In my experience it's tough to sell to some scientists (they like to work with R and Python here), but when it's tied with pipelines that ultimately publish materials (rather than every step of the way), it's extremely helpful and streamlines the QA process of ensuring correct data is packaged with publications.

When I've audited some of the published data at our org, there are errors that would have been caught with even basic type-safety. That's how I got the green light to start harassing my team with type safety in our pipelines.

Of course, as with all things in programming, it isn't a silver bullet. It adds a layer of rigor that can slow things down, and there are often (seemingly always) nuances which can't be caught easily by most type systems. Things like complex relations between values (like 'if in Y is in [range], X must be null, and Z must be one of [a, b, c]'). Even so, eliminating categories of errors is worthwhile, and makes it easier to focus on the more complex challenges.

Over all I'd agree though, it's a good combination.