Insights into the quantification and reporting of model-related uncertainty across different disciplines
dc.date.accessioned | 2023-01-26T17:38:40Z | |
dc.date.available | 2023-01-26T17:38:40Z | |
dc.date.created | 2022-11-09T11:09:45Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Simmonds, Emily Grace Dunn-Sigouin, Etienne Adjei, Kwaku Peprah Andersen, Christoffer Wold Aspheim, Janne Cathrin Hetle Battistin, Claudia Bulso, Nicola Christensen, Hannah M. Cretois, Benjamin Cubero, Ryan John Abat Davidovich, Ivan Andres Dickel, Lisa Dunn, Benjamin Adric Dyrstad, Karin Einum, Sigurd Giglio, Donata Gjerløw, Haakon Godefroidt, Amélie González-Gil, Ricardo Gonzalo Cogno, Soledad Große, Fabian Halloran, Paul Jensen, Mari Fjalstad Kennedy, John James Langsæther, Peter Egge Laverick, Jack H Lederberger, Debora Li, Camille Mandeville, Elizabeth G Mandeville, Caitlin Moe, Espen Schröder, Tobias Navarro Nunan, David Sicacha-Parada, Jorge Simpson, Melanie Rae Skarstein, Emma Sofie Spensberger, Clemens Stevens, Richard Subramanian, Aneesh C. Svendsen, Lea Theisen, Ole Magnus Watret, Connor O'Hara, Robert B. . Insights into the quantification and reporting of model-related uncertainty across different disciplines. iScience. 2022 | |
dc.identifier.uri | http://hdl.handle.net/10852/99281 | |
dc.description.abstract | Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the “sources of uncertainty” framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research. | |
dc.language | EN | |
dc.publisher | Cell Press | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Insights into the quantification and reporting of model-related uncertainty across different disciplines | |
dc.title.alternative | ENEngelskEnglishInsights into the quantification and reporting of model-related uncertainty across different disciplines | |
dc.type | Journal article | |
dc.creator.author | Simmonds, Emily Grace | |
dc.creator.author | Dunn-Sigouin, Etienne | |
dc.creator.author | Adjei, Kwaku Peprah | |
dc.creator.author | Andersen, Christoffer Wold | |
dc.creator.author | Aspheim, Janne Cathrin Hetle | |
dc.creator.author | Battistin, Claudia | |
dc.creator.author | Bulso, Nicola | |
dc.creator.author | Christensen, Hannah M. | |
dc.creator.author | Cretois, Benjamin | |
dc.creator.author | Cubero, Ryan John Abat | |
dc.creator.author | Davidovich, Ivan Andres | |
dc.creator.author | Dickel, Lisa | |
dc.creator.author | Dunn, Benjamin Adric | |
dc.creator.author | Dyrstad, Karin | |
dc.creator.author | Einum, Sigurd | |
dc.creator.author | Giglio, Donata | |
dc.creator.author | Gjerløw, Haakon | |
dc.creator.author | Godefroidt, Amélie | |
dc.creator.author | González-Gil, Ricardo | |
dc.creator.author | Gonzalo Cogno, Soledad | |
dc.creator.author | Große, Fabian | |
dc.creator.author | Halloran, Paul | |
dc.creator.author | Jensen, Mari Fjalstad | |
dc.creator.author | Kennedy, John James | |
dc.creator.author | Langsæther, Peter Egge | |
dc.creator.author | Laverick, Jack H | |
dc.creator.author | Lederberger, Debora | |
dc.creator.author | Li, Camille | |
dc.creator.author | Mandeville, Elizabeth G | |
dc.creator.author | Mandeville, Caitlin | |
dc.creator.author | Moe, Espen | |
dc.creator.author | Schröder, Tobias Navarro | |
dc.creator.author | Nunan, David | |
dc.creator.author | Sicacha-Parada, Jorge | |
dc.creator.author | Simpson, Melanie Rae | |
dc.creator.author | Skarstein, Emma Sofie | |
dc.creator.author | Spensberger, Clemens | |
dc.creator.author | Stevens, Richard | |
dc.creator.author | Subramanian, Aneesh C. | |
dc.creator.author | Svendsen, Lea | |
dc.creator.author | Theisen, Ole Magnus | |
dc.creator.author | Watret, Connor | |
dc.creator.author | O'Hara, Robert B. | |
cristin.unitcode | 185,17,8,0 | |
cristin.unitname | Institutt for Statsvitenskap | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.fulltext | postprint | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 2071107 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=iScience&rft.volume=&rft.spage=&rft.date=2022 | |
dc.identifier.jtitle | iScience | |
dc.identifier.volume | 25 | |
dc.identifier.issue | 12 | |
dc.identifier.doi | https://doi.org/10.1016/j.isci.2022.105512 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 2589-0042 | |
dc.type.version | PublishedVersion | |
cristin.articleid | 105512 | |
dc.relation.project | NFR/255027 |
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