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Better together: Elements of successful scientific software development in a distributed collaborative community.

Authors :
Julia Koehler Leman
Brian D Weitzner
P Douglas Renfrew
Steven M Lewis
Rocco Moretti
Andrew M Watkins
Vikram Khipple Mulligan
Sergey Lyskov
Jared Adolf-Bryfogle
Jason W Labonte
Justyna Krys
RosettaCommons Consortium
Christopher Bystroff
William Schief
Dominik Gront
Ora Schueler-Furman
David Baker
Philip Bradley
Roland Dunbrack
Tanja Kortemme
Andrew Leaver-Fay
Charlie E M Strauss
Jens Meiler
Brian Kuhlman
Jeffrey J Gray
Richard Bonneau
Source :
PLoS Computational Biology, Vol 16, Iss 5, p e1007507 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
16
Issue :
5
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.39b3fd5bfd834abfb5347fc6af6789bc
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1007507