Two NumPy-related news items appeared on my Twitter feed yesterday, just a few days after I had accidentally started a somewhat heated debate myself concerning the poor reproducibility of Python-based computer-aided research.

It's hard to find an aspect of modern life that is not influenced in some way by software. Some of it is very visible, for example the Web browser I start on my computer. Other software is completely invisible, such as the software controlling my car's diesel engine. Some software is safety critical, for example flight control software in airplanes. Other software is used in a much more futile way, such as playing games.

A few days ago, I noticed this tweet in my timeline: That sounded like a good read for the weekend, which it was. The main argument the author makes is that C remains unsurpassed as a system integration language, because it permits interfacing with "alien" code, i.e. code written independently and perhaps even in different languages, down to assembly.

Over the last few years, I have repeated a little experiment: Have two scientists, or two teams of scientists, write code for the same task, described in plain English as it would appear in a paper, and then compare the results produced by the two programs. Each person/team was asked to do a maximum amount of verification and testing before comparing to the other person's/team's work.

A few years ago, I decided to adopt the practices of reproducible research as far as possible within the technical and social constraints I have to live with. So how reproducible is my published code over time? The example I have chosen for this reproducibility study is a 2013 paper about computing diffusion coefficients from molecular simulations. All code and data has been published as an ActivePaper on figshare.

In discussions about computational reproducibility (or replicability, or repeatability, according to the preference of each author), I often see the argument that reproducing computations may not be worth the investment in terms of human effort and computational resources. I think this argument misses the point of computational reproducibility. Obviously, there is no point in repeating a computation identically. The results will be the same.

Two currently much discussed issues in scientific computing are the sustainability of research software and the reproducibility of computer-aided research. I believe that the communities behind these two ideals should work together on taming their common enemy: software collapse. As a starting point, I propose an analysis of how the risk of collapse affects sustainability and reproducibility.

The importance of reproducibility in computer-aided research (and elsewhere) is by now widely recognized in the scientific community. Of course, a lot of work remains to be done before reproducibility can be considered the default. Doing computational research reproducibly must become easier, which requires in particular better support in computational tools. Incentives for working and publishing reproducibly must also be improved.

Think of all the things you hate about using computers in doing research. Software installation. Getting your colleagues' scripts to work on your machine. System updates that break your computational code. The multitude of file formats and the eternal need for conversion. That great library that's unfortunately written in the wrong language for you. Dependency and provenance tracking. Irreproducible computations.

Yesterday a blog post by Cyrille Rossant entitled "Moving away from HDF5" caught my eye. My own tendency at the moment is to use HDF5 more and more, so I was interested in why someone else would want to do the opposite. Here is my conclusion after reading his post, plus some ideas about where scientific data management is or should be heading in my opinion.