Name
Abstract | ||
|---|---|---|
Empirical findings from studies in one scientific domain have very limited applicability to other domains, unless we formally establish deeper insights on the generalizability of task types. We present a domain-independent classification of visual analysis tasks with volume visualizations. This taxonomy will help researchers design experiments, ensure coverage, and generate hypotheses in empirical studies with volume datasets. To develop our taxonomy, we first interviewed scientists working with spatial data in disparate domains. We then ran a survey to evaluate the design participants in which were scientists and professionals from around the world, working with volume data in various scientific domains. Respondents agreed substantially with our taxonomy design, but also suggested important refinements. We report the results in the form of a goal-based generic categorization of visual analysis tasks with volume visualizations. Our taxonomy covers tasks performed with a wide variety of volume datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SciVis.2015.7429485 | SciVis |
Keywords | Field | DocType |
Task Taxonomy, Empirical Evaluation, Volume Visualization, Scientific Visualization, Virtual Reality, 3D Interaction | Data science,Generalizability theory,Spatial analysis,Categorization,Information visualization,Computer science,Visual analytics,3D interaction,Scientific visualization,Empirical research | Conference |
Citations | PageRank | References |
4 | 0.39 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bireswar Laha | 1 | 158 | 11.00 |
| Doug Bowman | 2 | 2681 | 194.49 |
| David H. Laidlaw | 3 | 1781 | 234.58 |
| John J Socha | 4 | 27 | 2.29 |