Jupyter notebooks are risky business for reproducible work. No dependency data means they're highly prone to bit rot. Stored results and out-of-order execution means they're prone to subtle errors. Environmental leakage is relatively high.

Literate programming suffers from this in general because you don't want to clutter up your document with noise about versions and so forth - even as appendices, including package information and build instructions is a lot of noise.

Requiring that papers making claims about some code base publish that code base simultaneously with the paper via the peer review process should be sufficient to improve the overall state of affairs.

Don't underestimate the value of demos and competitions with companion papers either, those tend to get a lot more notice.

I dunno, I get where you are coming from with respect to literate programming, but I find that it's often better to show all the versions in an org file (or whatever tool you use) and write up a report separately including the final results.

In general, you'll have a lot of approaches that don't work out which are nice to have a record of, but definitely don't merit being in the final paper.

A published code base doesn't necessarily mean a full revision history, just something others can reproducibly build and run.

If your claims don't depend on specific behaviours of some body of code, you wouldn't need it - eg, an article claiming some asymptotic performance of an algorithm should describe the algorithm in the abstract s.t. the performance bound can be proven, not some specific language's implementation of the algorithm.

Seems like one should be able to tag some metadata to a notebook - as little as a git url and a hash or as exotic as an IPFS link or (I hate to say it) some other blockchain info. Somehow dependencies would seem to be something needed to be specified in the submittal standards of a code-required journal

My experience with ML notebooks in particular has been poor. But most of the publications I read are not in the field of ML, and I tend to prefer ones that use an abstract definition of an algorithm rather than code in any case - whatever the language of the day is, it probably won't be compilable in fifteen years, but the algorithm will probably still be relevant.

Formatting articles as jupyter notebooks might help with reproducibility, but it wouldn't necessarily help with understanding.

distill.pub has a journal that strongly encourages interactive articles: https://distill.pub/journal/

It's a lot of work, tho:

> One good test for whether your article is a fit for Distill is whether your collaborators and you are willing to put in whatever time is necessary to write and illustrate an outstanding article. In our experience, this often takes 100+ hours.

Reproducibility is one of the core problems of the article. I'm not sure any format can assure understanding by itself - that's more the purview of the reviewers I would think. Applying the criticism to my own suggestion: even the reproducibility issue is only tangentially helped by a Jupyter format, it may only bring reviewers to the point where one might execute the notebook (though there are still issues there if its an exceeding long computation or needs special hardware setup, or the notebook executes w/o demonstrating actual computation).

This is a current area of research as the prevalence of interactive notebooks has peaked. As of now, they simply "EXIST" and we know we like them. Its still unclear as to why; obviously having code intertwined other common communication mediums but what about that makes us like it. What benefits does it have over pseudocode or just code? What about code beyond small scripts (500+ lines with design patterns or other abstractions)?

There are some journals who have started taking it seriously. eLife does that: asks authors to put their code on GitHub/gitlab etc. Fork their repo and use that as snapshot for the published paper. I like the eLife approach.

But for most journals, it's the story as usualy. Personally I dont feel very confident about a paper result if they don't publish the code. Why hold back on the code?