org/sites/heidelberg/). | ||
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| An up-to-date list of workshops using this lesson can be found in a `workshops.md` file in the GitHub repository (https://github.com/carpentries-incubator/deep-learning-intro/blob/main/workshops.md). |
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| An up-to-date list of workshops using this lesson can be found in a `workshops.md` file in the GitHub repository (https://github.com/carpentries-incubator/deep-learning-intro/blob/main/workshops.md). | |
| An up-to-date list of workshops that the authors are aware of having using this lesson can be found in a `workshops.md` file in the GitHub repository (https://github.com/carpentries-incubator/deep-learning-intro/blob/main/workshops.md). |
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| An up-to-date list of workshops using this lesson can be found in a `workshops.md` file in the GitHub repository (https://github.com/carpentries-incubator/deep-learning-intro/blob/main/workshops.md). | |
| An up-to-date list of workshops using this lesson can be found in a `workshops.md` file in the GitHub repository (https://github.com/carpentries-lab/deep-learning-intro/blob/main/workshops.md). |
| ## Carpentries Lab review process | ||
| Prior to submitting this paper the lesson went through the substantial review in the process of becoming an official Carpentries Lab (https://carpentries-lab.org/) lesson. This led to a number of improvements to the lesson. In general the accessibility and user-friendliness improved, for example by updating alt-texts and using more beginner-friendly and clearer wording. Additionally, the instructor notes were improved and many missing explanations of important deep learning concepts were added to the lesson. | ||
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| Most importantly, the reviewers pointed out that the CIFAR-10 [@noauthor_cifar-10_nodate] dataset that we initially used does not have a license. We were surprised to find out that this dataset, that is one of the most widely used datasets in the field of machine learning and deep learning, is actually unethically scraped from the internet without permission from image owners. As an alternative we now use 'Dollar street 10' [@van_der_burg_dollar_2024], a dataset that was adapted for this lesson from The Dollar Street Dataset (@gaviria_rojas_dollar_2022). The Dollar Street Dataset is representative and contains accurate demographic information to ensure their robustness and fairness, especially for smaller subpopulations. In addition, it is a great entry to teach learners about ethical AI and bias in datasets. |
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| Most importantly, the reviewers pointed out that the CIFAR-10 [@noauthor_cifar-10_nodate] dataset that we initially used does not have a license. We were surprised to find out that this dataset, that is one of the most widely used datasets in the field of machine learning and deep learning, is actually unethically scraped from the internet without permission from image owners. As an alternative we now use 'Dollar street 10' [@van_der_burg_dollar_2024], a dataset that was adapted for this lesson from The Dollar Street Dataset (@gaviria_rojas_dollar_2022). The Dollar Street Dataset is representative and contains accurate demographic information to ensure their robustness and fairness, especially for smaller subpopulations. In addition, it is a great entry to teach learners about ethical AI and bias in datasets. | |
| Most importantly, the reviewers pointed out that the CIFAR-10 [@noauthor_cifar-10_nodate] dataset that we initially used does not have a license. We were surprised to find out that this dataset, that is one of the most widely used datasets in the field of machine learning and deep learning, is actually unethically scraped from the internet without permission from image owners. As an alternative we now use 'Dollar street 10' [@van_der_burg_dollar_2024], a dataset that was adapted for this lesson from The Dollar Street Dataset (@gaviria_rojas_dollar_2022). The Dollar Street Dataset is representative and contains accurate demographic information to ensure their robustness and fairness, especially for smaller subpopulations. In addition, it is a great entry point to teach learners about ethical AI and bias in datasets. |
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Fixes #505
@annefou @florian-huber @dafnevk @psteinb @bpmweel @colinsauze @samumantha @dsmits @CunliangGeng @cpranav93 @tobyhodges
We are now really really close to our well-deserved paper featuring our lesson in the Journal of Open Source Education (JOSE) 🎉🎉
This is a follow-up of #366. I want to ask you if you can do another quick check to see if you like the changes made after the Carpentries Lab review that we just successfully finished.
Timeline
11th of February - @svenvanderburg drafted improvements to the paper after the Carpentries Lab review
Before the 24th of February - Everyone can check whether they like the updated paper
24th of February - @svenvanderburg will incorporate everyone's final feedback.
25th of February - @svenvanderburg (or @tobyhodges) will submit the paper to JOSE
Let me know if this timeline does not work for you! 🤗