Collaborative Research: CyberTraining: Implementation: Medium: Cyber2A: CyberTraining on AI-driven Analytics for Next Generation Arctic Scientists

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The Arctic is one of the Earth's remaining frontiers that is critical to the Earth's climate systems. Climate change and permafrost warming are documented across the Arctic, with such warming releasing greenhouse gasses that further drive global warming. The Arctic ecosystem has been pushed to a tipping point with dramatic impacts to inland and coastal landscapes: altered soil carbon fluxes, changes in vegetation cover, erosion, shifts in animal behavior, and challenges to infrastructure. As this transformation of ice to water through degrading permafrost and melting sea and lake ice reverberates through the entire Arctic ecosystem, understanding of Arctic change necessitates research from a broad range of Earth, engineering, and social science disciplines. Valuable climatic, geological, biological, and sociological data exist but have yet to be fully exploited by the Arctic science community. Artificial Intelligence (AI) and machine learning approaches, which have the ability to automatically process big data and extract hidden knowledge, would enable researchers to make the best possible use of these data to address diverse Arctic challenges. This project will develop a novel cybertraining program to increase the capacity for myriad Arctic researchers across disciplines to employ AI-driven techniques on Arctic data. These new skills will enable current and future Arctic scientists to use the new wave of data-driven discovery tools and thereby better understand the rapidly changing Arctic landscape, which is critically needed for societal welfare.Today, Artificial Intelligence has become one of the most powerful tools to analyze big data and enable a new paradigm of data-driven science. However, training in these emerging topics is largely missing in current undergraduate and graduate curricula, as well as for active Arctic researchers. This project will foster the growth of an Arctic science workforce by developing data science skills through a series of complementary and mutually reinforcing training activities. An Arctic-AI research network will be established for collecting AI training needs and for Arctic scientists and AI experts to share ideas and resources, to network with each other, and to experience the latest research advances through a monthly webinar series. Customized training will be provided through both in-person workshops and online, self-paced learning programs to broaden the adoption of advanced AI methods in Arctic science. The workshops will be open not only to Arctic researchers, but also to the Arctic science educators, offering a pathway for interested faculty and instructors at multiple institutions to incorporate training materials into their curricula and classroom teaching, amplifying the scale of the cybertraining activities. Meanwhile, an open competition, the Arctic GeoAI Challenge, will be launched as a novel form of hands-on technology training to attract talented individuals to develop novel AI solutions for solving a real-world Arctic big data problem. The recruitment plan will cultivate an inclusive and diverse culture of community, with a strong focus on growing the STEM research workforce with more women, women of color, and people from diverse ethnic groups, academic backgrounds, and sectors, enabling especially the Arctic indigenous community to have a greater voice in understanding and mitigating Arctic change. All training materials will be deposited in the Arctic Data Center's Learning Hub and the Permafrost Discovery Gateway to ensure long-term access by cyberinfrastructure users, professionals, and developers across all Arctic science and geoscience domains and beyond. This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.