Guix-Jupyter 0.3.0 released!
Ludovic Courtès — November 14, 2024
We are pleased to announce Guix-Jupyter 0.3.0, a long-overdue release of our Guix-powered Jupyter kernel for self-contained and reproducible notebooks.
Supporting academic conference artifact evaluation
Emmanuel Agullo, Ludovic Courtès, Romain Garbage, Florent Pruvost, Philippe Swartvagher — July 26, 2024
Having promoted Guix as one of the tools to support reproducible research workflows, we are happy that it is now officially presented as one way to produce and review software artifacts that accompany articles submitted to SuperComputing 2024 (SC24), the leading HPC conference. In this post we look at what this entails and reflect on the role of reproducible software deployment on conference artifact evaluation.
Adventures on the quest for long-term reproducible deployment
Ludovic Courtès — March 13, 2024
Rebuilding software five years later, how hard can it be? It can’t be that hard, especially when you pride yourself on having a tool that can travel in time and that does a good job at ensuring reproducible builds, right?
Guix-HPC Activity Report, 2023
Céline Acary-Robert, Emmanuel Agullo, Ludovic Courtès, Marek Felšöci, Konrad Hinsen, Arun Isaac, Ontje Lünsdorf, Pjotr Prins, Simon Tournier, Philippe Virouleau, Ricardo Wurmus — February 16, 2024
This document is also available as PDF (printable booklet)
We are pleased to publish the sixth Guix-HPC annual report. Launched in 2017, Guix-HPC is a collaborative effort to bring reproducible software deployment to scientific workflows and high-performance computing (HPC). Guix-HPC builds upon the GNU Guix software deployment tool to empower HPC practitioners and scientists who need reliability, flexibility, and reproducibility; it aims to support Open Science and reproducible research.
A guide to reproducible research papers
Ludovic Courtès, Marek Felšöci, Konrad Hinsen, Philippe Swartvagher — June 23, 2023
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.
Guix-HPC Activity Report, 2022
Céline Acary-Robert, Ludovic Courtès, Yann Dupont, Marek Felšöci, Konrad Hinsen, Ontje Lünsdorf, Pjotr Prins, Philippe Swartvagher, Simon Tournier, Ricardo Wurmus — February 10, 2023
This document is also available as PDF (printable booklet).
CRAN, a practical example for being reproducible at large scale using GNU Guix
Lars-Dominik Braun — December 21, 2022
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:
Is reproducibility practical?
Ludovic Courtès — July 21, 2022
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?
Guix-HPC Activity Report, 2021
Pierre-Antoine Bouttier, Ludovic Courtès, Yann Dupont, Marek Felšöci, Felix Gruber, Konrad Hinsen, Arun Isaac, Pjotr Prins, Philippe Swartvagher, Simon Tournier, Ricardo Wurmus — February 3, 2022
This document is also available as PDF (printable booklet).
Guix-HPC Activity Report, 2020
Lars-Dominik Braun, Ludovic Courtès, Pjotr Prins, Simon Tournier, Ricardo Wurmus — February 9, 2021
This document is also available as PDF (printable booklet).
Guix-Jupyter 0.2.1 released!
Ludovic Courtès — January 25, 2021
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.
Reproducible research articles, from source code to PDF
Ludovic Courtès — June 16, 2020
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.
Guix-HPC Activity Report, 2019
Ludovic Courtès, Paul Garlick, Konrad Hinsen, Pjotr Prins, Ricardo Wurmus — February 17, 2020
This document is also available as PDF (printable booklet).
Reproducible computations with Guix
Konrad Hinsen — January 14, 2020
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:
Towards reproducible Jupyter notebooks
Ludovic Courtès — October 10, 2019
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.
Chapter of “Evolutionary Genomics” on workflow tools and Guix
Ludovic Courtès — September 9, 2019
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.:
Connecting reproducible deployment to a long-term source code archive
Ludovic Courtès — March 29, 2019
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.
Guix-HPC Activity Report, 2018
Eric Bavier, Ludovic Courtès, Paul Garlick, Pjotr Prins, Ricardo Wurmus — February 12, 2019
This document is also available as PDF (printable booklet).
PiGx paper awarded at the International Conference on Genomics (ICG-13)
Ricardo Wurmus — January 11, 2019
December 2018 the Akalin lab at the Berlin Institute of Medical Systems Biology (BIMSB) published a paper about a collection of reproducible genomics pipelines called PiGx that are made available through GNU Guix. The article was awarded third place in the GigaScience ICG-13 Prize. Representing the authors, Ricardo Wurmus was invited to present the work on PiGx and Guix in Shenzhen, China at ICG-13.
Ricardo urged the audience of wet lab scientists and bioinformaticians to apply the same rigorous standards of experimental design to experiments involving software: all variables need to be captured and constrained. To demonstrate that this does not need to be complicated, Ricardo reported the experiences of the Akalin lab in building a collection of reproducibly built automated genomics workflows using GNU Guix.
Due to technical difficulties the recording of the talk was lost, so Ricardo re-recorded the talk a few weeks later.
Paper on reproducible bioinformatics pipelines with Guix
Ricardo Wurmus — May 9, 2018
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.
