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Paul Abraham uses mass spectrometry to study proteins.

Systems biologist Paul Abraham uses his fascination with proteins, the molecular machines of nature, to explore new ways to engineer more productive ecosystems and hardier bioenergy crops.

A structural model of HgcA, shown in cyan, and HgcB, shown in purple, were created using metagenomic techniques to better understand the transformation of mercury into its toxic form, methylmercury. Photo credit: Connor Cooper/ORNL, U.S. Dept of Energy

A team led by ORNL created a computational model of the proteins responsible for the transformation of mercury to toxic methylmercury, marking a step forward in understanding how the reaction occurs and how mercury cycles through the environment.

The CrossVis application includes a parallel coordinates plot (left), a tiled image view (right) and other interactive data views. Credit: Chad Steed/Oak Ridge National Laboratory, U.S. Dept. of Energy

From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.

Ecosystem Ecologist Verity Salmon captures and analyzes field data from sites in in Alaska and Minnesota to inform earth system models that are being used to predict environmental change.

While some of her earth system modeling colleagues at ORNL face challenges such as processor allocation or debugging code, Verity Salmon prepares for mosquito swarms and the possibility of grizzly bears.

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In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.

Researchers discovered the Tennessee cavesnail, Antrorbis tennesseensis, in caves near Oak Ridge National Laboratory. The snail measures in at less than 2 millimeters long. Credit: Nathaniel Shoobs and Matthew Niemiller

Sometimes conducting big science means discovering a species not much larger than a grain of sand.

Edge computing is both dependent on and greatly influencing a host of promising technologies including (clockwise from top left): quantum computing; high-performance computing; neuromorphic computing; and carbon nanotubes.

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.

Examples from the ORNL Overhead Vehicle Dataset, generated with images captured by GRIDSMART cameras. Image: Thomas Karnowski/ORNL

Each year, approximately 6 billion gallons of fuel are wasted as vehicles wait at stop lights or sit in dense traffic with engines idling, according to US Department of Energy estimates.

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Researchers across the scientific spectrum crave data, as it is essential to understanding the natural world and, by extension, accelerating scientific progress.

An artist rendering of the SKA’s low-frequency, cone-shaped antennas in Western Australia. Credit: SKA Project Office.

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.