CyberTraining: Pilot: Justice in Data: An intensive, mentored online bootcamp developing FAIR data competencies in undergraduate researchers in the water and energy sectors

信息技术的迅猛发展在世界各地产生了前所未有的数据量，为研究人员提供了利用网络基础设施(CI)为全球问题开发数据驱动的解决方案的机会。然而，能源和水领域的研究人员往往装备不足，无法将CI纳入他们的工作流程。该项目通过为期一周的在线训练营、暑期指导和闭幕研究研讨会，为能源和水领域的本科生研究人员创造了一个独特的网络培训机会。学生在CI的方方面面接受培训，从数据访问和共享到数据分析和可视化，这是知识和发现的先驱。训练营和培训材料将增加可查找、可访问、可互操作和可重复使用(公平)数据原则在研究中的应用，鼓励向研究中的公正实践过渡。教师参与两个研讨会是对本科生研究人员任务的补充和延伸。该项目的总体目标是开发和测试课外CI教学框架，同时确定现有研究人员采用和实施CI的需求，从而完成NSF促进科学进步的使命。该项目旨在开发和测试一个可使用的框架和教学材料，以扩大各种初出茅庐的研究人员对传播与信息的采用，并扩大现有研究实验室对传播与传播技术的采用。为此，该项目为进行夏季水或能源研究的本科生创建了为期一周的强化公平数据原理和机器学习入门训练营，以创新的研究公正为主题，并提供了两个研讨会。训练营为CI新用户探索高影响力的主题，包括大规模数据访问、数据分析和数据可视化。对训练营参与者的指导将持续整个夏季，夏末在线研究研讨会为训练营参与者提供了一个论坛，描述他们如何(1)在夏季研究中应用公平原则，(2)为与研究有关的任务开发工作流程和工具，可将其发展成可出版的资源。这两个讲习班确定了在土木工程研究中扩大传播与信息技术的机会，并探讨了改进和扩大训练营内容和交付的方法。公平的训练营内容旨在鼓励学生内化内容并将其整合到他们的研究中，同时增加参与者与暑期研究同事分享他们新知识和技能的可能性。这种新的自下而上的传播CI知识的方法通过迅速增加具有CI能力的活跃研究人员的数量来加强未来的科学和工程劳动力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Dramatic development of information technology has produced an unprecedented volume of data around the world, providing an opportunity for researchers to leverage cyberinfrastructure (CI) to develop data-driven solutions to global issues. However, the research workforce in the energy and water fields are often under-equipped to incorporate CI into their workflows. This project creates a unique cybertraining opportunity for undergraduate researchers in the energy and water fields through an online, weeklong bootcamp, summer mentoring, and a closing research symposium. Students train in every facet of CI, from data access and sharing to data analytics and visualization, which are precursors to knowledge and discovery. The bootcamp and training materials will increase the application of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles in research, encouraging a transition towards just practices in research. Faculty participation in two workshops complement and extend the mission of the undergraduate researcher components. The overall goal of this project is to develop and test an extracurricular CI instructional framework while also identifying the CI adoption and implementation needs of the established research workforce, thus fulfilling NSF's mission to promote the progress of science. This project seeks to develop and test an accessible framework and instructional materials for expanding CI adoption among diverse, budding researchers and to broaden the adoption of CI in established research laboratories. To do so, the project creates an intensive weeklong FAIR data principles and introductory machine learning bootcamp with an innovative research justice theme for undergraduate students conducting summer research in water or energy and delivers two workshops. The bootcamp explores high-impact topics for new CI users, including large-scale data access, data analytics, and data visualization. Mentoring of bootcamp participants will continue throughout the summer, and an end-of-summer online research symposium provides a forum for bootcamp participants to describe how they (1) applied FAIR principles in their summer research and (2) developed workflows and tools for research-related tasks, which may be developed into publishable resources. The two workshops identify opportunities for expanding CI in civil engineering research and explore ways to improve and scale bootcamp content and delivery. The FAIR bootcamp content is designed to encourage students to internalize the content and integrate it into their research, while simultaneously increasing the potential for participants to share their new knowledge and skills with summer research colleagues. This novel bottom-up approach to spreading CI knowledge strengthens the future science and engineering workforce by rapidly growing the number of active researchers with CI competencies.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.