Computationally reproducing results from meta-analyses in Ecology and Evolutionary Biology using shared code and data

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Authors

Steven Kambouris , David Peter Wilkinson , Eden T. Smith, Fiona Fidler

Abstract

The rates at which journal articles in ecology and evolutionary biology make data and code available have been studied previously. This study examines how often this data and code, when available, can be used to computationally reproduce results published in articles. This study surveys the data and code sharing practices of 177 meta-analyses published in ecology and evolutionary biology journals published over 2015-17. 26 articles (15%) were found to have obtainable data and code files. Results from these articles were targeted for computational reproduction using the data and code files obtained. Overall, from the sample of 177 articles, 4-13% of articles could be successfully reproduced, depending on the stringency of the criteria applied for a successful reproduction. The low overall success rate was primarily driven by the low rate of code sharing.

DOI

https://doi.org/10.32942/X2X602

Subjects

Ecology and Evolutionary Biology

Keywords

computational reproducibility, meta-analysis, data sharing, code sharing

Dates

Published: 2023-07-04 15:05

License

CC BY Attribution 4.0 International

Additional Metadata

Language:
English

Conflict of interest statement:
None.

Data and Code Availability Statement:
The data and code files to reproduce all results reported in this article are available on Zenodo at https://doi.org/10.5281/zenodo.8114702.