NRT-DESE: Environment and Society: Data Science for the 21st century (DS421)

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Global environmental change poses critical environmental and societal needs. This National Science Foundation Research Traineeship (NRT) award prepares master's and doctoral students at the University of California - Berkeley with tools to advance frontiers at the intersection of social, natural, and data science. Trainees will develop skills in data visualization, informatics, software development, and science communication. Through an innovative team-based problem-solving course, trainees will collaborate with an external partner organization to tackle a challenge in global environmental change that includes a significant problem in data analysis and interpretation of impacts and solutions. A new generation of researchers and professionals interested in issues of environmental change will be trained in the emerging practices of open science, which will create fully reproducible and collaborative analyses that document the evidence for scientific conclusions and their implications for policy. This training program will advance progress on grand challenges of national and global importance. The research theme of this program addresses grand challenges at the junction of open science and reproducible data analysis, responses of coupled human-natural systems to rapid environmental change, and development of evidence-based solutions. Trainees will research topics such as management of water resources, regional land use, and responses of agricultural systems to changes in economic and climate factors. Trainees will master the skills needed to evaluate how rapid environmental change impacts human and natural systems and to develop and evaluate solutions in public policy, resource management, and environmental design that will mitigate negative effects on human well-being. Trainees will learn to apply these skills examining the integrated dynamics of coupled human-natural systems from local to global scales.