How is model-related uncertainty quantified and reported in different disciplines?

Simmonds, Emily G.; Peprah, Adjei, Kwaku; Andersen, Christoffer Wold; Aspheim, Janne Cathrin Hetle; Battistin, Claudia; Bulso, Nicola; Christensen, Hannah; Cretois, Benjamin; Cubero, Ryan John; Davidovich, Ivan A.; Dickel, Lisa; Dunn, Benjamin; Dunn‐Sigouin, Etienne; Dyrstad, Karin; Einum, Sigurd; Giglio, Donata; Gjerløw, Haakon; Godefroidt, Amelie; Gonzalez-Gil, Ricardo; Cogno, Soledad Gonzalo; Große, Fabian; Halloran, Paul R.; Jensen, Mari F.; Kennedy, John James; Langsaether, Peter Egge; Laverick, Jack H.; Lederberger, Debora; Li, Camille; Mandeville, Elizabeth G.; Mandeville, Caitlin P.; Moe, Espen; Schröder, Tobias Navarro; Nunan, David; Parada, Jorge Sicacha; Simpson, Melanie Rae; Skarstein, Emma Sofie; Spensberger, Clemens; Stevens, Richard L; Subramanian, Aneesh; Svendsen, Lea; Theisen, Ole Magnus; Connor, Watret,; OHara, Robert B.

doi:10.48550/arxiv.2206.12179

Cited by 1 publication

(1 citation statement)

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“…Typically, a large quantity of the data needed for meta-analysis are locked away within figures. Indeed, Simmonds et al (2022) recently showed that in 3%-64% of studies (depending on the field of research), had measures of uncertainty needed for meta-analysis (e.g. standard error or variance) only contained within figures.…”

Section: Introductionmentioning

confidence: 99%

A framework for improving the reproducibility of data extraction for meta-analysis

Ivimey‐Cook¹,

Noble²,

Nakagawa³

et al. 2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%