Scientific visualization

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Scientific- (=Data-), and Information visualization are branches of computer graphics which are 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 to aid reasoning, hypothesis building and cognition. The field of data mining offers many abstract visualizations related to these visualization types. They are active research areas, drawing on theory in information graphics, computer graphics, human-computer interaction and cognitive science.


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Usage and distinction of the terms

In common usage, the slightly more general term information visualization is used to encompasses all visualizations that do not deal with the life sciences or engineering. Another aspect is termed visual analytics - the formation of abstract visual metaphors in combination with a human information discourse (interaction) that enables detection of the expected and discovery of the unexpected within massive, dynamically changing information spaces. These suites of technologies apply to almost all fields but are being driven by critical needs in biology and national security.

Information visualization, scientific visualization and visual analytics have lots of overlapping goals and techniques. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows. Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles more abstract data structures such as trees or graphs. Visual analytics is especially concerned with sensemaking and reasoning.The distinction between "natural" and complex data structures, however is blurred, keeping in mind that graphs can in general represented by adjacency matrices.

Another basic distinction could be made on the basis of numerical vs. non-numerical data. In practice, however this distinction becomes artificial, because the levels of measurement that are used in statistics and statistical packages encompass both.

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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.

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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.

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In the medical and 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.

Medical imaging is a huge application domain for scientific visualization with an emphasis on enhancing imaging results graphically, e.g. using pseudo-coloring or overlaying of plots. Real-time visualization can serve to simultaneusly image analysis results within or beside an analysed (e.g. segmented) scan.

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Related Research Areas

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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).
  • People
    • Donna Cox: Researcher in the field of scientific visualization
  • 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
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Information visualization

  • Bederson, Benjamin B., Shneiderman, Ben. The Craft of Information Visualization: Readings and Refelections, Morgan Kaufmann, 2003, ISBN 1558609156.
  • Card, Stuart K., Mackinlay, Jock D., Shneiderman, Ben. Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers, 1999, ISBN 1-55860-533-9.
  • William S. Cleveland (1993). Visualizing Data.
  • William S. Cleveland (1994). The Elements of Graphing Data.
  • Spence, Robert. Information Visualization, ACM Press, 2001, ISBN 0-201-59626-1.
  • Edward R. Tufte (1992). The Visual Display of Quantitative Information
  • Edward R. Tufte (1990). Envisioning Information.
  • Edward R. Tufte (1997). Visual Explanations: Images and Quantities, Evidence and Narrative.
  • Colin Ware (2000). Information Visualization: Perception for design.
  • Journal of Information Visualization


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External links

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Software

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Information v.

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Academic Conferences

One of the top academic conferences for new research in information visualization is the annually held IEEE Symposium on Information Visualization (InfoVis).

There is also the annually held International Conference on Information Visualization (IV).





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