Best Practices for Scientific Computing

This sounds like a gap in the curriculum that could be filled by grad students. I'm not in CS, but I know that the bioinformatics students have organized a course that covers some basics of the unix command line tools, probably because data conversion is a big part of most projects. There's also "Best practices for scientific computing" (http://arxiv.org/abs/1210.0530) that covers some of the same ground, and physics departments usually have a scientific computing course as well. Also, if it's really a one-off use of something, the department IT person could probably do it for you.

The article is more concerned with "open development" than with "scientific software". Those interested in more specifically science-oriented advice might like the following, which is referenced in the OP: http://arxiv.org/abs/1210.0530. For example, this one mentions version control and testing against data.

Recent version is v2 http://arxiv.org/abs/1210.0530v2 Summary: 1. Write programs for people, not computers. 2. Automate repetitive tasks. 3. Use the computer to record history. 4. Make incremental changes. 5. Use version control. 6. Don’t repeat yourself (or others). 7. Plan for mistakes. 8. Optimize software only after it works correctly. 9. Document design and purpose, not mechanics. 10. Conduct code reviews.