These are some concepts that I believe in for research code. Research code shouldn't be a monolith. Each hypothesis should be a script that follows a data pipe

These are some concepts that I believe in for research code. Research code shouldn't be a monolith. Each hypothesis should be a script that follows a data pipeline pattern. If you have a big research question, think about what the most modular progression of steps would be along the path from raw data to final output, and write small scripts that perform each step (input is the output from the previous step). Glue them all together with the data pipeline, which itself is a standalone, disposable script. If step N has already been run, then running the pipeline script once again shouldn't resubmit step N (as long as the input hasn't changed since the last run). This "intermediate data" approach is useful because we can check for errors each step on the way and we don't need to redo calculations if a particular step is shared by multiple research questions. I was taught this by a good mentor and I've been using this approach for many years for various ML projects and couldn't recommend it more highly.