Collaborative Research: SI2-SSI: Inquiry-Focused Volumetric Data Analysis Across Scientific Domains: Sustaining and Expanding the yt Community

合作研究：SI2-SSI：跨科学领域以调查为中心的体积数据分析：维持和扩展 yt 社区

• 批准号: 1663914
• 负责人: Matthew Turk
• 金额: $ 106.17万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663914&HistoricalAwards=false
• 关键词: Collaborative; Research; SI2; SSI; Inquiry

Scientific discovery across the physical sciences is increasingly dependent on the analysis of volumetric - or three-dimensional - data, that may come from a supercomputer simulation, direct measurement, or mathematical models. Researchers typically seek to extract meaningful insights from this data by visualizing and analyzing it in various ways. The ways in which scientists process volumetric data are actually quite similar across domains, but cross-disciplinary knowledge transfer and tool development is blocked by barriers of terminology. This project seeks to enhance an analysis and visualization toolkit named yt that is currently primarily used for astrophysical simulations. yt allows scientists to access and analyze data at several different levels by providing an interface that is designed to answer questions motivated by the underlying scientific problem, while worrying less about details such data formats, specific analysis techniques etc. yt's utilization in computational astrophysics has dramatically increased access to advanced algorithms for both visualization and analysis, and fostered the growth of a community of researchers sharing techniques and results. This project seeks to make yt available and adopted by scientists in other domains of science thus reproducing its success in astrophysics in these other science domains. This project will expand the yt community beyond theoretical astrophysics and enable and promote collaboration and advanced data analysis in the fields of meteorology, seismology and global tomography, observational astronomy, hydrology and oceanography, and plasma physics. Improvements to the yt project will proceed along four principal technical avenues. The first is to develop a system that adapts the way yt presents data via a set of domain contexts that encode the ontology, domain-specific vocabulary, and common analysis tasks for a given field of study. This will include creating a domain context system as well as a set of five pilot domain contexts developed in collaboration with domain practitioners. The second is to overhaul the yt field system, adding more versatility and enabling significant optimizations. Thirdly, the project team will implement non-spatial indexing schemes, providing methods for accessing and analyzing data that is not organized according to the standard spatial axes. The final improvement will be the development of a non-local analysis system, allowing generalized path traversal as well as domain convolutions. To ensure wide dissemination and use of these improved capabilities, the team will design domain-specific documentation and training materials, and organize outreach and training events for early-career researchers. This will consist of both hands-on technical workshops and curricula developed in collaboration with Data Carpentry for utilization at other institutions. This combination of technical developments and social investments has been designed to ensure both readiness of the software and engagement of the targeted research communities.

整个物理科学的科学发现越来越依赖于对体积或三维数据的分析，这些数据可能来自超级计算机模拟、直接测量或数学模型。研究人员通常试图通过以各种方式可视化和分析这些数据来从这些数据中提取有意义的见解。不同领域的科学家处理体积数据的方式实际上非常相似，但跨学科的知识转移和工具开发却受到术语障碍的阻碍。该项目旨在增强名为 yt 的分析和可视化工具包，该工具包目前主要用于天体物理模拟。 yt 允许科学家通过提供一个界面来访问和分析多个不同级别的数据，该界面旨在回答由潜在科学问题引发的问题，同时减少对数据格式、特定分析技术等细节的担忧。yt 在计算天体物理学中的利用显着增加了对用于可视化和分析的高级算法的访问，并促进了共享技术和结果的研究人员社区的发展。该项目旨在使 yt 可供其他科学领域的科学家使用并采用，从而在这些其他科学领域复制其在天体物理学方面的成功。该项目将把 yt 社区扩展到理论天体物理学之外，并促进和促进气象学、地震学和全球层析成像、观测天文学、水文学和海洋学以及等离子体物理学等领域的合作和高级数据分析。 yt 项目的改进将沿着四个主要技术途径进行。第一个是开发一个系统，通过一组领域上下文来适应数据呈现方式，这些领域上下文对给定研究领域的本体、特定领域词汇和常见分析任务进行编码。这将包括创建一个领域上下文系统以及与领域从业者合作开发的一组五个试点领域上下文。第二个是彻底检修 yt 现场系统，增加更多的多功能性并实现重大优化。第三，项目团队将实施非空间索引方案，提供访问和分析不按照标准空间轴组织的数据的方法。最终的改进将是开发非局部分析系统，允许广义路径遍历和域卷积。为了确保广泛传播和使用这些改进的功能，该团队将设计特定领域的文档和培训材料，并为早期职业研究人员组织外展和培训活动。这将包括实践技术研讨会和与 Data Carpentry 合作开发的课程，供其他机构使用。这种技术开发和社会投资的结合旨在确保软件的准备就绪和目标研究社区的参与。

项目成果

unyt: Handle, manipulate, and convert data with units in Python

unyt：在 Python 中使用单位处理、操作和转换数据

• DOI: 10.21105/joss.00809
• 发表时间: 2018-08
• 期刊: Journal of Open Source Software
• 作者: J. Goldbaum, Nathan;A. ZuHone, John;J. Turk, Matthew;Kowalik, Kacper;L. Rosen, Anna
• 通讯作者: L. Rosen, Anna