crangelsmith commented 1 year ago

Story Review:

Story Name: Esports analysis: Overwatch

**Submitting Author: @mhauru @kevinxufs

Pull Request: #163

Reviewers: @jack89roberts @llewelld

Reviewer instructions & questions

@jack89roberts, @llewelld, please carry out your review in this issue by updating the checklist below, and writing new comments in case you have any questions. 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

AoifeHughes commented 1 year ago

Great, thanks!

@jack89roberts how'd you like to do this? Shall we independently review + make notes, then have a chat / update the checklist? Happy to do whatever suits!

jack89roberts commented 1 year ago

Sure, let's each read through it then we can compare notes. I'll try to read it in the next week or so.

jack89roberts commented 1 year ago

Markus, Kevin et al.: I'm going to start adding comments to the PR as I read through but you can wait to look through them until me and Aoife have caught up about it.

mhauru commented 1 year ago

Thanks everyone, new story is now published: https://alan-turing-institute.github.io/TuringDataStories/stories/2023-02-01-Overwatch-League/OWL_story.html

alan-turing-institute / TuringDataStories

[Review] Overwatch story #219