Recommendations to enhance rigor and reproducibility in biomedical research | Zendy

Jaqueline Brito | Zendy; Jun Z. Li | Zendy; Jason H. Moore | Zendy; Casey S. Greene | Zendy; Nicole Nogoy | Zendy; Lana X. Garmire | Zendy; Serghei Mangul | Zendy

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Recommendations to enhance rigor and reproducibility in biomedical research

Author(s) -

Jaqueline Brito,

Jun Z. Li,

Jason H. Moore,

Casey S. Greene,

Nicole Nogoy,

Lana X. Garmire,

Serghei Mangul

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa056

Subject(s) - documentation , transparency (behavior) , open science , computer science , rigour , usability , software , data science , software engineering , protocol (science) , engineering ethics , engineering , medicine , human–computer interaction , computer security , physics , geometry , programming language , mathematics , alternative medicine , pathology , astronomy

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.

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