Reproducible, flexible and high-throughput data extraction from primary literature: The metaDigitise r package
Research synthesis, such as comparative and meta-analyses, requires the extraction of effect sizes from primary literature, which are commonly calculated from descriptive statistics. However, the exact values of such statistics are commonly hidden in figures. Extracting descriptive statistics from figures can be a slow process that is not easily reproducible. Additionally, current software lacks an ability to incorporate important metadata (e.g. sample sizes, treatment/variable names) about...[Show more]
|Pick, Joel L.
|Noble, Daniel W. A.
|Research synthesis, such as comparative and meta-analyses, requires the extraction of effect sizes from primary literature, which are commonly calculated from descriptive statistics. However, the exact values of such statistics are commonly hidden in figures. Extracting descriptive statistics from figures can be a slow process that is not easily reproducible. Additionally, current software lacks an ability to incorporate important metadata (e.g. sample sizes, treatment/variable names) about experiments and is not integrated with other software to streamline analysis pipelines. Here we present the r package metaDigitise which extracts descriptive statistics such as means, standard deviations and correlations from four plot types: (a) mean/error plots (e.g. bar graphs with standard errors), (b) box plots, (c) scatter plots and (d) histograms. metaDigitise is user-friendly and easy to learn as it interactively guides the user through the data extraction process. Notably, it enables large-scale extraction by automatically loading image files, letting the user stop processing, edit and add to the resulting data-frame at any point. Digitised data can be easily re-plotted and checked, facilitating reproducible data extraction from plots with little inter-observer bias. We hope that by making the process of figure extraction more flexible and easy to conduct, it will improve the transparency and quality of meta-analyses in the future.
|J.L.P. was supported by a Swiss National Science Foundation Early Mobility grant (P2ZHP3_164962), D.W.A.N. was supported by an Australian Research Council Discovery Early Career Research Award (DE150101774) and UNSW Vice Chancellors Fellowship and S.N. an Australian Research Council Future Fellowship (FT130100268).
|© 2018 The Authors and British Ecological Society
|Methods in Ecology and Evolution
|Reproducible, flexible and high-throughput data extraction from primary literature: The metaDigitise r package
|Added manually as didn't import from ARIES
|060399 - Evolutionary Biology not elsewhere classified
|Pick, Joel L., University of New South Wales
|Nakagawa, Shinichi, University of New South Wales
|Noble, Daniel, College of Science, ANU
|970106 - Expanding Knowledge in the Biological Sciences
|https://v2.sherpa.ac.uk/id/publication/16031..."Author accepted manuscript can be made open access on non-commercial institutional repository" from SHERPA/RoMEO site (as at 29.6.2021).
|ANU Research Publications
|Author Accepted Manuscript
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.