Analyze all Jupyter notebooks mentioned in PMC

[Daniel-Mietchen]

Jupyter notebooks are a popular vehicle these days to share data science workflows. To get an idea of best practices in this regard, it would be good to analyze a good number of them in terms of their reproducibility and other aspects of usability (e.g. documentation, ease of reuse).

A search in PubMed Central reveals the following results:

• query for Jupyter
• query for ipynb

[Daniel-Mietchen]

I've now turned this into a milestone over at
https://github.com/Daniel-Mietchen/learning2code/milestone/3 .

[Daniel-Mietchen]

1h to get things started. Also added the 1day label, as this could be a nice hackathon project.

[Daniel-Mietchen]

Just posted it as a hackathon project:
sparcopen/open-research-doathon#25 .

[Daniel-Mietchen]

The hackathon was very insightful in terms of this issue - see the documentation there. I am now thinking about submitting a talk to JupyterCon.

[Daniel-Mietchen]

Removed the 1h label for the time being, since this really needs concentrated action for several hours.

[Daniel-Mietchen]

The corresponding study has just been published:
Sheeba Samuel, Daniel Mietchen, Computational reproducibility of Jupyter notebooks from biomedical publications, GigaScience, Volume 13, 2024, giad113, https://doi.org/10.1093/gigascience/giad113 .