Collaborative Research: CyberTraining: Implementation: Small: Inclusive Cyberinfrastructure and Machine Learning Training to Advance Water Science Research

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Advanced cyberinfrastructure (CI) that emphasizes machine learning modeling, data analytics, cloud computing, and open and reproducible software practices is rapidly transforming water science. However, the water science user community and the current undergraduate and graduate curricula have not kept pace with this transformation. To address this gap, this project will train CI users and CI contributors through a recurring, two-week-long, immersive hackathon during a three-year project. By effectively integrating CI tools with analytics, machine learning, and cloud computing this project will create new, open-source CI modules and curriculum that can serve as templates to increase the adoption of advanced CI computational and data-driven models by a broad range of water science disciplines. Furthermore, this project will promote open, interoperable, reproducible, and accessible CI tools, allowing the scientific community to quickly integrate new findings and develop robust workflows. Faculty and scientists with complementary expertise in water science, engineering, computer science, and education will develop new CI training materials that are applicable to various water science disciplines. This project further aims to extend the data access and collaboration capabilities of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) by promoting and distributing the curriculum to the consortium’s 130-plus member universities. The cyber-training activities will increase water science research activities by (i) promoting holistic and open-source CI and analytics approaches, and (ii) empowering the next generation of researchers to overcome major bottlenecks in CI applications for water science. This project will increase diversity in water science, create state-of-the-art educational tools and curricula, and empower researchers and students to move from CI technology consumption to CI technology creation. It will use multiple diversity programs to recruit and broaden the participation of CI users and CI contributors from underrepresented groups across a broad range of types of educational institutions (HBCUs, PUIs, HSIs, R2s, and R1s).This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for 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.