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Open source is a hard requirement for reproducibility

R

Open source is a hard requirement for reproducibility.

No ifs nor buts. And I’m not only talking about the code you typed for your research paper/report/analysis. I’m talking about the whole ecosystem that you used to type your code.

(I won’t be talking about making the data available, because I think this is another blog post on its own.)

Is your code open? That’s good. But is it code for a proprietary program, like STATA, SAS or MATLAB? Then your project is not reproducible. It doesn’t matter if this code is well documented and written and available on Github. This project is not reproducible.

Why?

Because there is on way to re-execute your code with the exact same version of this proprietary program down the line. As I’m writing these lines, MATLAB, for example, is at version R2022b. And it is very unlikely that you can buy version, say, R2008a. Maybe you can. Maybe MATLAB offers this option. But maybe they don’t. And maybe if they do today, they won’t in the future. There’s no guarantee. And if you’re running old code written for version R2008a, there’s no guarantee that it will produce the exact same results on version 2022b. And let’s not even mention the toolboxes (if you’re not familiar with MATLAB’s toolboxes, they’re the equivalent of packages or libraries in other programming languages). These evolve as well, and there’s no guarantee that you can purchase older versions of said toolboxes. And also, is a project truly reproducible (even if old programs can be purchased) if it’s behind a paywall?

And let me be clear, what I’m describing here with MATLAB could also be said for any other proprietary programs still commonly (unfortunately) used in research and in statistics (like STATA or SAS).

Then there’s another problem: let’s suppose you’ve written a nice, thoroughly tested and documented script, and made it available on Github (and let’s even assume that the data is available for people to freely download, and that the paper is open access). Let’s assume further that you’ve used R or Python, or any other open source programming language. Could this study/analysis be said to be reproducible? Well, if the analysis ran on a proprietary operating system, then the conclusion is: your project is not reproducible.

This is because the operating system the code runs on can also influence the reproducibility of the project. There are some specificities in operating systems that may make certain things work differently. Admittedly, this is in practice rarely a problem, but it does happen, especially if you’re working with very high precision floating point arithmetic.

So where does that leave us? Basically, for something to be truly reproducible, it has to respect the following bullet points:

  • Source code must obviously to be available and thoroughly tested and document;
  • To be written with an open source programming language (nocode tools are by default non-reproducible and belong in the trash);
  • The project needs to be run on an open source operating system.
  • (Data and paper need obviously to be accessible as well)

And the whole thing would ideally be packaged using Docker or Podman. This means that someone could run an analysis in a single command, like:

docker run --rm --name my_analysis_container researchers_name/reproducible_project

Where reproducible_project is a Docker image, which would not only be based (very often) on the Ubuntu operating system (the most popular Linux distribution) but also contain, already installed and ready-to-use, the required programming language and the required libraries to run the project. Also, usually, the researcher would have added the required scripts and commands such that the command above, automatically, and without any further input, reruns the whole analysis. The entry cost to Docker (or similar tools) might seem high, but it is worth it, and the only way to have a truly 100% reproducible pipeline. If you’re using the R programming language for your analyses, you can use the pre-built Docker images from the amazing Rocker project. If you’re interested, I show how you can build a reproducible pipeline using these images in this chapter of my course I teach at university (as of writing this blog post, this chapter is not complete yet, but it will be by Sunday evening at the latest, as I have to teach this on Monday morning at the University).

Open source programming languages and libraries can be dockerized and the Docker images can be distributed. Maybe one day we will always have a Docker image alongside a research paper.

One can dream.

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