dst.lbl.govData Science and Technology at Lawrence Berkeley National Lab

Data Science and Technology Lawrence Berkeley National LabHome Groups Staff Projects Software Publications Links General Information Ten Usability Principles AmeriFlux Network Fluxdata FLUXNET International Soil Carbon Network Materials Project TCP Tuning Guide Bulk Transfer Guide Interactive Frog Cybersecurity for Science and Energy Delivery Medical Science DMZ For Members Private Wiki OverviewThe Data Science and Technology () Department - a part of the Computational Research Division (CRD) at Berkeley Lab - delivers leading-edge, innovative methods for solving data-intensive science problems. activities range from basic and applied research to deployment of software tools. Our projects span a diverse set of activities, including: data management; data movement; cybersecurity; machine learning, statistical, topological, and geometric analysis/analytics; computer vision; visualization; user-interface design; usability; end-to-end data-intensive system architecture and deployment.We focus on conceiving, developing, and applying leading-edge, innovative methods for solving data-intensive science problems. Our multidisciplinary teams are engaged on projects in five primary mission areas: Scientific workflows and data analysis algorithms and frameworks Data synthesis, management, movement, and curation of large and complex datasets User-centered design of interfaces and software High-performance machine learning, data analytics, and visualization capabilities Cybersecurity for science and energy Our collaborators are from across the science disciplines, ranging from theoretical astrophysicists to computational and experimental bioscientists. The capabilities we build are driven by the needs of contemporary computational, observational, and experimental science projects central to the mission of the DOE Office of Science. Our portfolio includes projects in basic and applied research, advanced software development, and deployment to the scientific community. The science challenges we are helping to understand include: understanding carbon interactions between the atmosphere and the biome, interpreting results from trillion-particle space weather simulations, detecting extreme weather events in climate models, locating halo particles in accelerator models, understanding organism function, and detecting blobs in fusion experiments while the data is in transit. more...Latest News Latest news on and other CRD and LBL projects are available at Computational Research Division New Page and Berkeley Lab News Center May 20, 2019 National Academies Taps Cholia for Scientific Workflows Committee group lead, Shreyas Cholia, has been invited by the National Academies of Sciences, Engineering, and Medicine to serve as a member for the Realizing Opportunities for Advanced and Automated Workflows in Scientific Research committee. The expert committee will conduct a study examining current efforts to develop advanced and automated workflows for scientific research to identify promising research approaches to accelerate progress in the utilization of workflow systems and tools. Read more in LBNL CS News . December 4, 2018 Berkeley Lab Cybersecurity Specialist Highlights Data Sharing Benefits, Challenges at NAS Meeting member Sean Peisert presented at a meeting of the National Academies’ Committee on Science, Engineering, Medicine, and Public Policy (COSEMPUP) about strategic use and combination of computer security and privacy-preserving techniques to overcome certain data-sharing barriers, and ways in which such techniques serve as a means to facilitate, enhance, and create incentives for increased data sharing in the sciences - thereby accelerating data-driven scientific discovery. Read more in LBNL CS News . November 20, 2018 Berkeley Lab Researchers to Build Standards for Neurophysiology Data $2 Million NIH Grant to Fund Neurodata Without Borders Project member Oliver Reubel along with a large team of other researchers will receive $2 million from the National Institutes of Health (NIH) to develop a next-generation data format and software ecosystem for the Neurodata Without Borders: Neurophysiology (NWB:N) project. NWB:N is a consortium of researchers and foundations that are interested in breaking down obstacles to data use and sharing in neuroscience. The group ultimately aims to standardize neurophysiology data on an international scale to ensure the success of brain research worldwide and accelerate the pace of scientific discovery. Read more in LBNL CS News . November 7, 2018 SENSEI Showcased at SC18 CRD scientists at SC18 are showcasing SENSEI, a lightweight software infrastructure that enables simulations to make use of a wide array of popular in situ analysis and visualization packages. In situ, or in place” data analysis and visualization will be critical to the success of exascale simulations. SENSEI decouples simulation codes from the specifics of any particular analysis or visualization package. Codes calling on SENSEI’s interface can switch out or combine the capabilities of multiple in situ packages. at SC18, CRD staff working on SENSEI, including Burlen Loring, Wes Bethel and Gunther Weber, will present a paper describing recenlty added support for the popular Python programming language. Read more in CRD News at Berkeley Lab . September 24, 2018 At Biden Summit, CRD’s Ushizima Discusses Using Machine Learning to Improve Cancer Detection Dani Ushizima, a staff scientist in Lawrence Berkeley National Laboratory’s Computational Research Division (CRD) who has adapted algorithms used in materials research to scan for cervical cancer, described her research in a panel discussion at the Sept. 21 East Bay Biden Cancer Community Summit. A common thread in each speaker’s presentation was the importance of federally funded research in developing new methods to prevent and treat cancer. The meeting was sponsored by East Bay Congressman Mark DeSaulnier, himself a cancer survivor, in conjunction with a Cancer Community Summit hosted by former Vice President Joe Biden in Washington, D.C., through his Biden Cancer Initiative. Read more in CRD News at Berkeley Lab . July 3, 2018 Staff Play Integral Role in ISC18 member Sean Peisert presented his work on " Cyber Security Challenges and Opportunities in High Performance Computing Environments " at the 2018 International Supercomputing Conference (ISC-18) in Frankfurt, Germany, on June 26, 2018 First held in 1986, ISC High Performance is the oldest conference and networking event for the high performance computing (HPC) community. It offers a five-day technical program focusing on HPC technological development and its application in scientific fields and commercial environments. Read more in Today at Berkeley Lab and LBNL CS News . June 2018 Berkeley Lab Researchers Use Machine Learning to Search Science Data Data Science and Technology Department () personnel along with National Energy Research Scientific Computing Center (NERSC) personnel have been developing new ways of developing metadata for science data. As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. Today, search engines are ubiquitously used to find information on the Internet but searching science data presents a different set of challenges. For example, Google’s algorithm relies on more than 200 clues to achieve an effective search. The lack of real metadata labels eventually causes problems when the scientist tries to find the data later or attempts to share it with others,” says Lavanya Ramakrishnan, a staff scientist in Berkeley Lab’s Computational Research Division (CRD) and co-principal investigator of the Science Search project. But with machine-learning techniques, we can have computers help with what is laborious for the users, including adding tags to the data....