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

在实验室中检测暗物质将是物理学的变革，而这样一个困难的测量需要为高级数据分析的早期职业科学家提供基础。 科学问题被普遍认为是粒子物理学和天体物理学中最重要的问题之一，也是理解宇宙绝大多数组成的关键。 要在这一领域取得重大研究进展，就必须进行良好计算做法方面的有效培训。 该项目将巩固和加强在暗物质实验研究领域内科学软件开发和数据分析方面的培训工作。 从科学的角度来看，培训将使来自全球范围的努力的发现成为可能，该努力由数百名年轻科学家组成，他们在PB级的数据中搜索极其罕见的事件-有效地在德克萨斯州大小的干草堆中寻找针。 该项目服务于国家利益，正如NSF的使命所述，即通过培养一支受过网络基础设施培训的劳动力队伍来促进科学进步，暗物质社区由1000多名处于超罕见事件搜索前沿的科学家组成，他们的努力支持了20多个不同的实验。 以多种方式搜索暗物质导致了不同的，往往是不充分的计算训练。该项目解决培训问题，以最大限度地扩大对整个外地的影响。 代表三个领先的暗物质实验，项目研究人员将为系统的数据科学教育开发教育材料和培训研讨会，以确保早期职业科学家能够利用现代实验产生的数据量。 该项目将每年举办两次培训讲习班，以发展一个教师社区和一套免费分发和重复使用的培训材料。除了稀有事件搜索领域的特定培训外，必要时还将通过与软件和数据Carpentries等合作伙伴合作开发基础计算知识。 该项目包括让妇女和代表性不足的少数群体参与培训活动并扩大其在该领域的地位的具体目标。 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，使他们能够从事数据科学和/或数据密集型研究。 该项目由计算机和信息科学与工程理事会高级网络基础设施办公室和数学与物理科学理事会物理部资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.