Best Practices for Scientific Computing
Greg Wilson, D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt, Davis, Richard T. Guy, Steven H. D. Haddock, Katy Huff, Ian M. Mitchell, Mark, Plumbley, Ben Waugh, Ethan P. White, Paul Wilson
TL;DR
This paper outlines best practices for scientific computing, emphasizing tools and methods that enhance code reliability, maintainability, and scientist productivity, based on research and practical experience.
Contribution
It introduces a comprehensive set of best practices for scientific software development grounded in research and experience, aiming to improve efficiency and reliability.
Findings
Adoption of best practices improves software reliability.
Using recommended tools reduces development effort.
Best practices enhance scientific reproducibility.
Abstract
Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software.
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