Toward practical transparent verifiable and long-term reproducible research using Guix - Scientific Data

Reproducibility crisis urge scientists to promote transparency which allows peers to draw same conclusions after performing identical steps from hypothesis to results. Growing resources are developed to open the access to methods, data and source codes. Still, the computational environment, an interface between data and source code running analyses, is not addressed. Environments are usually described with software and library names associated with version labels or provided as an opaque container image. This is not enough to describe the complexity of the dependencies on which they rely to operate on. We describe this issue and illustrate how open tools like Guix can be used by any scientist to share their environment and allow peers to reproduce it. Some steps of research might not be fully reproducible, but at least, transparency for computation is technically addressable. These tools should be considered by scientists willing to promote transparency and open science.

Large Scale Data Preprocessing with xcms

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