CyberTraining: CIU:Cross-disciplinary Training for Findable, Accessible, Interoperable, and Reusable (FAIR) science

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Addressing the grand challenges associated with growing population, food and water security, frequently occurring natural disasters, and changing climate requires not only geoscience domain expertise, but also computational (cyber) skills to deal with big data analytics and simulations. However, many challenges exist in providing cyber training to students, including steep learning curve for instructors, time commitment and lack of resources in terms of training material. This project creates cyber training programs for earth science students and working professional in the form of modules or labs for use in an existing course, an independent one-credit course, summer workshops and boot camps. Students receive training in running state-of-the-art numerical models, and in analyzing massive simulated and observational data sets with advanced data analysis tools. The overall goal is to create a new generation of scientists to manage data-rich and computationally intensive tasks to become globally competitive in the STEM, thus fulfilling NSF's mission to promote the progress of science. The training component of the project will make the science openly available and transparently reproducible using the best practices in Findable, Accessible, Interoperable, and Reusable (FAIR) science. The estimated number of trainees from this project is approximately 200 students at Purdue and potentially more than 400 students, faculty and working professionals outside Purdue.This project brings cyber-enabled state-of-the-art computational tools into practice by training students and working professionals through courses, workshops and boot camps at multiple institutions, including Purdue University, University of New Hampshire and University of Alabama. The project creates a cyber training curriculum that is driven by the need to acquire expertise in the following six areas: data access, geo-processing, time series analysis, computational simulation, visualization and publication. These areas form the foundation of a modular cyber training framework that supports development and implementation of training materials targeting geoscience learners. The innovation lies in enhancing geosciences curriculum to include cyber training by providing a range of flexible training options for students and working professionals. A FAIR Cyber Training (FACT) Fellowship will enhance computational thinking among graduate students and create an avenue to tap the most talented students to train the next generation of cyber savvy earth scientists. This interdisciplinary collaboration from hydrology, climate science, computer science and instruction design expertise enables holistic training covering both domain science and computational technology.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.