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. 2025 Mar 5;12(3):241936.
doi: 10.1098/rsos.241936. eCollection 2025 Mar.

Provide proactive reproducible analysis transparency with every publication

Paul Meijer  1 , Nicole Howard  1 , Jessica Liang  1 , Autumn Kelsey  1 , Sathya Subramanian  1 , Ed Johnson  1 , Paul Mariz  1 , James Harvey  1 , Madeline Ambrose  1 , Vitalii Tereshchenko  1 , Aldan Beaubien  1 , Neelima Inala  1 , Yousef Aggoune  1 , Stark Pister  1 , Anne Vetto  1 , Melissa Kinsey  1 , Tom Bumol  1 , Ananda Goldrath  1 , Xiaojun Li  1 , Troy Torgerson  1 , Peter Skene  1 , Lauren Okada  1 , Christian La France  1 , Zach Thomson  1 , Lucas Graybuck  1
Affiliations

Provide proactive reproducible analysis transparency with every publication

Paul Meijer et al. R Soc Open Sci. .

Abstract

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large datasets, a granular understanding of the analysis methodology is an essential component of reproducibility. This article discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.

Keywords: data analysis; equity in science; immunology; life sciences; open science; reproducibility crisis.

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Conflict of interest statement

We declare we have no competing interests.

Figures

The full provenance of an analysis.
Figure 1.
The full provenance of an analysis.
An example of subject metadata associated with a dataset.
Figure 2.
An example of subject metadata associated with a dataset.
An example of structural metadata for a transformation step.
Figure 3.
An example of structural metadata for a transformation step.
An example of a partial trace of a set of related processes.
Figure 4.
An example of a partial trace of a set of related processes.
See this image and copyright information in PMC

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