@energy

U.S. Department of Energy

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class="content__text"
 VISUALIZATION: When a scientist has an overwhelming amount of data, it can be useful to put it in a visual form. At the beginning of the COVID-19 pandemic, pathologists in New Orleans were making high-resolution 3D microscopic scans to understand the virus. However, the data sets were too large for them to analyze on their own. With the help of Brian Summa, a Tulane University researcher, they analyzed it effectively.

Now, with the help of an Early Career award from the Department of Energy’s Office of Science, Summa is making it easier for all researchers to visualize their data. The systems he's developing will make it easier for scientists to search, analyze, and share even the biggest datasets. 

The first part of the project is a scientific version of Google’s reverse image search. Instead of relying on the descriptive tags researchers attach to images, the program will allow scientists to search within the images. Using machine learning, the program will structurally analyze the images. It will also allow scientists to look for patterns across data in different areas of research. Summa’s team will be beta-testing it with datasets from DOE’s Lawrence Livermore National Laboratory and the National Renewable Energy Laboratory. 

The second part is finding ways to prioritize the key parts of data to make it easier to transfer very large datasets. Currently, most programs send a lower-resolution version of the data. This system will identify and emphasize key relationships in the data instead. 

Lastly, his team is developing large visualization software systems that will be easy for researchers to use. 

One thing that is unique about Summa’s project is that he’s designing it to run on different technologies. Instead of only working on very powerful supercomputers, his team is aiming to run it on systems as common as iPads. 

Image description: A swirl of blue waves cutting through an orange background. It's a visualization of a mixing transition in a Rayleigh-Taylor instability, which is when a light fluid pushes on a heavy one.
Credit: Duong Hoang, Brian Summa, Pavol Klacansky, Will Usher, Harsh Bhatia, Peter Lindstrom, Peer-Timo Bremer, Valerio Pascucci

class="content__text" VISUALIZATION: When a scientist has an overwhelming amount of data, it can be useful to put it in a visual form. At the beginning of the COVID-19 pandemic, pathologists in New Orleans were making high-resolution 3D microscopic scans to understand the virus. However, the data sets were too large for them to analyze on their own. With the help of Brian Summa, a Tulane University researcher, they analyzed it effectively. Now, with the help of an Early Career award from the Department of Energy’s Office of Science, Summa is making it easier for all researchers to visualize their data. The systems he's developing will make it easier for scientists to search, analyze, and share even the biggest datasets. The first part of the project is a scientific version of Google’s reverse image search. Instead of relying on the descriptive tags researchers attach to images, the program will allow scientists to search within the images. Using machine learning, the program will structurally analyze the images. It will also allow scientists to look for patterns across data in different areas of research. Summa’s team will be beta-testing it with datasets from DOE’s Lawrence Livermore National Laboratory and the National Renewable Energy Laboratory. The second part is finding ways to prioritize the key parts of data to make it easier to transfer very large datasets. Currently, most programs send a lower-resolution version of the data. This system will identify and emphasize key relationships in the data instead. Lastly, his team is developing large visualization software systems that will be easy for researchers to use. One thing that is unique about Summa’s project is that he’s designing it to run on different technologies. Instead of only working on very powerful supercomputers, his team is aiming to run it on systems as common as iPads. Image description: A swirl of blue waves cutting through an orange background. It's a visualization of a mixing transition in a Rayleigh-Taylor instability, which is when a light fluid pushes on a heavy one. Credit: Duong Hoang, Brian Summa, Pavol Klacansky, Will Usher, Harsh Bhatia, Peter Lindstrom, Peer-Timo Bremer, Valerio Pascucci

April 27, 2023

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