It does happen, though. C has mostly replaced FORTRAN for scientific applications. Not entirely, FORTRAN is (infamously) still used, but I don't know anyone who has started a new project with FORTRAN.

Just 6 years ago, I was taught Perl in my Introduction to Bioinformatics course. The teachers were still using Perl because it used to be the go-to language for bioinformaticians. The year after, and every year since, they've taught using Python.

We started a very large computational intensive project in Fortran. It is still easier to do maths in Fortran than in C/C++ and now, Fortran has a wonderful C binding system allowing direct call into C .so/.dll if you want to do some SQL or other kind of data input/output.

The idea that people were being forced to learn perl just for bioinfomatics as recently as 6 years ago fascinates me.

Python has gotten exceptionally lucky. I am sure the two or three remaining perl users on the planet are also on HN and ready to jump to its defense, but to me this just goes to show you how heavy the switching cost is for something like this is and also how lucky python was to have been the best language to switch to at this point. It was in the right place at the right time for a lot of these switches away from older languages in obvious decline and then it was able to leverage numpy and scikit to pick up a lot of additional momentum in ML and data science tasks. It is almost never the 'best' language for the job, but coming in as second choice on most tasks is a huge win.

Jack of all trades, master of none, but oftentimes better than some are at one.

There are many new projects in modern Fortran. Just search GitHub.

People who use a version of Fortran later than FORTRAN 77 do not spell it in all upper case letters.