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

北极是地球上仅存的边疆之一，对地球的气候系统至关重要。气候变化和永久冻土变暖在整个北极地区都有记录，这种变暖释放的温室气体进一步推动了全球变暖。北极生态系统已经被推到了一个临界点，对内陆和沿海景观产生了巨大影响：土壤碳通量的改变、植被覆盖的变化、侵蚀、动物行为的变化以及对基础设施的挑战。由于这种通过永久冻土退化和海冰和湖泊融化将冰转化为水的现象在整个北极生态系统中回荡，对北极变化的理解需要从广泛的地球、工程和社会科学学科进行研究。有价值的气候、地质、生物和社会学数据存在，但尚未被北极科学界充分利用。人工智能（AI）和机器学习方法具有自动处理大数据和提取隐藏知识的能力，将使研究人员能够最大限度地利用这些数据来应对各种北极挑战。该项目将开发一个新的网络培训计划，以提高跨学科的无数北极研究人员在北极数据上使用人工智能驱动技术的能力。这些新技能将使当前和未来的北极科学家能够使用新一波数据驱动的发现工具，从而更好地了解快速变化的北极景观，这对社会福利至关重要。如今，人工智能已成为分析大数据的最强大工具之一，并成为数据驱动科学的新范式。然而，在目前的本科和研究生课程以及活跃的北极研究人员中，这些新兴主题的培训在很大程度上是缺失的。该项目将通过一系列相辅相成的培训活动培养数据科学技能，促进北极科学工作队伍的发展。将建立一个北极-人工智能研究网络，以收集人工智能培训需求，并使北极科学家和人工智能专家通过每月的网络研讨会系列分享想法和资源，相互联系，并体验最新的研究进展。将通过现场研讨会和在线自学课程提供定制培训，以扩大在北极科学中采用先进的人工智能方法。这些研讨会不仅对北极研究人员开放，也对北极科学教育工作者开放，为多个机构感兴趣的教师和讲师提供了将培训材料纳入课程和课堂教学的途径，扩大了网络培训活动的规模。与此同时，一项名为北极地球人工智能挑战赛（Arctic GeoAI Challenge）的公开竞赛将启动，作为一种新颖的实践技术培训形式，以吸引有才华的人开发新颖的人工智能解决方案，以解决现实世界的北极大数据问题。该招聘计划将培养包容性和多元化的社区文化，重点是培养更多女性、有色人种女性以及来自不同种族、学术背景和部门的人的STEM研究队伍，使北极土著社区特别是北极土著社区在理解和减缓北极变化方面拥有更大的发言权。所有培训材料将存储在北极数据中心的学习中心和永久冻土发现网关中，以确保所有北极科学和地球科学领域及其他领域的网络基础设施用户、专业人员和开发人员能够长期访问。该项目由地球科学理事会和先进网络基础设施办公室共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。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。

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.