Advice for improving the reproducibility of data extraction in meta‐analysis-Reference-Cited by-同舟云学术

Advice for improving the reproducibility of data extraction in meta‐analysis

Published:2023-08-11 Issue:6 Volume:14 Page:911-915
ISSN:1759-2879
Container-title:Research Synthesis Methods
language:en
Short-container-title:Research Synthesis Methods

Author:

Ivimey‐Cook Edward R.¹^ORCID,Noble Daniel W. A.²^ORCID,Nakagawa Shinichi³^ORCID,Lajeunesse Marc J.⁴^ORCID,Pick Joel L.⁵^ORCID

Affiliation:

1. School of Biodiversity, One Health and Veterinary Medicine University of Glasgow Glasgow UK

2. Division of Ecology and Evolution, Research School of Biology The Australian National University Canberra Australian Capital Territory Australia

3. Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences The University of New South Wales Sydney New South Wales Australia

4. Department of Integrative Biology University of South Florida Tampa Florida USA

5. Institute of Ecology and Evolution, University of Edinburgh Edinburgh UK

Abstract

AbstractExtracting data from studies is the norm in meta‐analyses, enabling researchers to generate effect sizes when raw data are otherwise not available. While there has been a general push for increased reproducibility in meta‐analysis, the transparency and reproducibility of the data extraction phase is still lagging behind. Unfortunately, there is little guidance of how to make this process more transparent and shareable. To address this, we provide several steps to help increase the reproducibility of data extraction in meta‐analysis. We also provide suggestions of R software that can further help with reproducible data policies: the shinyDigitise and juicr packages. Adopting the guiding principles listed here and using the appropriate software will provide a more transparent form of data extraction in meta‐analyses.

Publisher

Wiley

Subject

Education

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jrsm.1663

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