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Computational biology continues to spread into new fields, becoming moreaccessible to researchers trained in the wet lab who are eager to takeadvantage of growing datasets, falling costs, and novel assays that present newopportunities for discovery even outside of the much-discussed developments inartificial intelligence. However, guidance for implementing these techniques ismuch easier to find than guidance for reporting their use, leaving biologiststo guess which details and files are relevant. Here, we provide a set ofrecommendations for sharing code, with an eye toward guiding those who arecomparatively new to applying open science principles to their computationalwork. Additionally, we review existing literature on the topic, summarize themost common tips, and evaluate the code-sharing policies of the mostinfluential journals in biology, which occasionally encourage code-sharing butseldom require it. Taken together, we provide a user manual for biologists whoseek to follow code-sharing best practices but are unsure where to start.

A how-to guide for code-sharing in biology