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.

• 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]