A how-to guide for code sharing in biology

In 2024, all biology is computational biology. Computer-aided analysis continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery...

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Veröffentlicht in:PLoS biology Jg. 22; H. 9; S. e3002815
Hauptverfasser: Abdill, Richard J., Talarico, Emma, Grieneisen, Laura
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 10.09.2024
Schlagworte:
Best practice
Biologists
Biology
Computational biology
Computational Biology - methods
Computer applications
Computer programming
Digital Object Identifier
Humans
Information Dissemination - methods
Information management
Information sharing
Methods
Science
Scientists
Software
Technology application
United States
ISSN:1545-7885, 1544-9173, 1545-7885
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In 2024, all biology is computational biology. Computer-aided analysis continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery. It is currently much easier to find guidance for implementing these techniques than for reporting their use, leaving biologists to guess which details and files are relevant. In this essay, we review existing literature on the topic, summarize common tips, and link to additional resources for training. Following this overview, we then provide a set of recommendations for sharing code, with an eye toward guiding those who are comparatively new to applying open science principles to their computational work. Taken together, we provide a guide for biologists who seek to follow code sharing best practices but are unsure where to start.
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ISSN:1545-7885
1544-9173
1545-7885
DOI:10.1371/journal.pbio.3002815