Best Practices for Scientific Computing

By D. Aruliah, C. T. Brown, Neil Philippe Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, K. Huff, Ian M. Mitchell, Mark D. Plumbley, Ben Waugh and 3 more. 2012

697 citations recorded by the source corpus.

We describe a set of best practices for scientific software development, based on research and experience, that will improve scientists' productivity and the reliability of their software.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Szilárd Páll
2. Tom J. Pollard
3. Matthew Brett
4. Jean-Baptiste Poline
5. Ross Barnowski
6. K. Jarrod Millman
7. Markku V. J. Maula
8. W. Stam

In Computer Vision and Pattern Recognition

1. Jean-Baptiste Poline
2. Matthew Brett
3. R. Mark
4. Li-wei H. Lehman
5. Matthew P. Lungren
6. Jimeng Sun
7. Fei Wang
8. Mihaela van der Schaar

In Information Systems and Management

1. Matthew Brett
2. K. Jarrod Millman
3. Jean-Baptiste Poline
4. Ross Barnowski
5. Markku V. J. Maula
6. W. Stam

Citations in the corpus

• Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “Additionally, similar to software programming, errors can occur easily (Wilson et al., 2014).”
• Influence apart from Adoption: How Interaction between Programming and Scientific Practices Shapes Modes of Inquiry in Four Oceanography Teams (2016, background): “, [70] is a particularly detailed list of such adaptations).”
• MIMIC-IV, a freely accessible electronic health record dataset (2023, methodology): “The creation of MIMIC-IV followed best practices in scientific computing 23 .”
• The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package (2020, background): “…a community-led, free, and open source38 core Python package (sunpy), supports an ecosystem of affiliated packages (see Section 6) consistent with best practices (Wilson et al. 2014), and engages with the community through mailing lists, chat rooms, tutorials, summer programs, and mentorship.”
• Teaching Computational Reproducibility for Neuroimaging (2018, background): “The best known may be Software Carpentry (Wilson, 2014).”
• Good enough practices in scientific computing (2016, background): “Two years ago a group of researchers involved in Software Carpentry(1) and Data Carpentry(2) wrote a paper called “Best Practices for Scientific Computing” [1].”
• The Spack package manager: bringing order to HPC software chaos (2015, background): “Unfortunately, building scientific software is notoriously complex, with immature build systems that are difficult to adapt to new machines [13, 23, 42].”
• Developing Open-Source Scientific Practice * (2018, background): “For a more detailed discussion, we recommend [17, 13, 1].”