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Scientific Data Division
The Scientific Data Division (SciData) transforms data-driven discovery and understanding through the development and application of novel data science methods, technologies, and infrastructures with scientific partners.
About SciData
The Scientific Data Division fosters breakthrough discoveries through the application and development of novel data science methods, technologies, and infrastructures in partnership with science domain experts. Read More »
Machine Learning and Analytics
The Machine Learning & Analytics Group conceives, designs, and implements new methods in high-performance machine learning, data and image analytics, computational geometry and topology, and visualization technologies. Our work involves a mix of theoretical research and applied research. The group works in close collaboration with scientific partners to identify and address challenging, large-scale, data-rich problems emerging from simulations, experiments, and observations. Read More »
Usable Data Systems
The Usable Data Systems (UDS) group* performs research and development on software and methodology for improving the usability, quality, and security of scientific computing. The group is a mix of software engineers and computer scientists. We work with earth scientists, material scientists, chemists, process engineers, and physicists, as well as the NERSC and ESNet facilities. Read More »
Scientific Data Management
The Scientific Data Management (SDM) group enables and accelerates scientific discoveries through effective data management and analysis tools and libraries. The SDM group’s research and development efforts focus on (1) scalable storage and I/O strategies, (2) autonomous data management infrastructure, (3) data life-cycle management, and (4) workflow optimization and automation. Read More »
Integrated Data Systems
The Integrated Data Systems (IDS) group is focused on data integration and frameworks for computational and data analysis systems. We are involved in several important areas of data science including usable scientific workflow tools and data pipelines; intuitive interfaces and web science gateways to explore, analyze, process data and run computations on HPC and distributed systems; and data integration, standardization and curation, with an emphasis on Findable, Accessible, Interoperable, and Reusable (FAIR) data. Read More »
Sustainable Software Engineering
By constantly improving the practices and knowledge around sustainable software, the Sustainable Software Engineering Group aims to create a broad and supportive community of researchers and engineers performing state-of-the-art software development for all aspects of publicly funded science. Read More »
Computational Biosciences
The Computational Biosciences Group is a collaboration between Berkeley Lab's Computing Sciences Area and the Biosciences Area. Our goal is to innovate data analytics, data management, and statistical machine learning methods for experimental data and mechanistic models towards addressing our nation's energy, environment, and health needs. Read More »
Physics and X-Ray Science Computing Group
Physics and X-Ray Science researchers design, develop and deploy data analysis and exploration methods at scale to satisfy the computing needs of mission-critical experimental collaborations. Read More »
Computational Cosmology Center
C3 is a focused collaboration of astrophysicists and computational scientists whose goals are to develop the tools, techniques and technologies to meet the analysis challenges posed by present and future cosmological data sets. Read More »
