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Dagstuhl Seminar 09251

Scientific Visualization

( Jun 14 – Jun 19, 2009 )

Please use the following short url to reference this page:



Resulting from a growth in data set size, complexity, and number of covered application areas, modern Scientific Visualization combines research from a wide variety of theoretical and practical fields such as mathematics, physics, biology and computer science. These research efforts yield a large number of different analysis, processing, and visualization techniques, allowing the efficient generation and presentation of visual results. This in turn directly contributes to the way domain experts are able to deduce knowledge from abstract data.

Emphasizing the heterogeneity of this research field, the Dagstuhl Scientific Visualization Seminar 2009 focused on a wide range of visualization topics such as ''Knowledge Assisted Visualization'', ''Visual Exploration Environment'', ''Biomedical Visualization'', and ''Visualization of Vector- and Tensorfields''. The seminar aimed to provide an open and international environment for the discussion of recent trends, breakthroughs and future directions of research in the area of visualization, fostering scientific exchange and collaboration among researchers of the Sci-Vis community and identifying new research directions.

In the course of the seminar, leading international scientists presented state-of-the-art summaries as well as novel research results and ideas. Among the discussed key topics were:

Interaction Techniques/Frameworks

To efficiently perform visual data analysis, end users and domain experts need not just be presented with visualization results, but have to be offered intuitive and efficient real-time interaction techniques and frameworks. User-centered approaches demonstrate, how human factors can influence the way data is processed and presented. Presentations and results from this seminar illustrated and devised methods for interactive data exploration and analysis.

Feature Definition and Extraction/Reconstruction

New data types and application fields require new types of features, novel extraction techniques and visualization algorithms. Work from a broad context of feature extraction and reconstruction in areas such as scalar-, vector- and tensorfield visualization was presented in the course of this seminar.

Visualization Metaphors

As existing work from the field of visualization is adapted to new application areas or visualization problems, an increase in size, structure or complexity of the given data necessarily leads to the development of optimized algorithms. This seminar identified algorithms and data structures for performance and accuracy improvement in key areas of scientific visualization such as (vector) field analysis.

Besides these topics, participants gave valuable presentations about conceptual, philosophical and psychological questions in visualization regarding the impact and benefit of user-centered approaches, research classification and other topics. The productive setting at Dagstuhl made it possible, that a selection of ideas presented at this seminar as well as scientific results of this gathering are made available as Proceedings.

  • James Ahrens (Los Alamos National Lab., US) [dblp]
  • Dirk Bartz (Universität Leipzig, DE)
  • Georges-Pierre Bonneau (INRIA - Grenoble, FR) [dblp]
  • Peer-Timo Bremer (LLNL - Livermore, US) [dblp]
  • Hamish Carr (University College Dublin, IE) [dblp]
  • Silvia Castro (National University of the South - Bahía Blanca, AR)
  • Min Chen (Swansea University, GB) [dblp]
  • Wei Chen (Zhejiang University, CN) [dblp]
  • João Luiz Dihl Comba (Federal University of Rio Grande do Sul, BR) [dblp]
  • Leila De Floriani (University of Genova, IT) [dblp]
  • Eduard Deines (University of California - Davis, US)
  • David S. Ebert (Purdue University - West Lafayette, US) [dblp]
  • Thomas Ertl (Universität Stuttgart, DE) [dblp]
  • Christoph Garth (University of California - Davis, US) [dblp]
  • Andreas Gerndt (DLR - Braunschweig, DE) [dblp]
  • Eduard Gröller (TU Wien, AT) [dblp]
  • Hans Hagen (TU Kaiserslautern, DE) [dblp]
  • Charles D. Hansen (University of Utah - Salt Lake City, US) [dblp]
  • Helwig Hauser (University of Bergen, NO) [dblp]
  • Hans-Christian Hege (ZIB - Berlin, DE) [dblp]
  • Martin Hering-Bertram (Fraunhofer ITWM - Kaiserslautern, DE)
  • Ingrid Hotz (ZIB - Berlin, DE) [dblp]
  • Yun Jang (ETH Zürich, CH)
  • Kenneth Joy (University of California - Davis, US) [dblp]
  • Arie Kaufman (SUNY - Stony Brook, US)
  • Daniel A. Keim (Universität Konstanz, DE) [dblp]
  • Gordon Kindlmann (University of Chicago, US) [dblp]
  • Robert Michael Kirby (University of Utah - Salt Lake City, US) [dblp]
  • Joe Michael Kniss (University of New Mexico - Albuquerque, US)
  • David H. Laidlaw (Brown University - Providence, US) [dblp]
  • Heike Leitte (Swansea University, GB) [dblp]
  • Lars Linsen (Jacobs University - Bremen, DE)
  • Raghu Machiraju (Ohio State University - Columbus, US) [dblp]
  • Ross Maciejewski (Purdue University - West Lafayette, US) [dblp]
  • Jörg Meyer (University of California - Irvine, US)
  • Torsten Möller (Simon Fraser University - Burnaby, CA) [dblp]
  • Klaus Mueller (SUNY - Stony Brook, US) [dblp]
  • Tamara Munzner (University of British Columbia - Vancouver, CA) [dblp]
  • Vijay Natarajan (Indian Institute of Science, IN) [dblp]
  • Ronald Peikert (ETH Zürich, CH)
  • Hanspeter Pfister (Harvard University - Cambridge, US) [dblp]
  • Bernhard Preim (Universität Magdeburg, DE) [dblp]
  • Huamin Qu (HKUST - Kowloon, HK) [dblp]
  • Penny Rheingans (University of Maryland, Baltimore Country, US) [dblp]
  • Gerik Scheuermann (Universität Leipzig, DE) [dblp]
  • Thomas Schultz (MPI für Informatik - Saarbrücken, DE) [dblp]
  • Han-Wei Shen (Ohio State University - Columbus, US) [dblp]
  • Shigeo Takahashi (University of Tokyo, JP)
  • Ayellet Tal (Technion - Haifa, IL) [dblp]
  • Holger Theisel (Universität Magdeburg, DE) [dblp]
  • Xavier Tricoche (Purdue University - West Lafayette, US) [dblp]
  • Jarke J. van Wijk (TU Eindhoven, NL) [dblp]
  • Amitabh Varshney (University of Maryland - College Park, US) [dblp]
  • Anna Vilanova (Eindhoven University of Technology, NL) [dblp]
  • Gunther Weber (Lawrence Berkeley National Laboratory, US) [dblp]
  • Daniel Weiskopf (Universität Stuttgart, DE) [dblp]
  • Rüdiger Westermann (TU München, DE) [dblp]
  • Ross Whitaker (University of Utah - Salt Lake City, US) [dblp]
  • Thomas Wischgoll (Wright State University - Dayton, US) [dblp]
  • Anders Ynnerman (Linköping University, SE) [dblp]
  • Ye Zhao (Kent State University, US)

Related Seminars
  • Dagstuhl Seminar 9135: Scientific Visualization (1991-08-26 - 1991-08-30) (Details)
  • Dagstuhl Seminar 9421: Scientific Visualization (1994-05-23 - 1994-05-27) (Details)
  • Dagstuhl Seminar 9724: Scientific Visualization (1997-06-09 - 1997-06-13) (Details)
  • Dagstuhl Seminar 00211: Scientific Visualization (2000-05-21 - 2000-05-26) (Details)
  • Dagstuhl Seminar 03231: Scientific Visualization: Extracting Information and Knowledge from Scientific Data Sets (2003-06-01 - 2003-06-06) (Details)
  • Dagstuhl Seminar 05231: Scientific Visualization: Challenges for the Future (2005-06-05 - 2005-06-10) (Details)
  • Dagstuhl Seminar 07291: Scientific Visualization (2007-07-15 - 2007-07-20) (Details)
  • Dagstuhl Seminar 11231: Scientific Visualization (2011-06-05 - 2011-06-10) (Details)
  • Dagstuhl Seminar 14231: Scientific Visualization (2014-06-01 - 2014-06-06) (Details)
  • Dagstuhl Seminar 18041: Foundations of Data Visualization (2018-01-21 - 2018-01-26) (Details)

  • visualization / computer graphics / modeling

  • scientific visualization
  • data analysis
  • data modeling
  • segmentation
  • knowledge extraction
  • ubiquitous visualization
  • categorical visualization
  • intelligent/automatic visualization
  • point-based/mesh-free visualization