This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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.
https://doi.org/10.32942/X2D30C
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
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
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