Collaborative Research: CyberTraining: CIC: Framework for Integrated Research Software Training in High Energy Physics (FIRST-HEP)

协作研究：网络培训：CIC：高能物理综合研究软件培训框架 (FIRST-HEP)

• 批准号: 1829729
• 负责人: G J Peter Elmer
• 金额: $ 37.5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829729&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; CIC; Framework

High-energy physics (HEP) aims to understand the fundamental building blocks of nature and their interactions by using large facilities such as the Large Hadron Collider (LHC) at the European Laboratory for Particle Physics (CERN) in Switzerland and the Long-Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE) planned for the 2020s at Fermilab, in Illinois, as well as many smaller experiments. These experiments generate ever increasing amounts of data and rely on a sophisticated software ecosystem consisting of tens of millions of lines of code that is critical to mine this data and produce physics results. People are the key to developing, maintaining, and evolving this software ecosystem for HEP experiments over many decades. Building the necessary software requires a workforce with a mix of HEP domain knowledge and advanced software skills. The Framework for Integrated Research Software Training in High Energy Physics (FIRST-HEP) project provides a training path from a researcher's first steps through active contribution to software training and workforce development. 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 impacts STEM disciplines in terms of much needed and sought after software training.The FIRST-HEP project directly organizes training activities and works with partners to leverage and bring synergy to disparate existing efforts in order to maximize their collective impact. It brings together an extended set of partners from the community to build not only missing basic training elements like introductory programming skills in Python, git and Unix but also use of HEP data formats like ROOT and advanced topics including parallel programming, performance tuning, machine learning and data science for Ph.D. students. It works to build a community of instructors and experiments around the software training material and transforms the approach for research software training in HEP. It builds the workforce required for the cyberinfrastructure challenges of running and planned HEP facilities and experiments in the coming years.The FIRST-HEP education and training activities include specific goals to educate minorities in HEP, K- 12 educators and the broader STEM workforce. The K-12 teachers learn very basic skills of Unix including file management, programming languages, such as C+ and shell scripting. FIRST-HEP harnesses the potential of the underrepresented groups and works to ensure that the pool meets or exceeds the diversity in the larger HEP graduate student population when selecting both training participants and instructors for the HEP fundamental training sessions and the advanced computing schools. FIRST-HEP includes a dedicated outreach activity on cybertraining to the local Puerto Rico public. FIRST-HEP leverages engagement with the Software Carpentries to host training of K-12 teachers at UPRM in basic Software Carpentry skills and who in turn train their students. This encourages the teachers and school authorities to consider incorporating the basic carpentries into the high school curriculum. The training and cyber skills gained during the FIRST-HEP fundamental training courses directly contribute to the broader STEM workforce and trains students to pursue data science careers and other research areas besides HEP, such as Astronomy, where similar software skills are required.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.

高能物理学（HEP）旨在通过使用大型设施来了解自然的基本组成部分及其相互作用，例如瑞士欧洲粒子物理实验室（CERN）的大型强子对撞机（LHC）和计划于2020年代在伊利诺伊州费米实验室（Fermilab）的长基线中微子设施（LBNF）和深地下中微子实验（DUNE）以及许多较小的实验。这些实验产生了越来越多的数据，并依赖于一个复杂的软件生态系统，该系统由数千万行代码组成，对于挖掘这些数据并产生物理结果至关重要。在过去的几十年里，人是开发、维护和发展HEP实验软件生态系统的关键。构建必要的软件需要一个混合了HEP领域知识和高级软件技能的劳动力。高能物理综合研究软件培训框架（FIRST-HEP）项目提供了一条从研究人员的第一步到软件培训和劳动力发展的积极贡献的培训路径。该项目服务于国家利益，正如NSF的使命所述，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，并在急需和追求的软件培训方面影响STEM学科。FIRST-HEP项目直接组织培训活动，并与合作伙伴合作，利用现有的各种努力并发挥协同作用，以最大限度地发挥其集体影响。它汇集了来自社区的一系列合作伙伴，不仅可以构建缺少的基本培训元素，如Python，git和Unix的入门编程技能，还可以使用HEP数据格式，如ROOT和高级主题，包括并行编程，性能调优，机器学习和数据科学博士。学生它致力于围绕软件培训材料建立一个教师和实验社区，并改变了HEP中研究软件培训的方法。它建立了所需的人力资源，以应对未来几年运行和计划HEP设施和实验的网络基础设施挑战。FIRST HEP教育和培训活动包括教育HEP少数群体，K- 12教育工作者和更广泛的STEM劳动力的具体目标。K-12教师学习Unix的基本技能，包括文件管理，编程语言，如C+和shell脚本。FIRST-HEP利用代表性不足的群体的潜力，并致力于确保在为HEP基础培训课程和高级计算学校选择培训参与者和讲师时，人才库满足或超过更大的HEP研究生群体的多样性。第一个健康教育方案包括一项专门针对当地波多黎各公众的网络培训外联活动。FIRST-HEP利用与Software Carpentries的合作，在UPRM举办K-12教师的基本软件木工技能培训，并反过来培训学生。这鼓励教师和学校当局考虑将基本的carpentries纳入高中课程。在FIRST-HEP基础培训课程中获得的培训和网络技能直接有助于更广泛的STEM劳动力，并培养学生从事数据科学职业和HEP以外的其他研究领域，如天文学，其中需要类似的软件技能。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响力审查标准进行评估来支持。