Another problem with reproducibility is the openness of the underlying data. Many academics are terrified of giving away the golden goose and the software is often useless without the data.

However many scientists do work openly, e.g. The Journal of Open Source Software:

https://joss.theoj.org/

At least some journals have this as a stipulation e.g. https://www.nature.com/nature-portfolio/editorial-policies/r...

Particularly the "data availability" and "Availability and peer review of computer code and algorithm".

However, in my limited experience, of trying to reproduce certain scRNA-seq processing pipelines, in practice it's never available as just a Github link. I can understand that some/many researcher's code is not in good shape, so I think it'll be quite a stretch to have this available.

I do think it's laudable though, to try and make it available. It would certainly have been very useful for me in the past.

I am particularly fascinated by labs like DeepMind [https://deepmind.google/science/]. The recent advances in their frontier models that are able to predict diseases before they're diagnosed is incredible. This is what AI should be built for and actually do!