UniversityofWashington_SyracuseUniversity

Intelligent Earth Computational and Data Science Methods for Research

The biosphere, geosphere, and cybersphere are changing fast, with massive datasets of earth and environmental observations, opportunities to compute enormously complex physical process calculations, and deep social implications for action based on predictions of coupled human and natural systems. Our future depends on how we apply our collective intelligence, creativity, and attention to detail while data mining, optimizing parameters, and using machine learning techniques to interactively build community research software, open data, and online learning experiences that increase the societal benefits of our scientific research. This module uses peer review research to develop case studies that demonstrate the utility of new techniques in data analytics as applied to wicked earth problems, spatiotemporally dynamic environmental datasets, and designed for accessibility for geoscientists and engineers at all levels of computer science experience. Specifically, students will learn how to 1) streamline research workflows with Jupyter Notebooks by accessing publicly available computational resources for hydrologic science, 2) lead and contribute to a Learning Community advancing Findable, Accessible, Interoperable, Reproducible (FAIR) standards; 3) transform and translate research questions, data, and code between geoscience domains, data science, and computer science using R and Python; 4) use transdisciplinary informatics, computational and data science to understand critical Earth and Environmental Science challenges with authentic tasks; and 5) build on infrastructure design methods that result in computational narratives that amplify new perspectives, increase curiosity, and communicate new insights.