National Center For Ecological Analysis and Synthesis
UC Santa Barbara’s National Center For Ecological Analysis and Synthesis (NCEAS) supports cross-disciplinary research that uses data to address major fundamental issues in ecology and related fields, and encourages the application of science to management and policy. NCEAS is a unique institution with an explicit mission to foster synthesis and analysis, turn information into understanding, and—through effective collaboration—alter how science is conducted. Its success is evident in the broad impact of its research and programs. This includes improving access to data, promoting a culture of scientific collaboration, and building the capacity of the scientific community through unique training initiatives.
Master's in Environmental Data Science
In 2021, the Bren School of Environmental Science and Management launched a Master's in Environmental Data Science (MEDS), an 11-month professional degree program focused on using data science to advance solutions to environmental problems.
The program immerses students in a collaborative, open-source, data-driven environment and supports them with expert faculty and researchers from the Bren School, the National Center of Ecological Analysis & Synthesis (NCEAS), and other UCSB departments. Students supplement their background in environmental science with leading-edge data science skills; receive in-depth training in applications such as R, Python, and SQL; and learn how to apply data visualization to share compelling scientific narratives. In the MEDS program, students learn robust and reproducible workflows, develop interfaces and documentation, and complete a capstone group project that provides real-world experience in applying data science to environmental issues.
The mission of the Earth Research Institute (ERI) is to support research and education in the sciences of our solid, fluid, and living Earth. While the scope of ERI research spans the breadth of Earth and Environmental sciences, the institute is organized around four major themes of Natural Hazards, Human Impacts, Earth System Science, and Earth Evolution. Each of these themes has been transformed in the past decade by both the increasing availability of high-resolution spatio-temporal data, and the emergence of complex physically-based modeling approaches for characterizing the dynamics of Earth and Environmental systems. ERI faculty and researchers are taking advantage of this convergence of big data and large-scale modeling to catalyzed new discoveries and understanding across campus, while ERI research computing staff are developing new infrastructure and data management tools for handling these computationally intensive approaches.
Environmental Studies and Hydrological Sciences
More and more, students in UCSB's Environmenal Studies Program are utilizing data science for their research and coursework. The interdisciplinary majors offered in ES require integrated perspectives across the natural sciences, social sciences, and humanities, as well as biology, physics, and calculus. An understanding of programming and statistics, as well as data visualization and inference is key to tackling the world's current and future environmental challenges. This is especially the case in the Hydrological Sciences and Policy, B.S. major, where students receive the training needed to understand and solve complex hydrologic problems at local, regional, and global levels, and learn to analyze the data that will inform policy decisions.
Cheadle Center for Biodiversity and Ecological Restoration
CCBER fulfills the UC Santa Barbara mission of research, education, and public service through stewardship and restoration of campus lands, preservation and management of natural history collections, and through learning experiences and programs that offer unique opportunities for students of all ages.
Smart Farms and the Internet of Things
The UCSB SmartFarm project is a computer science led effort focused on the use of data science with “The Internet of Things” (IoT) to improve sustainable agriculture, water usage and land management. Using novel systems and data science applications developed in the RACE Lab in the Computer Science Department, researchers have improved the analysis techniques commonly used by agronomists to analyze soil surveys. They have also been investigating new, data science based approaches to frost prevention and differential irrigation as ways of both saving water and sustaining agricultural yields.
UCSB data science research also enables scientists and citizen scientists to automatically classify wildlife images gathered from ecological preserves for use my local and statewide land management agencies and environmental researchers. The “Where’s the Bear?” project combines new computer science and data science research with open source analysis software with IoT systems and public clouds to monitor wildlife in remote areas.
The Institute for Energy Efficiency (IEE) is an interdisciplinary research institute dedicated to cutting-edge science and technologies that support an energy-efficient and sustainable future. IEE’s research activities leverage the considerable expertise of UCSB’s highly acclaimed faculty, scientists, engineers and researchers. By fostering collaborations, sponsoring research, and expediting the commercialization of new technologies, IEE is a key driver of significant advances in energy efficiency.
The Center for Spatial Studies focuses on promoting spatial thinking and spatial analytics across academia, industry, and government agencies, and across disciplines ranging from the humanities to the physical sciences with a particular focus on novel Spatial Data Science methods and Knowledge Graphs.
The center has expertise in spatiotemporally-explicit machine learning, in the formal representation of spatial phenomena including but not limited to geographic space, knowledge engineering, as well as in methods to improve the publication, retrieval, reuse, and integration of heterogeneous data across domain boundaries.
Movement Data Science
UCSB's MOVE Lab develops scalable computational data analytics, simulation, and prediction models to study movement and its relationship to environmental and geographic contexts. Our research advances context-aware data analytics and movement models by integrating information captured from heterogeneous and high dimensional tracking data sets.