41 to 50 of 835 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 - 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 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, "Code for modeling WASH components by RTMB", https://doi.org/10.18130/V3/ED1WNW, University of Virginia Dataverse, V3
The repository contains two R code files: data preprocessing and model/prediction/evaluation files. |
Apr 21, 2026 - School of Medicine
Rosenthal, Eric S.; Kamaleswaran, Rishikesan; Strekalova, Yulia Levites; Williams, Andrew E.; Cordes, Ashley; Williams, Ishan C.; Rashidi, Parisa; Evans, Barbara J.; Moorman, J. Randall; Park, Soojin; Amorim, Edilberto; Vespa, Paul M.; Jiang, Xiaoqian; Liu, Hongfang; Podgoreanu, Mihai V.; Herasevich, Vitaly; Wainwright, Mark; Barros, Andrew J.; Sullivan, Brynne A.; Young, Michael J.; Alvarez, Marta; Talapova, Polina; Muszynski, Jennifer A.; Gent, Alasdair; Bold, Delgersuren; Kwong, Manlik; Gunda, Dileep; Le, Jackie; Loar, India; Gow, Brian J.; Schmidt, Heidi; Houghtaling, Jared; Miller, Robert T.; Clark, Timothy; Ashe, William B.; Pan, Tony C.; Pollard, Tom J.; McCrary, Ciera; Bihorac, Azra; Hu, Xiao; Clermont, Gilles, 2026, "Data Manifest for Collaborative Hospital Repository Uniting Standards (CHoRUS) April 2026", https://doi.org/10.18130/V3/XNBOPG, University of Virginia Dataverse, V1
Access Protected Enclave Data: http://chorus4ai.org/dataset The Collaborative Hospital Repository Uniting Standards (CHoRUS) for Clinical Care AI is a clinical data network developing a flagship multi-hospital, multimodal, high-resolution dataset under the NIH Bridge2AI program. CHoRUS harmonizes electronic health record (EHR) data, imaging, high-r... |
Apr 21, 2026 - School of Engineering and Applied Science
Gardella, Nicholas, 2026, "Audio PhD Dissertation of Nicholas Gardella | Responsible and Equitable Use of AI Code Generators in Computer Science Education", https://doi.org/10.18130/V3/VM1IPO, University of Virginia Dataverse, V1
This is the archival audiobook version of the PhD Dissertation of Nicholas Gardella, Responsible and Equitable Use of AI Code Generators in Computer Science Education. |
