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1 to 10 of 793 Results

Code for modeling WASH components by RTMB Apr 6, 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, V1 The repository contains two R code files: data preprocessing and model/prediction/evaluation files.
A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions Apr 6, 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, V1 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.
Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations Apr 3, 2026 - School of Engineering and Applied Science Valavanis, Antonios S.; Gurevich, Evgeny L.; Shugaev, Maxim V.; Zhigilei, Leonid V., 2026, "Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations", https://doi.org/10.18130/V3/XCKLVR, University of Virginia Dataverse, V2 Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations
Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys Mar 30, 2026 - Materials Informatics Group Garg, Sunidhi; Jishnu Bhattacharyya; Vineet V. Joshi; Sean R. Agnew; Prasanna V. Balachandran, 2026, "Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys", https://doi.org/10.18130/V3/EXJA3W, University of Virginia Dataverse, V1 The data in each sheet is described below: 1. Data_train: Training dataset used for all the ML models 2. Data_test: Test dataset used for all the ML models 3. Data_virtual: Dataset having the principal component (PC) values of U-Mo-X alloys absent from train and test set and is the input to predict the TTT curves for these U-Mo-X alloys 4. Data_cal...
Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions Mar 26, 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, V2 Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions.
Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys Mar 23, 2026 - Materials Informatics Group Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys", https://doi.org/10.18130/V3/AD6H08, University of Virginia Dataverse, V1 Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys. This repository contains relaxation trajectories for all BCC HEA crystal structures, and 11 template SQS structures.
Materials Informatics Group Mar 19, 2026Department of Materials Science & Engineering
SUPPLEMENTAL MATERIALS for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years" Mar 18, 2026 - School of Medicine Burt Solorzano, Christine; McCartney, Christopher; Pannone, Aaron; Kim, Su Hee; Gurka, Matthew; DeBoer, Mark, 2026, "SUPPLEMENTAL MATERIALS for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years"", https://doi.org/10.18130/V3/WCIKL0, University of Virginia Dataverse, V1 Supplemental analyses for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years"
Replication Data for: Blue Light Enhances Background Current and Dopamine Sensitivity of Carbon-Fiber Microelectrodes During Fast-Scan Cyclic Voltammetry Mar 6, 2026 - Department of Chemistry Donarski, Eric, 2026, "Replication Data for: Blue Light Enhances Background Current and Dopamine Sensitivity of Carbon-Fiber Microelectrodes During Fast-Scan Cyclic Voltammetry", https://doi.org/10.18130/V3/CSSH1Y, University of Virginia Dataverse, V1 This dataset contains both raw and processed data that demonstrates the potential for visible (blue) light to produce a photocurrent and enhance dopamine detection during FSCV experiments.
Supporting data for manuscript Dulko et al., 2025 Mar 5, 2026 - School of Medicine Dulko, Elzbieta, 2026, "Supporting data for manuscript Dulko et al., 2025", https://doi.org/10.18130/V3/BADMP8, University of Virginia Dataverse, V1 MATLAB table including spike times and ECoG from all single-unit recordings.

Code for modeling WASH components by RTMB

Apr 6, 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, V1

The repository contains two R code files: data preprocessing and model/prediction/evaluation files.

A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions

Apr 6, 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, V1

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.

Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations

Apr 3, 2026 - School of Engineering and Applied Science

Valavanis, Antonios S.; Gurevich, Evgeny L.; Shugaev, Maxim V.; Zhigilei, Leonid V., 2026, "Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations", https://doi.org/10.18130/V3/XCKLVR, University of Virginia Dataverse, V2

Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations

Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys

Mar 30, 2026 - Materials Informatics Group

Garg, Sunidhi; Jishnu Bhattacharyya; Vineet V. Joshi; Sean R. Agnew; Prasanna V. Balachandran, 2026, "Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys", https://doi.org/10.18130/V3/EXJA3W, University of Virginia Dataverse, V1

The data in each sheet is described below: 1. Data_train: Training dataset used for all the ML models 2. Data_test: Test dataset used for all the ML models 3. Data_virtual: Dataset having the principal component (PC) values of U-Mo-X alloys absent from train and test set and is the input to predict the TTT curves for these U-Mo-X alloys 4. Data_cal...

Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions

Mar 26, 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, V2

Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions.

Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys

Mar 23, 2026 - Materials Informatics Group

Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys", https://doi.org/10.18130/V3/AD6H08, University of Virginia Dataverse, V1

Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys. This repository contains relaxation trajectories for all BCC HEA crystal structures, and 11 template SQS structures.

Materials Informatics Group

Mar 19, 2026Department of Materials Science & Engineering

SUPPLEMENTAL MATERIALS for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years"

Mar 18, 2026 - School of Medicine

Burt Solorzano, Christine; McCartney, Christopher; Pannone, Aaron; Kim, Su Hee; Gurka, Matthew; DeBoer, Mark, 2026, "SUPPLEMENTAL MATERIALS for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years"", https://doi.org/10.18130/V3/WCIKL0, University of Virginia Dataverse, V1

Supplemental analyses for "Free testosterone independently predicts metabolic syndrome severity in U.S. adolescent girls aged 12 to 19 years"

Replication Data for: Blue Light Enhances Background Current and Dopamine Sensitivity of Carbon-Fiber Microelectrodes During Fast-Scan Cyclic Voltammetry

Mar 6, 2026 - Department of Chemistry

Donarski, Eric, 2026, "Replication Data for: Blue Light Enhances Background Current and Dopamine Sensitivity of Carbon-Fiber Microelectrodes During Fast-Scan Cyclic Voltammetry", https://doi.org/10.18130/V3/CSSH1Y, University of Virginia Dataverse, V1

This dataset contains both raw and processed data that demonstrates the potential for visible (blue) light to produce a photocurrent and enhance dopamine detection during FSCV experiments.

Supporting data for manuscript Dulko et al., 2025

Mar 5, 2026 - School of Medicine

Dulko, Elzbieta, 2026, "Supporting data for manuscript Dulko et al., 2025", https://doi.org/10.18130/V3/BADMP8, University of Virginia Dataverse, V1

MATLAB table including spike times and ECoG from all single-unit recordings.

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Find and cite data across all research fields.Choose LibraData below to share and archive your data

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