Visualizing Uncertainty in Scientific Data Displays

可视化科学数据显示中的不确定性

• 批准号: 9423881
• 负责人: Alex Pang
• 金额: $ 37.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9423881&HistoricalAwards=false
• 关键词: Visualizing; Uncertainty; Scientific; Data; Displays

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.

本研究的主要目的是设计改进的方法，可视化的不确定性在科学显示。 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包含不确定性，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。 这项研究将采取两种方法：映射不确定性信息作为一个额外的数据和创建新的可视化原语和抽象。 研究人员将使用实验、指标和应用专家的评估来找到正确的技术组合。 可视化是理解大量数据和这些数据所代表的现象的有价值的工具。 完整的数据说明应包括不确定性。 由此产生的技术应显着改善所有的可视化和图形应用程序的数据不确定性是一个问题。