11 to 20 of 812 Results
May 18, 2026 - Department of Computer Science
Riya Ghate; Jingran Chen; Aparna Kishore; Madhav V Marathe, 2026, "AI-Enabled Synthesis of Open Source Multi-Attribute, Temporal Dataset Related to Data Centers in Virginia", https://doi.org/10.18130/V3/AYLB4S, University of Virginia Dataverse, V2, UNF:6:Xfi38fbR1I/lROEteajzow== [fileUNF]
Data centers are among the fastest-growing loads on the U.S. electrical grid. Virginia, the world's largest data center market, is an ideal testbed for analyzing energy and infrastructure impacts. However, researchers and policymakers lack open, consistent facility-level data for effective regional planning and impact analysis. Existing datasets ar... |
May 14, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2026, "Synthetic Population for California, US (ver. 2.5.0)", https://doi.org/10.18130/V3/A7DQWM, University of Virginia Dataverse, V1, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of California is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (... |
Apr 30, 2026 - School of Engineering and Applied Science
Zhu, Yuanhang; Ormonde, Pedro C.; Liu, Leo; Pan, Yu; Westfall, Elizabeth; Han, Tianjun; Zhu, Joseph; Zhong, Qiang; Bart-Smith, Hilary; Dong, Haibo; Lauder, George V.; Moored, Keith W.; Quinn, Daniel B., 2025, "Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions", https://doi.org/10.18130/V3/UL6CJE, University of Virginia Dataverse, V4
Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions. |
Apr 30, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2024, "Synthetic Population for Washington, US (ver. 2.4.0)", https://doi.org/10.18130/V3/PNGMRJ, University of Virginia Dataverse, V2, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of Washington is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (... |
Apr 30, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2025, "Synthetic Population for Georgia, US (ver. 2.4.0)", https://doi.org/10.18130/V3/4DWNRH, University of Virginia Dataverse, V2, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of Georgia is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (HIS... |
Apr 29, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2025, "Synthetic Population for Massachusetts, US (ver. 2.4.0)", https://doi.org/10.18130/V3/ZB0SGL, University of Virginia Dataverse, V2, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of Massachusetts is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispani... |
Apr 29, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2025, "Synthetic Population for Minnesota, US (ver. 2.4.0)", https://doi.org/10.18130/V3/SB2PWT, University of Virginia Dataverse, V2, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of Minnesota is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (H... |
Apr 28, 2026 - Biocomplexity Institute & Initiative
Mortveit, Henning S.; Adiga, Abhijin; Baek, Hannah; Bhattacharya, Parantapa; Eubank, Stephen; Machi, Dustin; Marathe, Madhav; Porebski, Przemyslaw; Swarup, Samarth; Venkatramanan, Srinivasan; Wilson, Mandy; Xie, Dawen, 2024, "Synthetic Population for Virginia, US (ver. 2.4.0)", https://doi.org/10.18130/V3/5LSDCY, University of Virginia Dataverse, V2, UNF:6:i+YB7Rre5h9cv3VjPNxfew== [fileUNF]
The synthetic population for the state of Virginia is constructed to be statistically indistinguishable from the real population at the spatial resolution of a US block group as measured by the US Census on selected demographic variables, which in this case are age (AGEP), household income (HINCP), household size (NP), race (RAC1P) and hispanic (HI... |
Apr 26, 2026 - LibraData: UVa's Scholarly Research
Das, Sree Sourav, 2026, "Optimizing Machine Learning Approaches to Explore Binary Metallic Alloys for Active Cooling Applications", https://doi.org/10.18130/V3/7NYJAE, University of Virginia Dataverse, V1
Data as a part of data sharing for journal publication |
Apr 22, 2026 - School of Engineering and Applied Science
Fang, Bin, 2026, "A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions", https://doi.org/10.18130/V3/O58DEE, University of Virginia Dataverse, V3
The dataset includes GeoTIFF raster layers representing the predicted probability of each ordinal class. Files are named using a standardized convention that encodes the WASH component and ordinal class name. |
