Code sharing in ecology and evolution increases citation rates but remains uncommon

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  • Brian Maitner
  • Paul Efren Santos Andrade
  • Luna Lei
  • Jamie Kass
  • Owens, Hannah Lois
  • George C. G. Barbosa
  • Brad Boyle
  • Matiss Castorena
  • Brian J. Enquist
  • Xiao Feng
  • Daniel S. Park
  • Andrea Paz
  • Gonzalo Pinilla-Buitrago
  • Cory Merow
  • Adam Wilson

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low-impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open-access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation.

Original languageEnglish
Article numbere70030
JournalEcology and Evolution
Volume14
Issue number8
Number of pages9
ISSN2045-7758
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.

    Research areas

  • code sharing, open access, open data, open science, R software, reproducibility

ID: 403323131