Some tips and tools for maintaining reproducibility in scientific workflows that use Jupyter notebooks

I wanted to share some stuff I’ve built and lessons learned regarding the use of Jupyter notebooks as part of a larger research project. Namely, how to keep things reproducible (with a single command) while retaining the benefits of interactivity notebooks provide.

You can read the docs here: https://docs.calkit.org/notebooks

Do you have any of your own tips and tricks for incorporating notebooks into a larger research or analytical project?

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