Scientific visualization

Scientific visualization is a branch of computer graphics which is concerned with the presentation of interactive or animated digital images to scientists who interpret potentially huge quantities of laboratory or simulation data or the results from sensors out in the field.

The field of data mining offers many abstract visualizations related to scientific visualization. There is a wider field, that of information visualization, which encompasses all visualization that does not deal with the life sciences or engineering.

Overview

Most people are familiar with the digital animations produced to present meteorological data during weather reports on television, though few can distinguish between those models of reality and the satellite photos which are also shown on such programs. TV also offers scientific visualizations when it shows computer drawn and animated reconstructions of road or airplane accidents. Some of the most popular examples of scientific visualizations are computer generated images which show real spacecraft in action, out in the void far beyond Earth, or on other planets. Dynamic forms of visualisation such as educational animation have the potential to enhance learning about systems that change over time.

Apart from the distinction between interactive visualizations and animation, the most useful categorization is probably between abstract and model-based scientific visualizations. The abstract visualizations show completely conceptual constructs in 2D or 3D. These generated shapes are completely arbitrary. The model-based visualizations either place overlays of data on real or digitally constructed images of reality, or they make a digital construction of a real object directly from the scientific data.

Scientific visualization is usually done with specialized software, though there are a few exceptions, noted below. Some of these specialized programs have been released as Open source software, having very often its origins in universities, within an academic environment where sharing software tools and giving access to the source code is common. There are also many proprietary software packages of scientific visualization tools.

In engineering

Some attribute the birth of Scientific Visualization to the efforts of electrical engineering professionals in the 1980s. This is a highly debated topic. Others point to such efforts as the mainframe generated Chernoff faces of the 1970s, which we owe to the noted mathematician Herman Chernoff. These multivariate expressions of data were, in their original form, not interactive or animated, but their supporters point out that animated and/or interactive versions are now available.

In the life sciences

Desktop programs capable of presenting interactive models of molecules and microbiological entities are becoming relatively common. The field of Bioinformatics and the field of Cheminformatics make a heavy use of these visualization engines for interpreting lab data and for training purposes. Since this field has known its biggest growth spurt at about the same time as the web, it is keen on integrating metadata formats such as the XML based Chemical Markup Language, while being conscious of older formats such as SMILES.

References

• Books
  • Visualization Handbook (Hardcover) by Charles D. Hansen, Chris Johnson, Academic Press (June, 2004).
  • The Visualization Toolkit, Third Edition (Paperback) by Will Schroeder, Ken Martin, Bill Lorensen (August 2004).
• renowed researcher: Donna Cox
• General
  • Globus, Al. Eric Raible. "Fourteen Ways to Say Nothing With Scientific Visualization". Computer. July 1994. pp. 86-88
  • Kravetz, Stephen A. and David Womble. ed. Introduction to Bioinformatics. Totowa, N.J. Humana Press, 2003.
  • Nielson, Gregory M. ed. Computer. Vol. 22, No. 8, Aug 1989. Special issue on scientific visualization.
  • Tufte, Edward, The Visual Display of Quantitative Information.
  • Wong, Pak Chung. R. Daniel Bergeron. "30 years of Multidimensional Multivariate Visualization". Scientific Visualization Overviews Methodologies and Techniques. IEEE Computer Society Press, 1997.
• Miscellaneous example systems
  • Pang, Alex; Wittenbrink, Craig. "Collaborative 3D Visualization with CSpray". IEEE Computer Graphics and Applications. July/August 1997. Vol. 17. No. 4, pp. 32-41. http://www.cse.ucsc.edu/research/slvg/cspray.html

External links

• VIS the annually held IEEE Visualization (VIS) academic conference
• Vienna University of Technology http://www.cg.tuwien.ac.at/research/vis/
• University of Florida http://vam.anest.ufl.edu
• Georgia Tech http://www.cc.gatech.edu/scivis/tutorial/tutorial.html
• University of Stuttgart's Visualization and Interactive Systems Group http://www.vis.uni-stuttgart.de/eng/
• Stony Brook's Visualization Lab http://www.cs.sunysb.edu/~vislab/
• Scientific Computing Insitute at the University of Utah http://www.sci.utah.edu/
• Ohio State's Graphics Lab http://www.cse.ohio-state.edu/research/graphics/
• Konrad Zuse Institute in Berlin (ZIB) http://www.zib.de/visual and http://visinfo.zib.de/
• Delft University of Technology Visualization Group http://visualization.tudelft.nl/
• National Institute of Standards and Technology http://math.nist.gov/mcsd/savg/vis/index.html
• University of Wisconsin's SSEC Visualization Project http://www.ssec.wisc.edu/~billh/vis.html

Software

• HoloDraw http://holodraw.org/
• Open Source Data Explorer http://www.opendx.org/
• The Visualization Toolkit http://www.vtk.org
• Amira http://www.amiravis.com and http://www.tgs.com
• ParaView http://www.paraview.org
• VisIt http://www.llnl.gov/visit
• SCIRUN http://software.sci.utah.edu/scirun.html
• Vis5D http://www.ssec.wisc.edu/~billh/vis5d.html
• VisAD http://www.ssec.wisc.edu/~billh/visad.html
• VMD (Visual Molecular Dynamics) http://www.ks.uiuc.edu/Research/vmd/
• Matlab http://www.mathworks.com/
• Scilab http://www.scilab.org/
• Spotfire http://www.spotfire.com/