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dc.contributor.authorUlbrich, Pavol
dc.contributor.authorWaldner, Manuela
dc.contributor.authorFurmanová, Katarína
dc.contributor.authorMarques, Sergio M.
dc.contributor.authorBednář, David
dc.contributor.authorKozlíková, Barbora
dc.contributor.authorByska, Jan
dc.date.accessioned2023-03-01T13:47:55Z
dc.date.available2023-03-01T13:47:55Z
dc.date.created2023-01-31T21:27:44Z
dc.date.issued2023
dc.identifier.issn1077-2626
dc.identifier.urihttps://hdl.handle.net/11250/3055055
dc.description.abstractWe present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titlesMolBoxes: Dataflow Model for Molecular Dynamics Explorationen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2022 IEEEen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doi10.1109/TVCG.2022.3209411
dc.identifier.cristin2121173
dc.source.journalIEEE Transactions on Visualization and Computer Graphicsen_US
dc.source.pagenumber581-590en_US
dc.identifier.citationIEEE Transactions on Visualization and Computer Graphics. 2023, 29 (1), 581-590.en_US
dc.source.volume29en_US
dc.source.issue1en_US


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