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Multidisciplinary Approaches to Multivalued Data: Modeling, Visualization, Analysis (Dagstuhl Seminar 16142) Hotz, Ingrid Özarslan, Evren Schultz, Thomas visualization image processing statistical analysis machine learning tensor fields higher-order descriptors diffusion-weighted imaging (DWI) structural mechanics fluid dynamics microstructure imaging connectomics uncertainty visualization feature extraction This report documents the program and the outcomes of Dagstuhl Seminar 16142, "Multidisciplinary Approaches to Multivalued Data: Modelling, Visualization, Analysis", which was attended by 27 international researchers, both junior and senior. Modelling multivalued data using tensors and higher-order descriptors has become common practice in neuroscience, engineering, and medicine. Novel tools for image analysis, visualization, as well as statistical hypothesis testing and machine learning are required to extract value from such data, and can only be developed within multidisciplinary collaborations. This report gathers abstracts of the talks held by participants on recent advances and open questions related to these challenges, as well as an account of topics raised within two of the breakout sessions. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ingrid Hotz and Evren Özarslan and Thomas Schultz 2016 Is Part Of Dagstuhl Reports, Volume 6, Issue 4 (2016) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.6.4.16 urn:nbn:de:0030-drops-61517 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.6.4.16 eng https://creativecommons.org/licenses/by/3.0/legalcode