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Scientific- (or data-), and Information visualization are branches of computer graphics and user interface which are concerned with the presentation of interactive or animated digital images to users to understand data. For example, scientists 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.

Usage and distinction of the terms

In common usage, the slightly more general term information visualization is used to encompass 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[1]. 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.


Visualization, in the presentation sense, is not a new phenomenon. It has been used in maps, scientific drawings, and data plots for over a thousand years. Examples of this are the map of China (1137 a.d.) and the famous map of Napoleon's invasion of Russia in 1812, by Jacque Minard. Most of the concepts learned in devising these images carry over in a straight forward manner to computer visualization and can be incorporated in courses in visualization. Edward Tufte has written two excellent books which explain many of these principles.

Computer Graphics has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the special issue of Computer Graphics on Visualization in Scientific Computing. Since then there have been several conferences and workshops, co-sponsored by the IEEE and ACM SIGGRAPH, devoted to the general topic, and special areas in the field, for example volume visualization. There have also been numerous books and research articles on visualization in the past several years.

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.

Models and frameworks for building visualizations include the data flow models popularized by systems such as AVS, IRIS Explorer, and VTK toolkit, and data state models in spreadsheet systems such as the Spreadsheet for Visualization and Spreadsheet for Images.

In the medical and life sciences

Desktop programs capable of presenting interactive models of molecules and microbiological entities are becoming relatively common (Molecular graphics). 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.

Related Research Areas


  • 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).
    • Illuminating the Path: The R&D Agenda for Visual Analytics, James J. Thomas & Kristin A. Cook, Editors (2005)
  • 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

Information visualization

  • Bederson, Benjamin B., Shneiderman, Ben. The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann, 2003, ISBN 1-55860-915-6.
  • 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.
  • Leland Wilkinson. "The Grammar of Graphics", Springer ISBN 0-387-24544-8 [2]
  • 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

External links


Information visualization


  • The Digital Magazine of by Juan C. Dürsteler (Spanish | English)
  • VAC Views - the Visualization and Analytics Centers Periodical: research updates in the field of visual analytics.

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).

Researchers in visualization

In alphabetical order:

  • Remo Aslak Burkhard
  • Stuart Card
  • Ed H. Chi
  • Donna Cox
  • Mary Czerwinski
  • Jeff Heer
  • Bill Hibbard
  • Alan Keahey
  • Bongshin Lee
  • Jock Mackinlay
  • George Robertson
  • Ben Shneiderman
  • Robert Spence
  • John Stasko
  • Bongwon Suh
  • Colin Ware
  • Chris Weaver
  • Ji Soo Yi

Educational visualization | Interactive visualization | Knowledge visualization | Product visualization | Scientific visualization
Related fields
Computer graphics | Computer science | Graphic design | Graphic image development | Human-computer interaction | Informatics | Visual communication
See also
Data mining | Gestalt psychology | Graph theory | Graphic organizer | Illustration | Imaging | Information graphic | Information graphic designers | List of graphical methods | List of graphing software | Representation (arts) | Representation (psychology) | Rendering (computer graphics)

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