I created Docker Containers for every "release" of the paper that we maintain in our Gitlab Registry (a CI builds those images automatically from `.gitlab-ci.yml`, `docker-compose.yml` using `docker:dind`), so you can pull a specific Docker Image for every version of the paper that will definitely have the correct dependencies (because the Jupyter Notebooks were tested with each specific version), including Jupyter itself.

That is awesome and should be the minimum standard, but even just getting the entire infrastructure set up is something almost all scientists are not willing to do.

In my experience, one should be happy if code is version controlled in a proper way. Too often, it isn't. In ML this might be a little different but in my field at least (electrical engineering) this is not the case at all.

Yes, I know and I was pretty stubborn with my initial goals to go through with this.. (probably will also cost my employment, but I learned a lot and it was thus still worth it).