Abstract
The analysis cycle of observational data is expanded by visualization. To increase scientific productivity in the data analysis process it is necessary for the visualizations (visual data representations) to be expressive and effective. Additionally, the use of tools to visualize data must allow the scientist to concentrate on exploring scientific data rather than tackling a complex software environment. The paper also introduces STAR (Scientific Toolkit for Astrophysical Research), a software system that is designed to increase the productivity of astrophysicists through visualization.
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References
ADAMS, J.L. (1986), Conceptual Blockbusting,Addison Wesley, third edition.
CHERNOFF, H. (1973), The Use of Faces to Represent Points in k-Dimensional Space Graphically, J. Am. Statistical Assoc., Vol. 68, Nr. 342, 361–368.
HABER, R.B., and MCNABB, D.A. (1990), Visualization Idioms: A Conceptual Model for Scientific Visualization Systems, in: in Scientific Computing, eds. G.M. NIELSON and B. SHRIVER, IEEE Computer Society Press, 74–93.
FISCHER, G. (1989), The Importance of Models in Making Complex Systems Comprehensible, Proceedings of the 8th Interdisciplinary Workshop on Informatics and Psychology, Schaerding, Austria.
IEEE Computer (1989), Issue on Scientific Visualization, IEEE Computer, Vol. 22, No. 8.
MACKINLAY, J. (1986), Automating the Design of Graphical Presentations of Relational Information, ACM Trans. Graphics, Vol. 5, No. 2, 110–141.
MCKIM, R.H. (1980), Experiences in Visual Thinking,PWS Publishers, second edition.
MAURER, H., and CARLSON, P. (1990), Computer Visualization, a Missing Organ, and CyberEquivalency, IIG - Report No. 295, Institutes for Information Processing Graz.
NSF (National Science Foundation’s) Advisory Panel on Graphics (1986), Image Processing, and Workstations, Visualization in Scientific Computing–A Synopsis, IEEE Comp. Graphics and applications, Vol. 7, No. 7, 61–70.
PICKET, R.M., and GRINSTEIN, G.G. (1988), Iconographics Displays for Visualizing Multidimensional Data, in: Proceedings of the 1988 IEEE Conference on Systems, Man and Cybernetics, Vol. I, eds. Beijing and Shenyang, People’s Republic of China, 514–519.
ROBERTSON, P.K. (1990), A Methodology for Scientific Data Visualization, Choosing Representations Based on a Natural Scene Paradigm, Proceedings of the Conference on Visualization ‘80, San Francisco, 114–123.
TREINISH, L.A. (1989), An interactive, discipline-independent data visualization system, Computers in Physics July/August, 55–64.
TREINISH, L.A. (1990), Interactive Visualization Requirements for Scientists,presented at the Panel on Interaction Issues in Visualization at the Conference on Visualization ‘80, San Francisco.
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© 1992 Springer-Verlag Berlin · Heidelberg
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Domik, G., Mickus, K.D. (1992). Visualization in the Analysis Cycle of Observational Data. In: Schader, M. (eds) Analyzing and Modeling Data and Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46757-8_27
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DOI: https://doi.org/10.1007/978-3-642-46757-8_27
Publisher Name: Springer, Berlin, Heidelberg
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