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.
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 »
The Integrated Data Frameworks (IDF) group works in a wide variety of scientific domains, building tools and models to allow scientists to move from the “raw” data generated by simulations, experiments, and observations, to a combined quality-controlled view of the data that they can easily manipulate to gain scientific insight. Often this process will involve extensive work on “cleaning” and transforming data in custom data pipelines, and often there will also be a strong user interface component to either the pipelines or the final data products. Read More »
The Scientific Data Management (SDM) group develops technologies and tools for efficient data access and storage management of massive scientific data sets. We are currently developing storage resource management tools, data querying technologies, in situ feature extraction algorithms, along with software platforms for exascale data. The group also works closely with application scientists to address their data processing challenges. These tools and application development activities are backed by active research efforts on novel algorithms for emerging hardware platforms. Read More »
The User-Centered Systems Group is focused on usability aspects of computational and data analysis systems. UCS researchers are involved in three primary research and development mission areas: 1) User-centered design processes that work in scientific environments; 2) Usable scientific workflow tools and data abstractions, and; 3) Intuitive interfaces to explore, analyze, process data and run computations on HPC and distributed systems. Read More »
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 »
The Computational Biology Group is a collaboration between Berkeley Lab's Computational Research Division and the Biosciences Area. Our goals are to develop tools and frameworks to meet the analysis challenges posed by present and future biological research that addresses our nation's energy and health needs. Read More »