CI-TEAM Implementation Project: Enabling Interactive Visual Exploration and Remote Collaboration for the Geosciences and Physical Sciences

随着地球和物理科学越来越依赖于通过大规模观测和高性能计算获得的超大型、复杂的数据集，科学家必须更好地准备好理解和使用网络基础设施工具(CI)进行数据分析、解释和呈现。这就需要能够使用和开发可视化方法和协作工具的科学家。与此同时，计算机科学家有效地开发CI需要对围绕科学探究的上下文、数据和问题的性质有深刻的洞察。在加州大学戴维斯分校计算机科学家、地球科学家和物理学家已建立的合作基础上，该项目将培训和指导下一代计算机科学家和领域科学家使用一系列可视化方法和协作工具，用于地质制图、地幔动力学模拟和矿物结构分析等各种科学问题。在现有研究工具的基础上，多学科学生团队将开发信息教育工具，并准备以地球科学和物理为重点的基于信息技术的教学模块。其目标是通过以下方式在教育系统的不同层次上吸引和教育不同的学生群体使用、支持、开发和设计地球科学CI：开发、使用、评估和传播灵活、高质量和用户友好的科学数据可视化和分析；指导学生CI研究，并为高年级本科生和研究生提供CI课程；招聘和培养多样化的科学家队伍，以便在他们未来的大学、工业、政府或K-12教育的职业生涯中充分利用CI；以及，创建有吸引力和可访问性的计算环境和可视化内容，以引起公众对CI和地球科学数据可视化的兴趣。

As the earth and physical sciences rely more and more on extremely large, complex datasets obtained via large scale observational efforts and high-performance computations, scientists must be better prepared to understand and work with cyberinfrastructure tools (CI) for data analysis, interpretation, and presentation. This requires a demand for scientists who are able to use and develop visualization methods and collaboration tools. At the same time, effective development of CI by computer scientists requires deep insight into the nature of the context, data, and questions surrounding scientific enquiry. Building on an established collaboration of computer scientists, geoscientists, and physicists at UC Davis, this project will train and mentor the next generation of computer scientists and domain scientists in a series of visualization methods and collaboration tools used for scientific problems as diverse as geologic mapping, mantle dynamics simulations and mineral structural analysis. Expanding on the extant research tools, multidisciplinary teams of students will develop CI educational tools and prepare CI-based instructional modules focused on geosciences and physics. The goal is to interest and educate a diverse student population in using, supporting, developing, and designing geoscience CI at various levels of the educational system by: Developing, using, assessing, and disseminating flexible, production-quality and user-friendly scientific data visualization and analysis; mentoring student research in CI and providing CI courses for upper division undergraduate and graduate students; recruiting and preparing a diverse workforce of scientists to take full advantage of CI in their future careers in universities, industry, government, or K-12 education; and, creating attractive and accessible computational environments and visualization content to interest the general public in CI and visualization of geosciences data.