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Computationally reproducing results from meta-analyses in ecology and evolutionary biology using shared code and data

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  • Steven Kambouris
  • David P Wilkinson
  • Eden T Smith
  • Fiona Fidler

Abstract

Many journals in ecology and evolutionary biology encourage or require authors to make their data and code available alongside articles. In this study we investigated how often this data and code could be used together, when both were available, to computationally reproduce results published in articles. We surveyed the data and code sharing practices of 177 meta-analyses published in ecology and evolutionary biology journals published between 2015–17: 60% of articles shared data only, 1% shared code only, and 15% shared both data and code. In each of the articles which had shared both (n = 26), we selected a target result and attempted to reproduce it. Using the shared data and code files, we successfully reproduced the targeted results in 27–73% of the 26 articles, depending on the stringency of the criteria applied for a successful reproduction. The results from this sample of meta-analyses in the 2015–17 literature can provide a benchmark for future meta-research studies gauging the computational reproducibility of published research in ecology and evolutionary biology.

Suggested Citation

  • Steven Kambouris & David P Wilkinson & Eden T Smith & Fiona Fidler, 2024. "Computationally reproducing results from meta-analyses in ecology and evolutionary biology using shared code and data," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0300333
    DOI: 10.1371/journal.pone.0300333
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    References listed on IDEAS

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    1. Malika Ihle & Isabel S. Winney & Anna Krystalli & Michael Croucher, 2017. "Striving for transparent and credible research: practical guidelines for behavioral ecologists," Behavioral Ecology, International Society for Behavioral Ecology, vol. 28(2), pages 348-354.
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