We have a data problem. Humanity is now generating more data than it can handle; more sensors, smartphones, and devices of all types are coming online every day and contributing to the ever-growing global dataset.
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
Liam Collins was drawn to study physics to understand “hidden things” and honed his expertise in microscopy so that he could bring them to light.
A typhoon strikes an island in the Pacific Ocean, downing power lines and cell towers. An earthquake hits a remote mountainous region, destroying structures and leaving no communication infrastructure behind.
Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.
While Tsouris’ water research is diverse in scope, its fundamentals are based on basic science principles that remain largely unchanged, particularly in a mature field like chemical engineering.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
The National Alliance for Water Innovation, a partnership of the Department of Energy’s Oak Ridge National Laboratory, other national labs, university and private sector partners, has been awarded a five-year, $100 million Energy-Water Desalination Hub by DOE to address water security issues in the United States.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.