Dagstuhl Seminar 14231
Scientific Visualization
( Jun 01 – Jun 06, 2014 )
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Organizers
- Min Chen (University of Oxford, GB)
- Charles D. Hansen (University of Utah - Salt Lake City, US)
- Penny Rheingans (University of Maryland, Baltimore Country, US)
- Gerik Scheuermann (Universität Leipzig, DE)
Contact
- Annette Beyer (for administrative matters)
Schedule
Scientific Visualization (SV) is the transformation of digital data, derived from observation or simulation, into readily comprehensible images, and has proven to play an indispensable part of the scientific discovery process in many fields of contemporary science. Since its inception two decades ago, the techniques of Scientific Visualization have aided scientists, engineers, medical practitioners, and others in the study of a wide variety of data including high-performance computing simulations, measured data from scanners, internet traffic, and financial records. This interplay between application areas and specific problem-solving visualization techniques will be emphasized in the 2014 SciVis seminar. We plan to discuss four major themes: uncertainty visualization, integrated multi-field visualization, scientific visualization to support sustainability and environmental applications (or Environmental Scientific Visualization in short), and scientific foundations of visualization.
Uncertainty Visualization: Decision making, especially rapid decision making, is always made under uncertain conditions. As former English Statesmen and Nobel Laureate (Literature), Winston Churchill said, "True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information." Uncertainty visualization seeks to provide a visual representation of errors and uncertainty for three-dimensional visualizations. Challenges include the inherent difficulty in characterizing comparisons among different data sets and the corresponding error and uncertainty in the experimental, simulation, and/or visualization processes.
Integrated Multi-field Visualization: The output of computational science simulations is typically a combination of fields involving a number of scalar fields, vector fields, or tensor fields. Similarly, data collected experimentally is often multi-field in nature. Multi-scale problems with scale differences of several orders of magnitude in computational fluid dynamics, material science, nanotechnology, biomedical engineering and proteomics pose challenging problems for data analysis. The state of the art in multi-scale visualization considerably lags behind that of multi-scale simulation. Novel solutions to multi-scale and multi-field visualization problems can have a large impact on scientific endeavors.
Environmental Scientific Visualization: Environmental scientific visualization refers to a collection of visualization applications that deal with captured and simulated data in climate research, atmospheric and environmental sciences, earth science, geophysics and seismic research, oceanography, and the energy industry. Research in these applications has a huge impact on mankind, and typically faces serious challenges of data deluge (e.g., very large volumes of multi-spectral satellite images, large data collections from different sensor types, ensemble computation of very large simulation models, scattered, time-varying, multi-modal data in seismic research). Scientific progress in the areas of the environment and sustainability is critical in the solution of global problems and scientific visualization has great potential to support this progress.
Scientific Foundation of Visualization: Many fundamental questions about the theoretical and perceptual aspects of visualization remain unanswered, such as, why is one visual design more effective than another, can visual designs be optimized and how, what is the role of visualization in a scientific workflow and how can such a role be formalized, can visualization quality be measured quantitatively and how, and what is the most informative way to conduct perceptual and usability studies involving domain experts? With the experience of delivering technical advances over the past two decades, it is timely for the visualization community to address these fundamental questions with a consorted effort. Such an effort will be critical to the long-term development of the subject, especially in building a scientific foundation for the subject.
Scientific Visualization (SV) is the transformation of digital data, derived from observation or simulation, into readily comprehensible images, and has proven to play an indispensable part of the scientific discovery process in many fields of contemporary science. Since its inception two decades ago, the techniques of Scientific Visualization have aided scientists, engineers, medical practitioners, and others in the study of a wide variety of data including, for example, high-performance computing simulations, measured data from scanners (CT, MR, confocal microscopy, satellites), internet traffic, and financial records. One of the important themes being nurtured under the aegis of Scientific Visualization is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes and simulations involving voluminous data across diverse scientific disciplines. Since vision dominates our sensory input, strong efforts have been made to bring the mathematical abstraction and modeling to our eyes through the mediation of computer graphics. This interplay between various application areas and their specific problem-solving visualization techniques has been the goal of all the Dagstuhl Scientific Visualization seminars and was emphasized in the seminar which took place June 1-6, 2014.
Our seminar was focused on four research themes that will have significant impact in the coming years. These four themes reflect the heterogeneous structure of Scientific Visualization and the current unsolved problems in the field. They represent cross-cutting topic areas where applications influence basic research questions on one hand while basic research drives applications on the other. This cross-cutting feature makes Dagstuhl a unique setting in the research community, as the scientific coverage of the seminar is broader than other more focused workshops and seminars hosted at Dagstuhl while much more focused and forward-looking than general conferences. Our four themes were:
Uncertainty Visualization: Decision making, especially rapid decision making, is always made under uncertain conditions. As former English Statesman and Nobel Laureate (Literature), Winston Churchill said, "True genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information." and echoed by Nobel Prize winning physicist Richard Feynman, "What is not surrounded by uncertainty cannot be the truth." Uncertainty visualization seeks to provide a visual representation of errors and uncertainty for three-dimensional visualizations. Challenges include the inherent difficulty in defining, characterizing, and controlling comparisons among different data sets and in part to the corresponding error and uncertainty in the experimental, simulation, and/or visualization processes.
Integrated Multi-field Visualization: The output of the majority of computational science and engineering simulations is typically a combination of fields, generally called multi-field data, involving a number of scalar fields, vector fields, or tensor fields. Similarly, data collected experimentally is often multi-field in nature (and from multiple sources). The ability to effectively visualize multiple fields simultaneously, for both computational and experimental data, can greatly enhance scientific analysis and understanding. Multi-scale problems with scale differences of several orders of magnitude in computational fluid dynamics, material science, nanotechnology, biomedical engineering and proteomics pose challenging problems for data analysis. The state of the art in multi-scale visualization considerably lags behind that of multi-scale simulation. Novel solutions to multi-scale and multi-field visualization problems have the potential for a large impact on scientific endeavors.
Environmental Scientific Visualization: Environmental scientific visualization or environmental visualization refers to a collection of visualization applications that deal with captured and simulated data in climate research, atmospheric and environmental sciences, earth science, geophysics and seismic research, oceanography, and the energy industry (e.,g., oil, gas and renewable energy). Research in these application domains has a huge impact on mankind, and typically faces serious challenges of data deluge (e.,g., very large volumes of multi-spectral satellite images, large data collections from different sensor types, ensemble computation of very large simulation models, scattered, time-varying, multi-modal data in seismic research). In comparison with biomedical visualization and small-to-medium scale computational fluid dynamics, the effort for developing visualization techniques for such applications has not been compatible with the importance and scale of the underlying scientific activities in these application domains. Scientific progress in the areas of the environment and sustainability is critical in the solution of global problems and scientific visualization has great potential to support this progress.
Scientific Foundation of Visualization: The rapid advances in scientific visualization have resulted in a large collection of visual designs (e.,g., for flow visualization), algorithms (e.,g., for volume rendering), and software tools and development kits. There have also been some scattered investigations into the theoretic and perceptual aspects of visualization. However, many fundamental questions remain unanswered, such as, why is one visual design more effective than another, can visual designs be optimized and how, what is the role of visualization in a scientific workflow and how can such a role be formalized in a scientific workflow, can visualization quality be measured quantitatively and how, and what is the most effective way to conduct perceptual and usability studies involving domain experts? With the experience of delivering technical advances over the past two decades, it is timely for the visualization community to address these fundamental questions with a consorted effort. Such an effort will be critical to the long-term development of the subject, especially in building a scientific foundation for the subject.
The format of the seminar was two-part: having groups of four to five shorter talks followed by a panel of the speakers which encouraged discussion and breakout groups on the four topics as well as topics which came up at the meeting. The scientific presentations were scheduled at the beginning of the week in order to simulate the discussions from a broad perceptive. Unlike the typical arrangement, all presentations in each session were given in sequence without a short Q&A session at the end of each talk. Instead, all speakers of a session were invited to sit on the stage after the presentation, and answer questions in a manner similar to panel discussions. This format successfully brought senior and junior researchers onto the same platform, and enabled researchers to seek a generic and deep understanding through their questions and answers. It also stimulated very long, intense, and fruitful discussions that were embraced by all participants. The breakout groups focused on the general themes and are reported in the following sections.
Creative Commons BY 3.0 Unported license
Min Chen, Charles D. Hansen, Penny Rheingans, and Gerik Scheuermann
- James Ahrens (Los Alamos National Lab., US) [dblp]
- Georges-Pierre Bonneau (INRIA - Grenoble, FR) [dblp]
- Rita Borgo (Swansea University, GB) [dblp]
- Nadia Boukhelifa (University Paris-Sud - Gif sur Yvette, FR) [dblp]
- Hamish Carr (University of Leeds, GB) [dblp]
- Remco Chang (Tufts University - Medford, US) [dblp]
- Jian Chen (University of Maryland, Baltimore Country, US) [dblp]
- Min Chen (University of Oxford, GB) [dblp]
- Hank Childs (University of Oregon - Eugene, US) [dblp]
- João Luiz Dihl Comba (Federal University of Rio Grande do Sul, BR) [dblp]
- Leila De Floriani (University of Genova, IT) [dblp]
- Thomas Ertl (Universität Stuttgart, DE) [dblp]
- Issei Fujishiro (Keio University, JP) [dblp]
- Christoph Garth (TU Kaiserslautern, DE) [dblp]
- Andreas Gerndt (DLR - Braunschweig, DE) [dblp]
- Michael Gleicher (University of Wisconsin - Madison, US) [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 (Konrad-Zuse-Zentrum - Berlin, DE) [dblp]
- Mario Hlawitschka (Universität Leipzig, DE) [dblp]
- Ingrid Hotz (DLR - Braunschweig, DE) [dblp]
- Christopher R. Johnson (University of Utah, US) [dblp]
- Kenneth Joy (University of California - Davis, US) [dblp]
- Robert Michael Kirby (University of Utah - Salt Lake City, US) [dblp]
- David H. Laidlaw (Brown University - Providence, US) [dblp]
- Heike Leitte (Universität Heidelberg, DE) [dblp]
- Ross Maciejewski (Arizona State University - Tempe, US) [dblp]
- Georgeta Elisabeta Marai (University of Pittsburgh, US) [dblp]
- Torsten Möller (Universität Wien, AT) [dblp]
- Kristi Potter (University of Utah - Salt Lake City, US) [dblp]
- Penny Rheingans (University of Maryland, Baltimore Country, US) [dblp]
- Timo Ropinski (Linköping University, SE) [dblp]
- Gerik Scheuermann (Universität Leipzig, DE) [dblp]
- Claudio T. Silva (New York University, US) [dblp]
- Holger Theisel (Universität Magdeburg, DE) [dblp]
- Amitabh Varshney (University of Maryland - College Park, US) [dblp]
- Anna Vilanova (TU Delft, NL) [dblp]
- Daniel Weiskopf (Universität Stuttgart, DE) [dblp]
- Rüdiger Westermann (TU München, DE) [dblp]
- Ross Whitaker (University of Utah - Salt Lake City, US) [dblp]
- Anders Ynnerman (Linköping University, SE) [dblp]
- Xiaoru Yuan (Peking University, CN) [dblp]
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Classification
- computer graphics / computer vision
- modelling / simulation
- society / human-computer interaction
Keywords
- scientific visualization
- uncertainty
- environmental visualization
- multi-field visualization
