Story Review: Issue 174

Story Name: Machine Learning for the 20th century: Can a United Kingdom database from 1987 classify a glass cosmetics jar from the 1930s that might have belonged to Amelia Earhart?

Submitting Author: joecerniglia

Pull Request: #176

Reviewers: @lukehare , Eirini Zormpa and Jennifed Ding

Reviewer instructions & questions

@lukehare, Eirini Zormpa and Jennifed Ding , please carry out your review in this issue by updating the checklist below, and writing new comments in case you have any questions. Specific comments about text can be done using the ReviewNB app linked to the PR. If you cannot edit the checklist please:

Make sure you're logged in to your GitHub account
Be sure to accept the invite at this URL: https://github.com/alan-turing-institute/TuringDataStories/settings/access

Any questions, concerns or suggestions regarding the review process please let @crangelsmith, @DavidBeavan or @samvanstroud know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Review Checklist

Code of conduct

[ ] I confirm that I read and will adhere to the Turing Data Stories code of conduct.

General checks

[ ] Notebook: Is the source code for this data story available as a notebook in the linked pull request?
[ ] Contribution and authorship: Are the authors clearly listed? Does the author list seem appropriate and complete?
[ ] Scope and eligibility: Does the submission contain an original and complete analysis of open data? Is the story aligned with the Turing Data Stories vision statement?

Reproducibility

[ ] Does the notebook run in a local environment?
[ ] Does the notebook build and run in binder?
[ ] Are all data sources openly accessible and properly cited with a link?
[ ] Are the data open, and do they have an explicit licence, provenance and attribution?

Pedagogy

[ ] Does the story demonstrate some specific data analysis or visualisation techniques?
[ ] Are these techniques well motivated?
[ ] Are these techniques well implemented?
[ ] Is the notebook well documented, using both markdown cells and comments in code cells?
[ ] Does the notebook has a introduction section motivating the story?
[ ] Does the notebook has a conclusion section discussing the main insight from the stories?
[ ] Is the paper well written (it does not require editing for structure, language, or writing quality)?

Context

[ ] Does the story give an insight into some societal issue?
[ ] Is the context around this issue well referenced (newspaper articles, scientific papers, etc.)?

Ethical

[ ] Is any linkage of datasets in the story unlikely to lead to an increased risk of the personal identification of individuals?
[ ] Is the Story truthful and clear about any limitations of the analysis (and potential biases in data)?
[ ] Is the Story unlikely to lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice?

AOB

Please use the ReviewNB app to submit your reviews on the notebook, the story to be reviewed is under the name stories/2022-03-27_Glass_ML/ML_20th_Century.ipynb. Other files are complementary and do not need to be reviewed.

Agree between yourselves on how to divide the review work. For example, one could take the more technical review and others focus on the storytelling, etc.

Jul 06 '22 12:07 crangelsmith

thank you for the explanation 😊 this link: https://github.com/alan-turing-institute/TuringDataStories/settings/access doesn't seem to work?

Jul 13 '22 17:07 eirini-zormpa

thank you for the explanation 😊 this link: https://github.com/alan-turing-institute/TuringDataStories/settings/access doesn't seem to work?

Oh sorry about that! I have now added you as a collaborator to the project, so you should have access now.

Jul 14 '22 10:07 crangelsmith

@crangelsmith could you add me as well? link currently doesn't work for me either!

Jul 14 '22 13:07 dingaaling