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Comment by breckuh | Hacker News Reader

This is an anecdote but my guess is a lot. I was a software engineer for a decade and recently transitioned to a bioinformatics job. A cursory look at many of the repos that supplement published papers (even in top journals like Nature), will show you that there are often no tests or type documentation of any kind and quite often you'll find bugs when you use packages with your own datasets. So to the OP I would recommend either teaching fundamentals first or concurrently--but don't skip fundamentals!

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To be clear though -- sometimes (often) this is not due to lack of education. The job of researchers is to produce papers and grants, not working software, and postdocs and grad students are junior researchers, not junior developers. It advances their careers to forgo the time to write tests (or, sigh, manually test other cases than their lab's data).

Big labs with large funding where funding sources allow hiring non-research full-time support staff as software engineers, places like the Broad, tend to have slightly better practices. But if you're using a package written by a graduate student.... his job is not to make yours easier. And since he's paid less than the guy who manages your local sandwich shop, maybe it's understandable.

There's a problem in science where we don't value scientists. Telling them "the world values software engineering" and turning them into software engineers is only going to make them go into industry faster.

But surely they (or most of them) value being able to trust their data?

I agree that we shouldn't ask them to be professional software developers, but then we need to provide them with access to professional developers if we want there to be such a thing as science that uses data and computing.

It's ridiculous to ask untrained people to write data processing scripts if you want to be able to rely on the output.

And then people wonder why we have a reproducibility crisis.

Yes, absolutely. It would make sense either for funding bodies to provide ways to fund contract developers or for even smaller institutes to be required to spend a portion of their budget on full-time developers (instead of spending it all on machines and student staff).

For the actual data science, as opposed to writing new software packages, it would be reasonable for students to maintain lab notebooks (except for computer experiments not wet lab ones), with exact versions of everything, exact steps, name, size and checksum of every dataset, and so on. I think things are definitely moving in that direction but this is a skill that is not beaten as much into grad students as recording wet lab work is. But in time it will be.

Though, my experience in wet labs is, both quality of the lab notebooks and quality of the labeling of the stuff in the fridge definitely varies.

This is why the push to open science is so important. Making data sets and code accessible for more in depth peer review should be required. Maybe it is a side effect of our antiquated publishing industry though.

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