1 to 10 of 751 Results
May 7, 2026 - Center for Advanced Medical Analytics (CAMA)
Kausch, Sherry, 2026, "Replication Data for: Modeling Heart Rate Patterns to Quantify Neonatal Opioid Withdrawal Syndrome", https://doi.org/10.18130/V3/RLOW6S, University of Virginia Dataverse, V1, UNF:6:Q1kdLvKSzbc3fN44Nn0s2A== [fileUNF]
Anonymized data set for replication of study findings. |
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. |
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. |
