The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Reproducible, flexible and high‐throughput data extraction from primary literature: The metaDigitise r package
Open accessAbstract
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.
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Cited by
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O'Dea, R. E., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W. A., Parker, T. H., Gurevitch, J., Page, M. J., Stewart, G., Moher, D., et al. (2021) · Biological Reviews · doi:10.1111/brv.12721 · Cited by 697
Self-citationRocci, K. S., Lavallee, J. M., Stewart, C. E., & Cotrufo, M. F. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.148569 · Cited by 470
Promoting memory consolidation during sleep: A meta-analysis of targeted memory reactivation
Hu, X., Cheng, L. Y., Chiu, M. H., & Paller, K. A. (2020) · Psychological Bulletin · doi:10.1037/bul0000223 · Cited by 232
Software support for environmental evidence synthesis
Westgate, M. J., Haddaway, N. R., Cheng, S. H., McIntosh, E. J., Marshall, C., & Lindenmayer, D. B. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0502-x · Cited by 51
Metadata
- Authors
- Pick, J. L., Nakagawa, S., & Noble, D. W. A.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2018-11-26
- Volume
- 10
- Issue
- 3
- Pages
- 426-431
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2952314317
- Cited by
- 257
- References
- 15
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Pick, J. L., Nakagawa, S., & Noble, D. W. A. (2019). Reproducible, flexible and high‐throughput data extraction from primary literature: The metaDigitise r package. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13118
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
