CyberTraining: Implementation: Small: COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure (COMPLECS)

网络培训：实施：小型：最终用户全面学习以有效利用网络基础设施 (COMPLECS)

• 批准号: 2320934
• 负责人: Robert Sinkovits
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320934&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Small; COMPrehensive; Learning

Advanced cyberinfrastructure (CI), comprising resources with capabilities beyond those of desktop computing, plays an important role in nearly every scientific and engineering discipline. Most CI users no longer need to write their own software, relying instead on existing robust 3rd party applications. Unfortunately, much of the available training still emphasizes software development topics rather than the skills other than programming that are needed to effectively use advanced CI. COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure (COMPLECS) addresses this gap by creating a coherent training program spanning foundational knowledge to the practical skills that enable these CI users to make best use of these specialized resources. The training consists of three tiers (multi-day workshops, webinars and self-paced study), which gives participants a variety of options that best fit into their schedules. Since workshop participation is limited, preference is given to students and educators at minority serving and primarily undergraduate institutions (MSIs and PUIs), and researchers from domains that have not traditionally used advanced CI. Webinars and self-paced learning are available to all. COMPLECS helps to build a better trained workforce with skills that make them more effective researchers, while also leading to improved utilization of NSF-funded CI resources.COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure (COMPLECS) helps CI Users acquire the skills and knowledge they need to efficiently accomplish their compute and data intensive research. Training topics include parallel computing concepts, intermediate Linux and shell scripting, hardware essentials, batch computing, data management, best practices in securing data and research, interactive computing, getting help and how to work effectively with user support, code migration and installation, high throughput computing and Linux tools for file processing. Except for several foundational topics, the program is designed so that webinar and self-paced study participants can take the training in any order. Materials are available through GitHub repositories, Google drives and captioned recordings of webinars. The evaluation plan consists of both formative components that allows the program to be continuously improved and summative components to ensure that the program is meeting its goals. COMPLECS makes a concerted effort to recruit participants from underrepresented groups though coordination with ACCESS, Campus Champions and other organizations.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.

先进的网络基础设施 (CI) 由具有桌面计算能力之外的资源组成，在几乎每个科学和工程学科中都发挥着重要作用。大多数 CI 用户不再需要编写自己的软件，而是依赖现有的强大的第三方应用程序。不幸的是，许多可用的培训仍然强调软件开发主题，而不是有效使用高级 CI 所需的编程以外的技能。最终用户有效利用网络基础设施的全面学习 (COMPLECS) 通过创建涵盖基础知识和实践技能的连贯培训计划来解决这一差距，使这些 CI 用户能够充分利用这些专业资源。培训包括三个层次（多日研讨会、网络研讨会和自定进度学习），为参与者提供了最适合其日程安排的各种选择。由于研讨会参与有限，因此优先考虑少数族裔服务机构和主要本科机构（MSI 和 PUI）的学生和教育工作者，以及传统上不使用高级 CI 的领域的研究人员。所有人都可以参加网络研讨会和自定进度的学习。 COMPLECS 有助于培养一支训练有素的员工队伍，其技能使他们成为更高效的研究人员，同时还可以提高 NSF 资助的 CI 资源的利用率。为最终用户有效利用网络基础设施而进行的 COMPLECS 学习 (COMPLECS) 可帮助 CI 用户获得高效完成计算和数据密集型研究所需的技能和知识。培训主题包括并行计算概念、中级 Linux 和 shell 脚本、硬件基础知识、批处理计算、数据管理、保护数据和研究的最佳实践、交互式计算、获取帮助以及如何有效地使用用户支持、代码迁移和安装、高吞吐量计算和用于文件处理的 Linux 工具。除了几个基础主题外，该计划的设计使网络研讨会和自定进度的研究参与者可以按任何顺序参加培训。材料可通过 GitHub 存储库、Google 驱动器和网络研讨会的字幕录音获得。评估计划由形成性部分和总结性部分组成，形成性部分使项目得以不断改进，总结性部分确保项目实现其目标。 COMPLECS 通过与 ACCESS、Campus Champions 和其他组织协调，共同努力从代表性不足的群体中招募参与者。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。