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dc.contributor.authorSterzik, Annaen_US
dc.contributor.authorLichtenberg, Nilsen_US
dc.contributor.authorKrone, Michaelen_US
dc.contributor.authorCunningham, Douglas W.en_US
dc.contributor.authorLawonn, Kaien_US
dc.contributor.editorRenata G. Raidouen_US
dc.contributor.editorBjörn Sommeren_US
dc.contributor.editorTorsten W. Kuhlenen_US
dc.contributor.editorMichael Kroneen_US
dc.contributor.editorThomas Schultzen_US
dc.contributor.editorHsiang-Yun Wuen_US
dc.date.accessioned2022-09-19T11:46:28Z
dc.date.available2022-09-19T11:46:28Z
dc.date.issued2022
dc.identifier.isbn978-3-03868-177-9
dc.identifier.issn2070-5786
dc.identifier.urihttps://doi.org/10.2312/vcbm.20221186
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20221186
dc.description.abstractData are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the users' trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Therefore, we conducted a perceptual study to determine the suitability of the line variables sketchiness, dashing, grayscale, and width for distinguishing several uncertainty values on molecular surfaces.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Empirical studies in visualization; Scientific visualization; Computing methodologies --> Non-photorealistic rendering; Applied computing --> Imaging"
dc.subjectHuman centered computing
dc.subjectEmpirical studies in visualization
dc.subjectScientific visualization
dc.subjectComputing methodologies
dc.subjectNon
dc.subjectphotorealistic rendering
dc.subjectApplied computing
dc.subjectImaging"
dc.titlePerceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfacesen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.description.sectionheadersUncertainties, Ensembles, and Comparisons
dc.identifier.doi10.2312/vcbm.20221186
dc.identifier.pages41-51
dc.identifier.pages11 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License