[Random_ernest]

I recall a story where a friend was unable to publish a paper in which he wrote an alternative to a very commonly used commercial tool (that virtually everybody used) with roughly 10 times better performance. He open sourced it and all, it was extremely useful, but there was no new methodology, it was simply very well implemented.

At a talk of his it lead to a very heated discussion where an older professor accused him of wasting government money on such nonsense.

[dr_zoidberg]

Been there. A few years back I got a government scholarship for my PhD (which is still in progress, due to my follow up work). I basically built the foundation upon which to establish a new field for my university, and the region where I live. There are some professor who think that scholarship (and the little money it gave me) was wasted on my because I chose to build all of that from the ground up, instead of rushing through my PhD.

By the way, those of that opinion are all professors who wanted me on their labs, but I turned them down...

[yig]

For every story like this, I believe there are many more in which the student simply writes their own implementation due to not invented here syndrome or engineering as a form of procrastination.

[V1ndaar]

If you talked to me about my PhD for a few minutes you would surely put me into your "had to reinvent the wheel for no reason" category.

As indeed, I wrote an analysis framework for my data (of a gaseous detector used for axion search) [0] instead of using an existing framework used by my predecessor. However, things are always more complicated than they seem. Many of those not talked about students who rewrite stuff probably have reasons!

In my case the existing framework [1] was a monster that was bent to allow it to work with the kind of data we have in the first place. In my case my detector had several additional features, which fit _even less_ into the existing framework. It would have been a hack and still a significant amount of work to make it work well.

To be fair, when I started this I expected it to be less work than it ended up being. But that's the story of software development.

The advantages now are significant of course. I know the whole codebase. It does exactly what I want. I can extend it easily as I see fit.

That doesn't mean I didn't also partly procrastinate writing software. Far from it. Hell, there was no reason to write a freaking plotting library (a sort of port of ggplot2 for Nim) [3]. But again, this means my thesis will have plots created natively using a TikZ backend while at the same time provide links to Vega-Lite plots for each and every plot in my thesis (which of course will include the data for each plot!).

Finally, the most important point: A university / professor who only pays me for 20h a week does not get to tell me how I do my PhD.

[0]: https://github.com/Vindaar/TimepixAnalysis [1]: https://ilcsoft.desy.de/portal/software_packages/marlintpc/ [2]: https://github.com/Vindaar/ggplotnim

[dr_zoidberg]

I certainly have experienced similar things, particularly been acused of reinventing wheels. Flexibility and performance are two big reasons, but also "it's fun" or "I want to understand X" also have a good weight when we do this kind of "useless reinvention".

[dr_zoidberg]

Maybe I wasn't clear enough. When I started my PhD, I was working on leading edge, basically 3 people in my country knew that we were talking about (and I was one of them). It certainly wasn't NIH-syndrome. Still, instead of "bailing out" on the easy path (present a paper here, work with that professor in That Other Thing That Doesn't Interest Me, etc) I chose to keep doing what I love.

End result so far? I'm quite respected, still one of the leading researchers in my country on my specific topic, but since I don't have a PhD (because of the aforementioned delays, and some grumpy professors actively pushing against me) I'm starting to lose access to grants and programs.

I'd still do it all again, but with a few tweaks here and there, you know hindsight always helping.

[coliveira]

Yes, it is easy to be sidetracked on writing software. Not that software is a bad thing, but research is something different.

[mratsim]

Given the current publish or perish culture, I doubt any student serious about publication can afford to waste time.

[coliveira]

It is obvious that we need good software, however from the point of view of science the old professor may have reason. If you are receiving a grant, you're not being paid to write software, in the same way that an engineer is not paid to write novels. As useful as the software may be, the person in question should be spending time on research (by definition new subjects), not writing again an existing software.

[ivirshup]

> If you are receiving a grant, you're not being paid to write software

In my (albeit limited) experience, software is a pretty common deliverable from a grant, at least in computational biology. This has also been my experience with more alternative funding sources like CZI and DARPA.

Taken more broadly, I think there is a huge disconnect between what academics are paid to do, and what takes most of their time. Review is unpaid. Grants are not dependent on which journal the results go into, but time could be saved by aiming lower. A salary can be payed from a research grant, while the investigator still has to teach.

[DiogenesKynikos]

What if that piece of software increases research output across the entire field? Often, a good piece of scientific software advances research more than what you're calling "research."

[kergonath]

Writing software is sometimes necessary to achieve the objectives of a grant (even though this is not necessarily explicit). It’s not writing software that’s a problem, it’s reinventing the wheel; you should not focus on “scientists should not write software”, because that is obviously far from the truth.

For a scientist, writing useful software is a good way to get exposure, build a reputation and get citations. It’s an opportunity to do some different kind of problem solving than usual. It’s also a way of understanding how the software really work (which assumptions are built in, which methods are used, and how does it affect the software’s results?). This does help improve the quality of subsequent results.

A grant typically (there are exceptions, of course) lists things that are going to be studied. How the studying is done is typically down to the people doing the work. It certainly isn’t for grumpy old professors who hear a talk at a conference to judge.