Recommendations to enhance rigor and reproducibility in biomedical research

被引：43

作者：

Brito, Jaqueline J. ^{[1
]}

Li, Jun ^{[2
]}

Moore, Jason H. ^{[3
]}

Greene, Casey S. ^{[4
,5
]}

Nogoy, Nicole A. ^{[6
]}

Garmire, Lana X. ^{[2
]}

Mangul, Serghei ^{[1
,7
]}

机构：

[1] Univ Southern Calif, Sch Pharm, Dept Clin Pharm, 1985 Zonal Ave, Los Angeles, CA 90089 USA

[2] Univ Michigan, Sch Med, Dept Computat Med & Bioinformat, 1301 Catherine St, Ann Arbor, MI 48109 USA

[3] Univ Penn, Inst Biomed Informat, Dept Biostat Epidemiol & Informat, 3700 Hamilton Walk, Philadelphia, PA 19104 USA

[4] Univ Penn, Perelman Sch Med, Dept Syst Pharmacol & Translat Therapeut, 3400 Civ Ctr Blvd, Philadelphia, PA 19104 USA

[5] Alexs Lemonade Stand, Childhood Canc Data Lab, 1429 Walnut St,Floor 10, Philadelphia, PA 19102 USA

[6] GigaScience, Shek Mun, 26-F,Kings Wing Plaza 2,1 Kwan St, Hong Kong, Peoples R China

[7] Univ Southern Calif, Quantitat & Computat Biol, Los Angeles, CA 90089 USA

来源：

GIGASCIENCE | 2020年 / 9卷 / 06期

关键词：

rigor; reproducible research; installability; archival stability; big data; open science; BIOINFORMATICS;

D O I：

10.1093/gigascience/giaa056

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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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