Recommendations to enhance rigor and reproducibility in biomedical research

Jaqueline J Brito; Jun Li; Jason H Moore; Casey S Greene; Nicole A Nogoy; Lana X Garmire; Serghei Mangul

doi:10.1093/gigascience/giaa056

Recommendations to enhance rigor and reproducibility in biomedical research

Gigascience. 2020 Jun 1;9(6):giaa056. doi: 10.1093/gigascience/giaa056.

Authors

Jaqueline J Brito¹, Jun Li², Jason H Moore³, Casey S Greene^{4

5}, Nicole A Nogoy⁶, Lana X Garmire², Serghei Mangul^{1

7}

Affiliations

¹ Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, 1985 Zonal Avenue, Los Angeles, CA 90089, USA.
² Department of Computational Medicine & Bioinformatics, Medical School, University of Michigan, 1301 Catherine Street, Ann Arbor, MI 48109, USA.
³ Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
⁴ Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA.
⁵ Childhood Cancer Data Lab, Alex's Lemonade Stand, 1429 Walnut St, Floor 10, Philadelphia, PA 19102, USA.
⁶ GigaScience, 26/F, Kings Wing Plaza 2, 1 On Kwan Street, Shek Mun, N.T., Hong Kong.
⁷ Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.

Abstract

Biomedical research depends increasingly on computational tools, but mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present software for which source code or documentation are or become unavailable; this compromises the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit subsequent work. We provide 8 recommendations to improve reproducibility, transparency, and rigor in computational biology-precisely the values that should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in life science research.

Keywords: archival stability; big data; installability; open science; reproducible research; rigor.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Biomedical Research / standards*
Computational Biology
Data Accuracy
Humans
Reproducibility of Results
Software

Abstract

Publication types

MeSH terms

Grants and funding