A framework for improving the reproducibility of data extraction for meta-analysis

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.


Download Preprint


Edward Richard Ivimey-Cook, Daniel W.A. Noble, Shinichi Nakagawa, Marc J. Lajeunesse, Joel L Pick


Extracting 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 throughout the many facets of meta-analysis, the transparency and reproducibility of the data extraction phase are still lagging be-
hind. This particular meta-analytic facet is critical because it facilitates error-checking and enables users to update older meta-analyses. Unfortunately, there is little guidance of how to make the process of data extraction more transparent and shareable, in part this is as a result of relatively few data extraction tools currently offering such functionality. Here, we suggest a simple framework that aims to help increase the reproducibility of data extraction for meta-analysis. We also provide suggestions of software that can further help users adopt open data policies. More specifically, we overview two GUI style software in the R environment, shinyDigitise and juicr, that both facilitate reproducible workflows while reducing the need for coding skills in R. Adopting the guiding principles listed here and using appropriate software will provide a more streamlined, transparent, and shareable form of data extraction for meta-analyses.




Ecology and Evolutionary Biology, Life Sciences, Psychology, Research Methods in Life Sciences


meta-analysis, data extraction, shiny, metaDigitise, reproducibility, data extraction, metaDigitise, juicr, shinyDigitise


Published: 2022-12-14 01:29


CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

Additional Metadata

Conflict of interest statement:
The authors declare no conflict of interest.

Data and Code Availability Statement:
Data and code for the metaDigitise analysis can be found here: https://github.com/EIvimeyCook/DataExtraction_MS

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.