Mangul Lab at USC has published our fourth paper in GigaScience, which presents our group’s review of efforts to improve scientific rigor and reproducibility via open-source data and academic software.
Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open data, open software, and reproducibility are variably enforced. Today’s researcher often encounters peer-reviewed publications that describe software for which source code is unavailable, documentation is incomplete or unmaintained, and analytical source code is missing. Lack of this information limits the role of peer review in evaluating technical strength and scientific contribution, and limits any subsequent work that intends to use the described software.
To address the growing need for systematic archiving of software developed for the academic community, Jaqueline J. Brito (Postdoctoral researcher, Mangul Lab at USC), Jun Li (Professor, Computational Medicine and Bioinformatics, University of Michigan), Jason H. Moore (Director, Penn Institute for Biomedical Informatics), Casey S. Greene (Associate Professor, Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania), Nicole A. Nogoy (Editor, GigaScience), and Lana X. Garmire (Associate Professor, Computational Medicine and Bioinformatics, University of Michigan) developed recommendations to improve reproducibility, transparency, and rigor in computational biology—precisely the values which should be emphasized in foundational life and medical science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in biomedicine and life science research.
The good news is that the infrastructure required to systematically adopt best practices for reproducibility of biomedical research is largely already in place! The remaining challenge to the systematic promotion of scientific reproducibility is that incentives are not currently aligned to support good practices. We will integrate these effective practices for enabling reproducible research in the curriculum of the School of Pharmacy.