| name | about | title | labels | assignees |
|---|---|---|---|---|
🔦 Turing Data Story Review |
Procedure and Checklist to review a Turing Data Story |
Story Name:
Submitting Author: ()
Pull Request:
Reviewers:
, 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 ✨
- I confirm that I read and will adhere to the Turing Data Stories code of conduct.
- 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?
- 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?
- 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)?
- Does the story give an insight into some societal issue?
- Is the context around this issue well referenced (newspaper articles, scientific papers, etc.)?
- 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?