Provide Proactive Reproducible Analysis Transparency with Every Publication

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

arXiv · 2408.09103

Provide Proactive Reproducible Analysis Transparency with Every Publication

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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 data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper 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.

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Paul Meijer, Nicole Howard, Jessica Liang, Autumn Kelsey, Sathya Subramanian, Ed Johnson, Paul Mariz, James Harvey, Madeline Ambrose, Vitalii Tereshchenko, Aldan Beaubien, Neelima Inala, Yousef Aggoune, Stark Pister, Anne Vetto, Melissa Kinsey, Tom Bumol, Ananda Goldrath, Xiaojun Li, Troy Torgerson, Peter Skene, Lauren Okada, Christian La France, Zach Thomson, Lucas Graybuck. 2024-08-17. Provide Proactive Reproducible Analysis Transparency with Every Publication. https://arxiv.org/abs/2408.09103

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