[piva00]

From what I see helping some friends not well versed with programming doing their PhDs, FORTRAN still has a lot of penetration with scientists. Lots of climate models are FORTRAN code.

Also, through helping them I can see the dire state of scientific code, it's a mess, if departments had the budget to employ 1-2 professional programmers to help them mentor staff it could be extremely helpful for science code to be more easily shared and reasoned about, some of the code I've seen is basically throw away code after the contributors aren't around anymore...

[btrettel]

I get the impression that you think that Fortran is a bad language for science.

Modern Fortran is a nice language for many scientific computing tasks. For me, I like Fortran because it's basically the easiest statically-typed compiled language (I rarely have to think about pointers, for instance), it's basically as fast as C, it has some good features for my variety of scientific computing (arrays in particular), and it has many compilers. Certainly, a better language could be designed without the legacy baggage and some features I'd like (better generics in particular), but I haven't seen anything better yet myself.

Now, legacy FORTRAN (note caps) is often a mess. It wasn't until the Fortran 90 standard that the capitalization changed, and the language changed a lot with that standard as well. I suspect the issues that you're seeing are more from many scientists not modernizing, and not from Fortran in itself.

[piva00]

No, not at all, I didn't want to imply that, simply noted that Fortran is still used a lot in science and that scientists would greatly benefit from programmers to mentor them on how to write code in whatever language in a better way.

[aragilar]

https://software-carpentry.org/ has been around for over 10 years.

Also, software engineers without the requisite background are usually worse for a project (and for teaching researchers) than anyone else.

[mike_hearn]

A lot of scientific code is worse than a mess, it outright doesn't work and yields incorrect results, a problem which is then routinely covered up. I've seen this first hand :( The worst part is that universities don't realize how much they don't know. On the rare occasions outsiders notice what's going on they are faced with a wall of baffling excuses and justifications for why so much doesn't work, like "if it didn't crash it must be correct" or "scientists don't need unit tests, we just look at the results and know they are right because we're experts". Academics are happy to pronounce that professional coders can't judge the correctness of their work and do so loudly and publicly.

As for money, well departments do have the budgets to hire developers. Science funding is in the high billions in most western countries. The problem is not departmental budgets, it's a social problem. Universities love the practice of spreading money amongst as many professors and tiny departments as possible in order to lay claim to every possible area of human knowledge/experience. Combine that with a culture of low standards and coverups and you've got a recipe for disasters (e.g. invariably critical bug fixes that corrupt data are described as not affecting the final results even when that clearly can't be true).

[abyssin]

I have the same experience in a psychology faculty. I helped a PhD student who was working on some MATLAB code. The amount of code I had to write for something similar as they were doing was orders of magnitude lower, and faster to write. But they spent months hurting their brains and therefor thought they were doing something really smart. There was a lot of micromanagement of clueless researchers, and the consequence is that everything had to be done the exact same way as the more experienced members of the lab did previously. This was the most wasteful work organization I've seen in my life, but there was little awareness of it, compared to other state-subsidized projects.

[abought]

Some departments are starting to hire programmers. There's an effort to define this as a (broad) job category under the heading "Research Software Engineer":

https://society-rse.org/ https://us-rse.org/

Institutional support varies widely; some projects or teams are rather well funded for big projects and senior talent, while at other schools, the cost structure is more aimed at "one off" projects staffed by more recent graduates.

A recent grant is trying to fund this work at several schools with a history of well organized services: https://www.schmidtfutures.org/our-work-old/virtual-institut...

[mike_hearn]

That's been around for a while but the pay for these roles is very uncompetitive, and universities don't care if professors don't do it. It doesn't make any difference to whether you get published or not so it'll probably remain niche.

Now, if journals start rejecting papers because the code isn't provided or because it was provided but professional software engineers reviewed it and gave it a thumbs down, that'd change. But journals struggle to keep obviously AI generated material out of their pages, so they aren't going to do anything like that.

[abought]

It depends. Some of the foundation-funded positions are competitive, and a few centers have surprisingly professional leadership. People are trying to organize, and- even if it's an uphill climb- there's been some improvement at the edges.

Anecdotally, some of the RSE leads I've spoken to are seeing more long-term demand than they predicted, which might lead to more room for senior roles. Currently quite a few teams (outside the big centers) seem to be priced way too low, usually explained as because they're testing the waters.... so "cheap student labor" and "one off project" is what they can afford.

Minor heretical aside: one thing I miss about old twitter is that academia was developing a real "second layer" on top of journals, where things like reproducibility could be discussed publicly. PubPeer is a partial solution, as are GitHub issues... if enough gatekeepy people really see value in code quality, norms will shift with or without mandates.

[mike_hearn]

What's different about current Twitter/X in that regard? I see reproducibility and other science problems discussed there sometimes.

[abought]

A lot of the audience fragmented for various reasons, and even within the same discipline, not everyone has converged in a new place yet. I'm told mastodon got more CS/phys science people, and Bluesky got more social scientists. That's a shifting landscape though.

There was one obvious place to look for these discussions; now there are many. Changes to search tools and API access didn't help discoverability either.

Some of the departures are for practical reasons: Twitter regularly changes the rules around logged-in viewing, direct links, and promotion/ordering of posts in ways that create friction for people trying to engage in public outreach. ("this method worked yesterday" should refer to the software, not the communication, thank you very much!)

[DonHopkins]

Possibly something about the way women and blacks and jews and gays and trans people and other minorities are being regularly and systematically and reproducibly attacked and harassed by neo-nazis and MAGA trolls who Elon Musk retweets and supports instead of discourages and bans that turns academics off from Twitter/X.

[jltsiren]

Research software engineers are paid what they are worth. Typically a bit more than a postdoc, but not that much more. The problem is that the academia is a cash-starved environment, and everyone's work is worth much less than similar work in the industry.

Another problem is the PI-centric model. Most of the funding goes to individual labs. If a typical grant is $200k/year, you are not going to pay competitive salaries. And you're probably not going to hire a software engineer, because then you won't have anyone doing the actual research.

[abought]

I think that's the rationale behind organizing separate teams as service organizations (mini contractors who serve multiple PIs).

Other option is research "centers" with multiple PIs. Not an option for most fields, but a few, like bioinformatics, can justify both the cost and the shared employee.

[aragilar]

Uh, why would "profession coders" be able to check the work? Given "floating point is weird" articles appear here regularly, and the comments show widespread misconceptions about how they work, I'm not sure adding random software engineers to the process would help in some way.

Also, most research funding is tied to specific projects, and the amounts tend to be quite small.

[mike_hearn]

The buggy model I reviewed had, amongst other problems, floating point non-determinisms. The average software dev is going to be better at this stuff than someone who only learned enough to make some numbers appear on screen, and are those numbers right? Well, hard to say, probably doesn't really matter.

[konradha]

There is project bringing Fortran to the browser, though (_using WebAssembly_)

https://dev.lfortran.org/