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Towards readable code in neuroscience

Nature Reviews Neuroscience volume 22, pages 257–258 (2021)Cite this article

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Code has become central to neuroscience, and the neuroscience community must take steps to ensure its reproducibility and best coding practices. Improving code readability benefits individual researchers and the wider neuroscience community.

Scientific research requires a diverse set of skills, including performing statistical analyses, writing manuscripts and, increasingly, developing software for experiment design, testing theories and processing data sets. A 2014 UK survey reported that 69% of researchers consider software essential for their work and that 56% develop their own software tools1. Particularly in neuroscience, the quantity and complexity of data often make automated processing tools essential for analysis. Most data published today have been processed by software, including that used in imaging pipelines, for sorting recorded neuronal spikes and for model simulations. As of January 2021, about 1 in 5 PhD or postdoctoral positions on the online FENS (Federation of European Neuroscience Societies) Job Market explicitly reference ‘Python’, ‘MATLAB’ or ‘programming’. Therefore, ensuring that neuroscientists follow good coding practices is becoming increasingly important.

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Acknowledgements

The authors gratefully acknowledge S. Eglen, J. Kirchner and G. Laurent for helpful feedback.

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Authors and Affiliations

  1. Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany

    Juan Luis Riquelme & Julijana Gjorgjieva

  2. School of Life Sciences, Technical University of Munich, Freising, Germany

    Juan Luis Riquelme & Julijana Gjorgjieva

Authors
  1. Juan Luis Riquelme
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  2. Julijana Gjorgjieva
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Correspondence to Julijana Gjorgjieva.

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The authors declare no competing interests.

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Related links

FENS (Federation of European Neuroscience Societies) Job Market: https://www.fens.org/News-Activities/Jobs/?pid=509%7C508&key=python%7Cmatlab%7Cprogramming

Git: https://git-scm.com/

Guide for reproducible research: https://the-turing-way.netlify.app/reproducible-research/reproducible-research.html

How to cite and describe software: https://www.software.ac.uk/how-cite-software

Jupyter: https://jupyter.org/

Nature journals’ reporting standards and availability: https://www.nature.com/nature-research/editorial-policies/reporting-standards

NEST: https://www.nest-simulator.org/publications/index.php

Research software engineers: https://researchsoftware.org/

The Carpentries: https://carpentries.org/

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Riquelme, J.L., Gjorgjieva, J. Towards readable code in neuroscience. Nat Rev Neurosci 22, 257–258 (2021). https://doi.org/10.1038/s41583-021-00450-y

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