School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia.
Res Synth Methods. 2023 Nov;14(6):911-915. doi: 10.1002/jrsm.1663. Epub 2023 Aug 11.
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 in meta-analysis, the transparency and reproducibility of the data extraction phase is still lagging behind. Unfortunately, there is little guidance of how to make this process more transparent and shareable. To address this, we provide several steps to help increase the reproducibility of data extraction in meta-analysis. We also provide suggestions of R software that can further help with reproducible data policies: the shinyDigitise and juicr packages. Adopting the guiding principles listed here and using the appropriate software will provide a more transparent form of data extraction in meta-analyses.
从研究中提取数据是荟萃分析的常规操作，当原始数据不可用时，它使研究人员能够生成效应量。尽管人们普遍呼吁增加荟萃分析的可重复性，但数据提取阶段的透明度和可重复性仍然滞后。不幸的是，几乎没有关于如何使这一过程更加透明和可共享的指导。为了解决这个问题，我们提供了几个步骤来帮助提高荟萃分析中数据提取的可重复性。我们还提供了一些关于 R 软件的建议，这些软件可以进一步帮助实现可重复的数据策略：shinyDigitise 和 juicr 包。采用这里列出的指导原则并使用适当的软件，将为荟萃分析中的数据提取提供更透明的形式。
