A core tenet of science is the ability to independently verify research results. When computations are involved, verifiability implies reproducibility: one should be able to re-run the computations to ensure they get the same results, at which point they may want to start experimenting with variants of the computational methods, feed it different data sets, and so on. This is the motivation behind our work on Guix: we want to empower scientists by providing a tool in support of reproducible computations and experimentation.

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A recent study published in Nature Scientific Data in February 2022 gives empirical insight into the success rate of reproducing R scripts obtained from Harvard’s Dataverse:

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Our attention was recently caught by a nice slide deck on the methods and tools for reproducible research in R. Among those, the talk mentions Guix, stating that it is “for professional, sensitive applications that require ultimate reproducibility”, which is “probably a bit overkill for Reproducible Research”. While we were flattered to see Guix suggested as a good tool for reproducibility, the very notion that there’s a kind of “reproducibility” that is “ultimate” and, essentially, impractical, is something that left us wondering: What kind of reproducibility do scientists need, if not the “ultimate” kind? Is “reproducibility” practical at all, or is it more of a horizon?

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This document is also available as PDF (printable booklet).

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This document is also available as PDF (printable booklet).

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We are pleased to announce Guix-Jupyter 0.2.1, a new release of our Guix-powered Jupyter kernel for self-contained and reproducible notebooks.

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Early this year, ReScience, which is concerned with publishing replications (successful or not) of previously-published articles, organized the Ten Years Reproducibility Challenge. The idea is simple: pick a paper of yours that is at least ten years old, and try to replicate its results. The first difficulty is usually to get the source code of the software used to produce the results and to get that code to build and run. This challenge helped highlight again ways in which research practices can and must be improved. We took it as an opportunity to devise new practices and tools to ensure reproducibility and provenance tracking for articles, end-to-end: from source code to PDF.

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This post is about reproducible computations, so let's start with a computation. A short, though rather uninteresting, C program is a good starting point. It computes π in three different ways:

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Jupyter Notebooks are becoming a key component of the researcher’s toolbox when it comes to sharing and reproducing computational experiments. Jupyter notebooks allow users to not only intermingle a narrative with supporting code in a way reminiscent of literate programming, they also make it easy to interact with the code and, thus, build on the work of each other.

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The book Evolutionary Genomics was published in July this year. Of particular interest to Guix-HPC is the chapter entitled “Scalable Workflows and Reproducible Data Analysis for Genomics”, by Francesco Strozzi et al.:

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GNU Guix can be used as a “package manager” to install and upgrade software packages as is familiar to GNU/Linux users, or as an environment manager, but it can also provision containers or virtual machines, and manage the operating system running on your machine.

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This document is also available as PDF (printable booklet).

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I’m happy to announce that the bioinformatics group at the Max Delbrück Center that I’m working with has released a preprint of a paper on reproducibility with the title Reproducible genomics analysis pipelines with GNU Guix.

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