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工作和高性能计算获得的超大型复杂数据集，科学家必须更好地准备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。这需要能够使用和开发可视化方法和协作工具的科学家的需求。与此同时，计算机科学家有效地开发CI需要深入了解围绕科学探究的背景、数据和问题的本质。该项目建立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的既定合作基础上，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来解决地质测绘，地幔动力学模拟和矿物结构分析等科学问题。扩展现有的研究工具，多学科的学生团队将开发CI教育工具，并准备以地球科学和物理学为重点的基于CI的教学模块。我们的目标是兴趣和教育不同的学生群体在使用，支持，开发和设计地球科学CI在各级教育系统：开发，使用，评估和传播灵活的，生产质量和用户友好的科学数据可视化和分析;指导学生研究CI和提供CI课程的高年级本科生和研究生;招募和准备一支多元化的科学家队伍，以便在大学，工业，政府或K-12教育的未来职业生涯中充分利用CI;并创建有吸引力和可访问的计算环境和可视化内容，以吸引公众对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.