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dc.contributor.authorSimmonds, Emily Grace
dc.contributor.authorDunn-Sigouin, Etienne
dc.contributor.authorAdjei, Kwaku Peprah
dc.contributor.authorAndersen, Christoffer Wold
dc.contributor.authorAspheim, Janne Cathrin Hetle
dc.contributor.authorBattistin, Claudia
dc.contributor.authorBulso, Nicola
dc.contributor.authorChristensen, Hannah M.
dc.contributor.authorCretois, Benjamin
dc.contributor.authorCubero, Ryan John Abat
dc.contributor.authorDavidovich, Ivan Andres
dc.contributor.authorDickel, Lisa
dc.contributor.authorDunn, Benjamin Adric
dc.contributor.authorDyrstad, Karin
dc.contributor.authorEinum, Sigurd
dc.contributor.authorGiglio, Donata
dc.contributor.authorGjerløw, Haakon
dc.contributor.authorGodefroidt, Amélie
dc.contributor.authorGonzález-Gil, Ricardo
dc.contributor.authorGonzalo Cogno, Soledad
dc.contributor.authorGroße, Fabian
dc.contributor.authorHalloran, Paul
dc.contributor.authorJensen, Mari Fjalstad
dc.contributor.authorKennedy, John James
dc.contributor.authorLangsæther, Peter Egge
dc.contributor.authorLaverick, Jack H
dc.contributor.authorLederberger, Debora
dc.contributor.authorLi, Camille
dc.contributor.authorMandeville, Elizabeth G
dc.contributor.authorMandeville, Caitlin
dc.contributor.authorMoe, Espen
dc.contributor.authorSchröder, Tobias Navarro
dc.contributor.authorNunan, David
dc.contributor.authorSicacha-Parada, Jorge
dc.contributor.authorSimpson, Melanie Rae
dc.contributor.authorSkarstein, Emma Sofie
dc.contributor.authorSpensberger, Clemens
dc.contributor.authorStevens, Richard
dc.contributor.authorSubramanian, Aneesh C.
dc.contributor.authorSvendsen, Lea
dc.contributor.authorTheisen, Ole Magnus
dc.contributor.authorWatret, Connor
dc.contributor.authorO'Hara, Robert B.
dc.date.accessioned2023-02-06T14:04:24Z
dc.date.available2023-02-06T14:04:24Z
dc.date.created2022-11-09T11:09:45Z
dc.date.issued2022
dc.identifier.issn2589-0042
dc.identifier.urihttps://hdl.handle.net/11250/3048628
dc.description.abstractQuantifying 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.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleInsights into the quantification and reporting of model-related uncertainty across different disciplinesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.source.articlenumber105512en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1016/j.isci.2022.105512
dc.identifier.cristin2071107
dc.source.journaliScienceen_US
dc.relation.projectNorges forskningsråd: 255027en_US
dc.identifier.citationiScience. 2022, 25 (12), 105512.en_US
dc.source.volume25en_US
dc.source.issue12en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal