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 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.
Authors
- Tom Bumol
- Vitalii Tereshchenko
- Paul Meijer (ORCID: https://orcid.org/0000-0001-5535-0399)
- Lauren Okada (ORCID: https://orcid.org/0000-0002-5691-3373)
- Nicole Howard
- Aldan Beaubien
- Ananda W. Goldrath (ORCID: https://orcid.org/0000-0002-5900-508X)
- Troy R. Torgerson (ORCID: https://orcid.org/0000-0003-3489-5036)
- Peter J. Skene (ORCID: https://orcid.org/0000-0001-8965-5326)
- Lucas T. Graybuck (ORCID: https://orcid.org/0000-0002-8814-6818)
- Ed Johnson
- Xiaojun Li (ORCID: https://orcid.org/0000-0003-0832-9479)
- Neelima Inala
- Yousef Aggoune
- Stark Pister
- Anne Vetto
- Christian La France
- Zach Thomson
- Paul Mariz
- Madeline Ambrose
- Sathya Subramanian
- James Harvey
- Melissa Kinsey
- Jessica Liang
- Autumn Kelsey
Institutions
- Allen Institute for Immunology (US)
Publication Details
- Journal
- Royal Society Open Science
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1098/rsos.241936
- Citations
- 3
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 12.51