CyberTraining: CIP: Training and Developing a Research Computing and Data CI Professionals (RCD-CIP) Community

今天的科学和工程研究界本质上是跨学科的，并且不断超越单一学科的界限，通常不仅需要这些学科的深度，而且需要跨越这些学科的知识广度。特别是，科学对先进计算资源和专业人员的需求日益增长，这些专业人员可以作为科学研究和先进计算基础设施或网络基础设施（CI）之间的桥梁。该项目通过培训和单独指导一批跨学科研究CI专业人员（idr - cip）来认识和支持这些复杂的需求，这些专业人员使用获得的技术能力和软技能与研究团队成功合作。IDR-CIP受训者来自社区，包括：领域科学家、跨学科研究专业人员、教师和学生。IDR-CIP学员被纳入研究项目，使他们有机会发展CI技能，从而成为更大社区的一部分。该培训计划通过以下核心活动培养有经验的cip，包括：制定个性化培训计划；进行培训周期，以支持新的、现有的和返回的学员；将学员纳入参与机构的项目和计划；指导科研项目匹配；赞助教员研究员计划；通过赞助IDR-CIP受训者参与促进社区发展；并倡导学员成为CIP职位的全额资助人员。该项目的一个成果包括新的IDR-CIP劳动力职业道路的定义，这些职业道路反映了他们对研究企业的重要性，并有可能影响NSF生态系统中研究的完成方式。招募过程积极地接触到各种代表性不足的社区，从而推进了NSF扩大科学和工程参与的目标的一个重要方面。拥有这样的网络基础设施专业人员来支持研究人员及其日益复杂的计算需求，对我们国家的竞争力至关重要。2022年，美国国家科学基金会（NSF）将“增长融合研究”列为其十大创意之一。融合研究将专家们联系在一起，共同解决需要广泛而多样的知识、方法、专业知识、科学学科和CI能力的问题。然而，缺乏跨学科的网络基础设施（CI）专业人员（cip）是发展的障碍。该项目的主要目标是通过制定与每个受训者的能力和背景相一致的培训计划来减轻这一障碍。该项目对CIP劳动力培训的教学挑战的一个重要贡献是围绕四个主要要素阐述和组织培训：1)招聘和培训：战略性地接触新社区；制定一个灵活的培训周期，以支持新的、现有的和返回的学员；为CIP培训项目创建一个精心策划的培训材料集合；2)科学与工程项目匹配和指导：基于CIP技能集，确定科学与工程项目，并将每个CIP与导师匹配；将学员连接到NSF ACCESS计算科学支持网络（csn）；3)科研咨询：指导和跟踪学员向独立顾问阶段的过渡；通过确定合适的职业道路，促进CIP学员过渡到全职专业职位；4)建立CIP社区：通过会议和社交媒体联系项目CIP学员；促进与其他CIP和网络培训项目的互动；赞助受训者成为培训师并加入其他HPC社区。该培训计划填补了CI研究生态系统中的一个重要空白，并通过创建具有适当职称和职业路径的新CIP职位，帮助实现融合研究，并将CIP转换为完全资助的员工职位。CIP培训计划将提高新社区对CI资源的认识，为他们更多地融入研究项目提供途径，并支持CI的长期战略发展。CIP培训材料将通过一个公共网站公开提供给NSF以外的网络工作人员。由于拟议的合作和推广，该计划将影响成千上万的用户，并帮助培养下一代CIP研究人员。该奖项反映了美国国家科学基金会的法定使命，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。

Today’s science and engineering research community is interdisciplinary in nature and continually evolving beyond the boundaries of single disciplines, often requiring not only depth but also breadth of knowledge across those disciplines. In particular, science has a growing need for advanced computing resources and professionals who can serve as a bridge between the worlds of scientific research and advanced computing infrastructure, or cyberinfrastructure (CI). This project recognizes and supports these complex needs by training and individually mentoring a cohort of Interdisciplinary Research CI Professionals (IDR-CIPs) who use acquired technical competencies and soft skills to work successfully with research teams. The IDR-CIP trainees are drawn from communities including: domain scientists, interdisciplinary research professionals, faculty, and students. IDR-CIP trainees are embedded into research projects, giving them the opportunity to develop CI skills, and as a result, become part of a larger community. The training program produces experienced CIPs using the following core activities, including: developing individualized training programs; conducting training cycles that support new, current, and returning trainees; embedding trainees within the participating institution’s projects and programs; mentoring scientific research project matching; sponsoring a Faculty Fellows program; fostering communities through sponsoring IDR-CIP trainee participation; and advocating for trainees to become fully funded staff in CIP positions. An outcome of this project includes the definition of new IDR-CIP workforce career paths that reflect their importance for the research enterprise and has the potential to impact how research is done in the NSF ecosystem. The recruitment process actively reaches out to a variety of underrepresented communities, thereby advancing an important aspect of NSF’s goal to broaden participation in science and engineering. It is vital for the competitiveness of our nation to have such cyberinfrastructure professionals available to support researchers and their increasingly complex computational needs.In 2022, the NSF named Growing Convergence Research as one of their 10 Big Ideas. Convergence research interconnects experts who work together to solve problems that require a broad and diverse set of knowledge, methods, expertise, scientific disciplines, and CI capabilities. However, the lack of interdisciplinary cyberinfrastructure (CI) professionals (CIPs) presents a barrier to advancement. This project's main goal is to mitigate this barrier by developing a training program that aligns with the competencies and backgrounds of each individual trainee. An important contribution of this project to the pedagogical challenge of training a CIP workforce is to articulate and organize training around four major elements: 1) Recruitment & Training: strategically reach out to new communities; develop a flexible training cycle that supports new, current and returning trainees; create a curated collection of training materials for CIP training programs; 2) Science & Engineering Project Matching and Mentoring: Based in CIP skillset, identify S&E projects and match each CIP with a mentor; connect trainees to the NSF ACCESS Computational Science Support Network (CSSN); 3) Scientific Research Consulting: Mentor and track trainees as they transition to an independent consultant phase; facilitate CIP trainee transition to full time professional positions by identifying appropriate career paths; 4) Fostering a CIP Community: connect program CIP trainees through meetings and social media; foster interactions with other CIP and CyberTraining programs; sponsor trainees to become trainers and to join other HPC communities. This training program fills an important gap in the CI research ecosystem and helps enable convergence research through the creation of new CIP positions with appropriate job titles and career pathways, and transition CIPs to fully funded staff positions. The CIP training program will raise awareness of CI resources in new communities, provide an avenue for their increased integration into research projects, and support long-term strategic CI development. CIP training materials will be publicly available to reach beyond the NSF cyber workforce via a public website. As a result of the proposed collaboration and outreach, this program will impact thousands of users and help develop the next generation of the CIP research workforce.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.