Dynameta: a dynamic platform for ecological meta-analyses in R Shiny
Skinner, Grace ORCID: https://orcid.org/0000-0002-6972-2963; Cooke, Rob ORCID: https://orcid.org/0000-0003-0601-8888; Keum, Junghyuk; Purvis, Andy ORCID: https://orcid.org/0000-0002-8609-6204; Raw, Cristina; Woodcock, Ben A. ORCID: https://orcid.org/0000-0003-0300-9951; Millard, Joseph ORCID: https://orcid.org/0000-0002-3025-3565. 2023 Dynameta: a dynamic platform for ecological meta-analyses in R Shiny. SoftwareX, 23, 101439. 6, pp. 10.1016/j.softx.2023.101439
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Abstract/Summary
Meta-analyses have brought a significant improvement in our understanding of global biodiversity change. However, in ecology the static nature of current approaches, both in terms of the data included and the predictions presented, make meta-analyses difficult for policymakers to fully interrogate and adopt. Here we introduce Dynameta, a living-review (i.e. continually updateable) R Shiny platform for interactive ecological meta-analyses, oriented around testing the effect of anthropogenic threats on biodiversity. This platform is written as an R package and can be applied in the context of any meta-analytic PICO (Population, Intervention, Comparator, and Outcome) question concerning the effect of any threat on any taxonomic group, for any biodiversity metric and with relevance to all geographic regions. Our hope is that, alongside other existing tools, Dynameta can help encourage the broader adoption of dynamic meta-analyses in ecology.
Item Type: | Publication - Article |
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Digital Object Identifier (DOI): | 10.1016/j.softx.2023.101439 |
UKCEH and CEH Sections/Science Areas: | Biodiversity (Science Area 2017-) |
ISSN: | 2352-7110 |
Additional Information. Not used in RCUK Gateway to Research.: | Open Access - full text available via Official URL link. |
Additional Keywords: | dynamic meta-analysis, living-review, biodiversity change, R Shiny, anthropogenic disturbance |
NORA Subject Terms: | Ecology and Environment Computer Science Data and Information |
Date made live: | 10 Jul 2023 11:28 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/535062 |
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