Difference between revisions of "SMHS SciVisualization"
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===Questions=== | ===Questions=== | ||
| − | * How and why should we “look” at data? | + | |
| − | * What data characteristics are important for exploratory data analytics (EDAs)? | + | *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: | Scientific Data-driven or Simulation-driven visualization methods may be classified in many alternative ways. Visualization techniques can be classified according to many criteria: | ||
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<center>[[Image:SMHS_SciVisualization1.png|500px]] </center> | <center>[[Image:SMHS_SciVisualization1.png|500px]] </center> | ||
| − | + | *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 | |
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| − | |||
| − | |||
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| − | + | *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 | ||
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http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html | http://www.socr.umich.edu/CSCD/html/Cores/Macore2/SciViz.html | ||
| − | + | ==[[SMHS_SciVisualization_SOCR_Excel_R_Charts|SOCR, Excel and R Charts]]== | |
| − | + | ==[[SMHS_SciVisualization_NetworkViz|Complex Network Visualization]]== | |
Latest revision as of 08:26, 23 May 2016
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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