Mathematicians tend to view their work as somewhat Bohemian endeavor, driven by inspiration and imagination and not by process or organization. At the same time, the research enterprise in sciences and engineering is far more often perceived as a structured team effort, thoroughly documented, perspirational rather than inspirational.
This comparison is true only to some degree – perhaps less than we use to think: our discipline depends on a rigorous (one might say, tedious) process of verification and social acceptance of a result, we work increasingly often in teams, and schedule our research efforts around our teaching or travel engagements… With all the differences, mathematical research is not as far from the general research practices as we might believe.
Still, to someone trained in mathematics, experiencing the daily practices of a scientific or engineering lab might feel a cultural shock. A well run lab operates as a machine, with lots of rules and practices woven into the daily routine.
The good news is that these rules, by and large, make a lot of sense. They eliminate (a fraction of) noise, waste and confusion, and open room for creativity and imagination. Learning and adopting them make life easier, and research better.
Below are some of the best practices relevant to PI4 where most of the internships will deal in some degree with scientific computing. They are borrowed from a PLoS article Best Practices for Scientific Computing (thanks to David L. for pointing it to me).
Summary of Best Practices
- Write programs for people, not computers.
- A program should not require its readers to hold more than a handful of facts in memory at once.
- Make names consistent, distinctive, and meaningful.
- Make code style and formatting consistent.
- Let the computer do the work.
- Make the computer repeat tasks.
- Save recent commands in a file for re-use.
- Use a build tool to automate workflows.
- Make incremental changes.
- Work in small steps with frequent feedback and course correction.
- Use a version control system.
- Put everything that has been created manually in version control.
- Don’t repeat yourself (or others).
- Every piece of data must have a single authoritative representation in the system.
- Modularize code rather than copying and pasting.
- Re-use code instead of rewriting it.
- Plan for mistakes.
- Add assertions to programs to check their operation.
- Use an off-the-shelf unit testing library.
- Turn bugs into test cases.
- Use a symbolic debugger.
- Optimize software only after it works correctly.
- Use a profiler to identify bottlenecks.
- Write code in the highest-level language possible.
- Document design and purpose, not mechanics.
- Document interfaces and reasons, not implementations.
- Refactor code in preference to explaining how it works.
- Embed the documentation for a piece of software in that software.
- Use pre-merge code reviews.
- Use pair programming when bringing someone new up to speed and when tackling particularly tricky problems.
- Use an issue tracking tool.