Mini-guide to reproducible Python code
A lot of modern research requires custom software to be written, either to do some calculations, analyse experimental data or something else. Creating good quality, sustainable software is always desirable, but ticking all the boxes that are often described as necessary to accomplish this can be a daunting task for people - researchers - who often have other priorities in mind.
Reproducibility is, however, not an optional feature of a piece of research - including software or otherwise - and that is something that researchers are fully responsible for addressing. Luckily, out of the many requirements of good quality and sustainable software, only a handful are necessary, or can go a long way, to support the reproducibility of the results.
In this post we describe these absolutely essential steps that researchers should take in order to support the reproducibility of their software. The recommendations in this blog post are for software developed using Python. It might not apply to all cases, and it is not fool proof as reproducibility is a really complex business, but it is a good start and will narrow the chances of things going wrong when other people try to use the software.