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This image from Sept. 30, 2022, shows how the Federal Emergency Management Agency used ORNL's USA Structures data along with new satellite images to identify structures that were destroyed in Lee County, Florida, during Hurricane Ian. Credit: ORNL, U.S. Dept. of Energy

Deep learning underlies geographic dataset used in hurricane response

Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters. This dataset provides a consistent, nationwide accounting of the buildings where people reside and work.

A simulation of the planet from the DOE Energy Exascale Earth System Model, one of the large-scale models incorporated in the Earth System Grid Federation led by DOE’s Oak Ridge, Argonne and Lawrence Livermore national laboratories. Credit: LLNL, U.S. Dept. of Energy

Project launched to contribute upgrades to Earth System Grid Federation global climate data system

The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades.

Data from different sources are joined on platforms created by ORNL researchers to offer better information for decision makers. Credit: ORNL/Nathan Armistead

Data science teams analyzed COVID-19 data for early pandemic response

When the COVID-19 pandemic stunned the world in 2020, researchers at ORNL wondered how they could extend their support and help

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

VA, ORNL and Harvard develop novel method to identify complex medical relationships

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.