Visualizing Uncertainty in Scientific Data Displays

这项研究的主要目标是设计改进的方法，将科学展示中的不确定性可视化。第二个目标是从数据和算法中识别和计算不确定性。几乎所有的科学数据都含有不确定性，这对于正确解释结果和原始数据一样重要。然而，很少有可视化技术描述不确定性。本研究将采取两种方法：将不确定性信息映射为附加数据和创建新的可视化原语和抽象。研究人员将使用实验、指标和应用专家(S)的评估来找到正确的技术组合。可视化是理解大量数据以及这些数据所代表的现象的宝贵工具。完整的数据规范应包括不确定性。由此产生的技术应该会显著改进所有需要考虑数据不确定性的可视化和图形应用程序。

The primary objective of this research is to design improved methods of visualizing uncertainty in scientific displays. A secondary objective is to identify and calculate uncertainty from data and algorithms. Almost all scientific data contain uncertainty, which are as important to the proper interpretation of the results as the original data. However, very few visualization techniques depict uncertainty. This research will take two approaches: mapping uncertainty information as an additional piece of data and creating new visualization primitives and abstractions. The researchers will use experimentation, metrics, and application expert(s evaluations to find the right combination of techniques. Visualization is a valuable tool in understanding large amounts of data and the phenomenon represented by these data. Complete specification of data should include uncertainty. The resulting techniques should significantly improve all visualization and graphics applications where data uncertainty is a concern.