SMHS SciVisualization
Contents
Scientific Methods for Health Sciences - Scientific Visualization
Questions
- How and why should we “look” at data?
- What data characteristics are important for exploratory data analytics (EDAs)?
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:
- Examining the composition of the data
- Exploring the distribution of the data
- Contrasting or comparing several data elements, relations, association
- 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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