Two weeks ago, on June 27th, we held an second on-line hackathon on reproducible research issues. This hackathon was a collaborative effort to bring GNU Guix to concrete examples inspired by contributions to the online journal ReScience C.
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.
It's time to run the second Reproducible Research hackathon! The first one was from... 2020, already! The date: Tuesday June, 27th. Start: 9h30 (CEST) End: 17h30.
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:
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?
In modern science, analysis is required to process data. When the data-flow is linear, such a process is easily represented by tools such as the standard Unix pipeline. However, this data-flow is often modeled by a directed graph: each processing node may have one or more inputs and the outputs may be directed to different processing nodes. This directed graph, mainly used in the fields of bioinformatics, medical imaging and astronomy, among many others, is called a workflow.
We like to say that Docker images are like smoothies: you can immediately tell whether it’s your liking, but you can hardly guess what the ingredients are. Although containers are an efficient way to ship things, the core question is how these things are produced.
There is no shortage of package managers. Each tool makes its own set of tradeoffs regarding speed, ease of use, customizability, and reproducibility. Guix occupies a sweet spot, providing reproducibility by design as pioneered by Nix, package customization à la Spack from the command line, the ability to create container images without hassle, and more.
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.
Last week, on July 3rd, we held an on-line hackathon on reproducible research issues. This hackathon was a collaborative effort to bring GNU Guix to concrete examples inspired by to contributions the recent Ten Years Reproducibility Challenge organized by ReScience.
Several submissions to the recent Ten Years Reproducibility Challenge organized by ReScience took advantage of GNU Guix, as discussed earlier.
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.
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:
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.
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.:
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.
In the quest for truly reproducible workflows I set out to create an example of a reproducible workflow using GNU Guix, IPFS, and CWL. GNU Guix provides content-addressable, reproducible, and verifiable software deployment. IPFS provides content-addressable storage, and CWL describes workflows that can run on specifically supported backend hardware system. In principle, this combination of tools should be enough to provide reproducibility with provenance and improved security.
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.
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.
Guix is a good fit for multi-user environments such as clusters:
it
allows non-root users to install packages at will without interfering with each other.
However, a common complaint is that installing Guix requires administrator
privileges. More precisely, guix-daemon, the system-wide daemon that
spawns package builds and downloads on behalf of
users,
must be running as root.
This is not much of a problem on one's laptop but it surely makes it
harder to adopt Guix on an HPC cluster.
This post marks the debut of Guix-HPC, an effort to optimize GNU Guix for reproducible scientific workflows in high-performance computing (HPC). Guix-HPC is a joint effort between Inria, the Max Delbrück Center for Molecular Medicine (MDC), and the Utrecht Bioinformatics Center (UBC). Ludovic Courtès, Ricardo Wurmus, Roel Janssen, and Pjotr Prins are driving the effort in each of these institutes, each one focusing specific areas of interest within this overall Guix-HPC effort. Our institutes have in common that they are users of HPC, and that, as scientific research institutes, they have an interest in using reproducible methodologies to carry out their research.
