Provide Proactive Reproducible Analysis Transparency with Every Publication

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 co...

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Bibliographic Details
Published inarXiv.org
Main Authors Meijer, Paul, Howard, Nicole, Liang, Jessica, Kelsey, Autumn, Subramanian, Sathya, Johnson, Ed, Mariz, Paul, Harvey, James, Ambrose, Madeline, Tereshchenko, Vitalii, Beaubien, Aldan, Inala, Neelima, Aggoune, Yousef, Stark Pister, Vetto, Anne, Kinsey, Melissa, Bumol, Tom, Goldrath, Ananda, Li, Xiaojun, Torgerson, Troy, Skene, Peter, Okada, Lauren, Christian La France, Thomson, Zach, Lucas Graybuck
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.08.2024
Subjects
Data analysis
Data transparency
Reproducibility
Software
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Summary: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.
ISSN:2331-8422