New blog post!
What would you do as a reviewer if you suspected that the methods described in a paper weren't accurate?
Here, I walk through such a case and explain how access to the data allow me to test my suspicions.
http://steveharoz.com/blog/2023/why-open-data-is-critical-during-review-an-example-case/
COS TOP level 2 (https://www.cos.io/initiatives/top-guidelines) outlines a journal policy for mandatory transparency.
1. Mandatory experiment code/stimuli + data + analysis code (I prefer RMD, but jupyter is works too)
2. On a public persistent repo (not hosted by the journal) such as http://osf.io
3. If anything can't be shared (e.g. for privacy), the paper needs to share what it can and be very explicit about why ("privacy" is often a BS excuse).
Steve Haroz (@sharoz on 🐤)
Stefan Zitz