Blar i NTNU Open på forfatter "Skarstein, Emma Sofie"

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    • Accounting for spatial bias in citizen science observations of Norwegian freshwater fish by using an effort spatial field 

      Skarstein, Emma Sofie (Master thesis, 2020)
      Modellbasert dataintegrasjon gir et lovende rammeverk for konstruksjon av artsfordelingsmodeller ved bruk av folkeforsknings-data sammen med strukturerte data fra undersøkelser, men en vanlig utfordring er hvordan man skal ...

    • Insights into the quantification and reporting of model-related uncertainty across different disciplines 

      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. (Peer reviewed; Journal article, 2022)
      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 ...

    • A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations 

      Skarstein, Emma Sofie; Martino, Sara; Muff, Stefanie (Peer reviewed; Journal article, 2023)
      Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while ...

    • Recommendations for quantitative uncertainty consideration in ecology and evolution 

      Simmonds, Emily Grace; Adjei, Kwaku Peprah; Cretois, Benjamin; Dickel, Lisa; González-Gil, Ricardo; Laverick, Jack H; Mandeville, Caitlin Marie; Mandeville, Elizabeth G; Ovaskainen, Otso Tapio; Sicacha-Parada, Jorge; Skarstein, Emma Sofie; O'Hara, Robert Brian (Peer reviewed; Journal article, 2023)
      Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers – a focus on parameter-related uncertainty, obscure ...