Cybertraining: Pilot: Collaborative Research: Cybertraining for Earth Surface Processes Modelers

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Living sustainably on a rapidly changing planet is one of the greatest modern scientific and societal challenges. One critical aspect of global change involves the earth's surface itself: the rearrangement of its landforms, soils, and sediments by processes such as landslides, debris flows, floods, and coastal erosion. The Community Surface Dynamics Modeling System, CSDMS, creates cyberinfrastructure to enable advanced numerical models of the earth's surface, its changes through time, and the influence of human activity. However, traditional earth science education does not usually equip students with skills to become effective cyberinfrastructure users and cyberinfrastructure contributors. In order to develop innovative models for analyzing and predicting how the earth's surface responds to environmental change and human influence, the earth surface processes (ESP) modeling community needs a platform to teach modern programming practices and High Performance Computing methods. This project implements a 10-day Cyberinfrastructure in Earth Surface Processes Institute (ESPIn) for graduate students, postdoctoral fellows and early career faculty at the CSDMS Integration Facility at the University of Colorado in Boulder in the summers of 2020-2021 trains the next generation to be innovators. ESPIn aims to transcend the traditional model of department-based graduate education through interdisciplinary, problem-based, "Just in Time Teaching" of model use and development. Over forty participants, selected from diverse disciplinary backgrounds with explicit slots reserved for underrepresented minorities, gain direct experience in converting their research codes into open-source distributed software. ESPIn hosts developed lesson material in online open access educational repositories. ESPIn helps to train a new generation of computationally savvy, integrative scientists, while accomplishing major community science priorities. This project thus serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national prosperity and welfare by building a capable geoscience workforce.The Earth Surface Processes Institute (ESPIn) is a 10-day immersive experience for graduate students, postdoctoral fellows and early career faculty, allowing them to make advances on critical earth surface processes research questions with state-of-the-art modeling tools. This project targets learners who would benefit from critical knowledge, skills, and tools to become better cyberinfrastructure users and developers through a careful, inclusive selection procedure. This project aims to help make scientific advances in the study of Earth Surface Processes (ESP) that leverage the powerful and advanced capabilities of new cybertools, such as the Python Modeling Tool. To these ends, the primary objective is to expand the use of cyberinfrastructure among members of the ESP research community with training that (1) increases their competence and confidence with using cyberinfrastructure tools, methods, and resources and (2) moves the larger ESP community towards more widely adopting tools to advance the fundamental science of predicting surface change. Experienced scientists, visiting faculty, and software engineers assist with training and mentoring of the participants. ESPIn offers hands-on training in best programming practices, numerical methods, open source software development, advanced use of version control systems, writing unit tests, HPC-based sensitivity testing and model uncertainty quantification techniques. Several days are dedicated to working collaboratively on research and coding projects. Participants work on developing their own codes, with the intent of making codes more robust and compliant with existing ESP CI frameworks. The Summer Institute is quantitatively evaluated for learning efficacy and evaluations are used to iterate on lesson material quality. ESPIn provides all developed lesson material as online learning and teaching modules and broadly advertises these resources to the geoscience community.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.