CyberTraining: Implementation: Small: Enabling Dark Matter Discovery through Collaborative Cybertraining

网络培训：实施：小型：通过协作网络培训实现暗物质发现

• 批准号: 2017506
• 负责人: Andrew Renshaw
• 金额: $ 16.21万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017506&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Small; Enabling; Dark

Detecting dark matter in the lab would be transformational for physics, and such a difficult measurement requires providing a foundation for early-career scientists in advanced data analytics. The science question being pursued is generally acknowledged to be one of the most important questions in particle physics and astrophysics and is key to understanding what makes up the vast majority of the universe. Effective training in good computing practices is required for major research advances in this field. The project will consolidate and strengthen training efforts in scientific software development and data analysis within the field of experimental dark matter research. Scientifically, the training will enable discovery that will come from a world-wide effort consisting of hundreds of junior scientists searching for extremely-rare events on petabytes of data - effectively looking for a needle in a haystack the size of Texas. The project serves the national interest as stated by NSF's mission to promote the progress of science by preparing a workforce trained in cyberinfrastructure, and will support STEM disciplines with critical software training that is much needed both in scientific fields and in industry.The dark matter community consists of more than a thousand scientists at the frontier of ultra-rare event searches whose efforts support more than twenty different experiments. Searching for dark matter in multiple ways has resulted in disparate and often inadequate computational training. This project addresses the training problem to maximize impact across the field. Representing three leading dark matter experiments, the project investigators will develop educational material and training workshops for systematic data science education to ensure early career scientists can harness the data volumes being produced by modern experiments. The project will host two training workshops per year, toward the goal of developing a community of instructors and also a set of training materials for free distribution and reuse. Beyond domain-specific training in rare-event searches, foundational computational knowledge will be developed when necessary by working with partners such as the Software and Data Carpentries. The project includes specific goals to engage women and underrepresented minorities in the training activities and broaden their advancement within the field. Additionally, the project will provide mentors for advanced students through hackathons. These trainings will directly contribute to broader STEM workforce development while training students such that they can pursue careers in data science and/or data-intensive research. This project is funded by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Physics in the Directorate for Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

检测实验室中的暗物质将是物理学的转化，因此这种困难的测量需要为高级数据分析中的早期职业科学家提供基础。 通常认为要提出的科学问题是粒子物理和天体物理学中最重要的问题之一，并且是理解构成宇宙绝大部分的关键。 在该领域的重大研究进展中，需要进行良好计算实践的有效培训。 该项目将在实验性暗物质研究领域内整合和加强科学软件开发和数据分析的培训工作。 从科学上讲，该培训将带来来自全球努力的发现，该努力由数百名初级科学家组成，这些科学家在数据的数据中寻找极其稀有的事件 - 有效地寻找德克萨斯州大小的干草堆中的针头。 NSF的使命旨在通过准备一名在网络基础设施中培训的劳动力来促进科学进步的使命，该项目旨在促进科学的进步，并将通过关键的软件培训来支持STEM学科，在科学领域和行业中，这在科学领域和行业中都非常需要。暗物质社区组成了超过一千个在超级领域的搜索范围内的努力，而不是二十个不同的实验。 以多种方式搜索暗物质导致了不同的计算训练。该项目解决了培训问题，以最大程度地发挥整个领域的影响。 该项目调查人员代表三个领先的暗物质实验，将开发有关系统数据科学教育的教育材料和培训研讨会，以确保早期职业科学家可以利用现代实验生产的数据量。 该项目每年将举办两个培训研讨会，以建立一个教师社区以及一套免费分发和重复使用的培训材料。除了在罕见的搜索中进行特定于领域的培训外，还将在必要时与软件和数据木匠等合作伙伴合作时开发基础计算知识。 该项目包括使妇女和代表性不足的少数群体参与培训活动的具体目标，并扩大其在该领域的进步。 此外，该项目将通过黑客马拉松为高级学生提供导师。这些培训将直接有助于更广泛的STEM劳动力发展，同时培训学生可以从事数据科学和/或数据密集型研究的职业。 该项目由计算机和信息科学和工程局的高级网络基础设施办公室以及数学和物理科学局的物理部门资助。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子优点和更广泛的影响来审查审查的审查标准来通过评估来获得支持。