Difference between revisions of "SMHS SciVisualization"
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<li>How and why should we “look” at data?</li> | <li>How and why should we “look” at data?</li> | ||
<li>What data characteristics are important for exploratory data analytics (EDAs)?</li> | <li>What data characteristics are important for exploratory data analytics (EDAs)?</li> | ||
Revision as of 10:58, 18 May 2016
Contents
Scientific Methods for Health Sciences - Scientific Visualization
Questions
Scientific Data-driven or Simulation-driven visualization methods may be classified in many alternative ways. Visualization techniques can be classified according to many criteria:
• Data Type: structured/unstructured, small/large, complete/incomplete, time/space, ascii/binary, Euclidean/non-Euclidean, etc.
• Task type: Task type is one of the aspects considered in classification of visualization techniques, which provides means of interaction between the researcher, the data and the display software/platform
• Scalability: Visualization techniques are subject to some limitations, such as the amount of data that a particular technique can exhibit
• Dimensionality: Visualization techniques can also be classified according to the number of attributes
• Positioning and Attributes: the distribution of attributes on the chart may affect the interpretation of the display representation, e.g., correlation analysis, where the relative distance among the plotted attributes is relevant for observation
• Investigative Need: the specific scientific question or exploratory interest may also determine the type of visualization:
o Examining the composition of the data
o Exploring the distribution of the data
o Contrasting or comparing several data elements, relations, association
o Unsupervised exploratory data mining
http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html
SOCR, Excel and R Charts
Complex Network Visualization
- SOCR Home page: http://www.socr.umich.edu
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