Reproducible, flexible and high throughput data extraction from primary literature: The metaDigitise R package [article]

Joel L Pick, Shinichi Nakagawa, Daniel WA Noble
2018 bioRxiv   pre-print
1. Research synthesis, such as meta-analysis requires the extraction of effect sizes from primary literature. Such effect sizes are calculated from summary statistics. However, exact values of such statistics are commonly hidden in figures. 2. Extracting summary statistics from figures can be a slow process that is not easily reproducible. Additionally, current software lacks an ability to incorporate important meta-data (e.g., sample sizes, treatment/variable names) about experiments and is
more » ... integrated with other software to streamline analysis pipelines. 3. Here we present the R package metaDigitise which extracts descriptive statistics such as means, standard deviations and correlations from the four plot types: 1) mean/error plots (e.g. bar graphs with standard errors), 2) box plots, 3) scatter plots and 4) 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. 4. 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.
doi:10.1101/247775 fatcat:6u74capdrzfqxn2a5rtmp24ht4