CAREER: Picturing Motion: Analyzing Multidimensional Time-Varying Data through Perceptually Accurate Exploratory Visualization

职业：描绘运动：通过感知准确的探索性可视化分析多维时变数据

• 批准号: 1054783
• 负责人: Daniel Keefe
• 金额: $ 46.7万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054783&HistoricalAwards=false
• 关键词: CAREER; Picturing; Motion; Analyzing; Multidimensional

The goal of this research project is to transform the way that complex time-varying scientific phenomena are analyzed using computers. The project makes possible a new style of aggregate and comparative analysis of large, high-resolution scientific motion databases. The approach combines the high-bandwidth channel of the human visual system with computational algorithms and interactive data exploration.The experimental research is integrated with educational efforts, including training computer scientist students in interdisciplinary research via a new course, a new writing-focused curriculum, and broadly disseminated lesson plans. Artists and designers are direct participants in the research, opening up new career paths in the sciences for visually creative students. Hands-on interactive visualizations engage 7-12th grade minority and underprivileged students in scientific computing. The results include experimentally grounded guidelines for perceptually accurate visualization, new techniques for coupling dimensionality reduction with illustrative data visualization, and tested human-computer interfaces for exploring spatially complex multidimensional data. These results are significant because they lead to new understandings of the coupled relationship between people and computing, specifically helping scientists move from vast, complex datasets to new insights. The broader impacts of the work lie in applications to multiple disciplines including musculoskeletal biomechanics, evolutionary biology, and geospatial science. Through specific applications, the work yields insights that could lead to improved treatments for spinal disorders and diseases in children and adults, and inform new theories of historical diversification among animals. Project results will be disseminated via open source software, videos, publications, and demos, all available on the project web site (http://www.cs.umn.edu/~keefe/NSFCAREER).

该研究项目的目标是改变使用计算机分析复杂的时变科学现象的方式。 该项目使得对大型高分辨率科学运动数据库进行新的聚合和比较分析成为可能。 该方法将人类视觉系统的高带宽通道与计算算法和交互式数据探索相结合。实验研究与教育工作相结合，包括通过新课程、新的以写作为重点的课程来培训计算机科学家学生进行跨学科研究，并广泛传播教学计划。 艺术家和设计师是研究的直接参与者，为视觉创意学生开辟了科学领域新的职业道路。 动手互动可视化让 7 至 12 年级的少数族裔和贫困学生参与科学计算。 结果包括以实验为基础的感知准确可视化指南、将降维与说明性数据可视化相结合的新技术，以及用于探索空间复杂多维数据的经过测试的人机界面。 这些结果意义重大，因为它们使人们对人与计算之间的耦合关系有了新的理解，特别是帮助科学家从庞大、复杂的数据集转向新的见解。 这项工作更广泛的影响在于其在多个学科的应用，包括肌肉骨骼生物力学、进化生物学和地理空间科学。通过具体的应用，这项工作产生的见解可能会改善儿童和成人脊柱疾病和疾病的治疗，并为动物历史多样化的新理论提供信息。 项目结果将通过开源软件、视频、出版物和演示进行传播，所有这些都可以在项目网站 (http://www.cs.umn.edu/~keefe/NSFCAREER) 上获取。