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1 to 10 of 77 Results
Replication data for "What drives rural drinking water system performance? Evidence from Ghana"
Sep 16, 2026
Smith, Daniel Worthington, 2026, "Replication data for "What drives rural drinking water system performance? Evidence from Ghana"", https://doi.org/10.13021/ORC2020/XHZ9XM, George Mason University Dataverse, V1, UNF:6:TNyfj0uhqqnyTm2MnH7pbQ== [fileUNF]
Replication data with and without 8 instances for sensitivity analysis as well as R code.
Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data
Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data
Sep 12, 2026
Yim, An Di, 2025, "Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data", https://doi.org/10.13021/ORC2020/LFPH6M, George Mason University Dataverse, V4, UNF:6:qZs4yjoHl2/IcJhKN6RvYg== [fileUNF]
Data files, 3D model files, and research protocols supporting the findings of "Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data." This study evaluates the utility of an automated super-resolution reconstruction (SRR) framework in generating high-resolution skeletal models from multiple orthogonal t...
Green Public Infrastructure for Local Climate Resilience: A Case Study of the Mount Vernon District
Green Public Infrastructure for Local Climate Resilience: A Case Study of the Mount Vernon District
Aug 24, 2026
Younsung Kim, 2026, "Green Public Infrastructure for Local Climate Resilience: A Case Study of the Mount Vernon District", https://doi.org/10.13021/ORC2020/OTO7WZ, George Mason University Dataverse, V1
Data and maps used in the analysis of 'Green Public Infrastructure for Local Climate Resilience: A Case Study of the Mount Vernon District'. Published in Urban Science.
4VA Spatial Computing
4VA Spatial Computing
Aug 24, 2026
Dr. Younsung Kim, 2026, "4VA Spatial Computing", https://doi.org/10.13021/ORC2020/G2TGDO, George Mason University Dataverse, V1
4VA Spatial Computing Series
Replication Data for: Applicability of handheld laser scanning for nonmetric skeletal assessment in biological profile estimation: A preliminary two-case study
Aug 4, 2026
Cummings, Chloe; Yim, An-Di, 2026, "Replication Data for: Applicability of handheld laser scanning for nonmetric skeletal assessment in biological profile estimation: A preliminary two-case study", https://doi.org/10.13021/ORC2020/HICSJ0, George Mason University Dataverse, V3, UNF:6:FEOHGdGyILVKjzIdS43b9g== [fileUNF]
Nonmetric trait scores for the two skeletal cases included in the study from participants. Two data files were included: the first contains all participants' scores for the physical assessment, the second contains a subset of participants' scores for the physical assessment and all corresponding scores for the virtual assessment.
Datasets Supporting "Neighborhood-Scale O₃, NO₂, and PM₂.₅ in Washington, D.C. from a 1 × 1 km WRF-SMOKE-CMAQ Framework in Washington, D.C."
Datasets Supporting "Neighborhood-Scale O₃, NO₂, and PM₂.₅ in Washington, D.C. from a 1 × 1 km WRF-SMOKE-CMAQ Framework in Washington, D.C."
Jul 13, 2026
Hallaji, Hoda; Ma, Siqi; Tong, Daniel; Henneman, Lucas, 2026, "Datasets Supporting "Neighborhood-Scale O₃, NO₂, and PM₂.₅ in Washington, D.C. from a 1 × 1 km WRF-SMOKE-CMAQ Framework in Washington, D.C."", https://doi.org/10.13021/ORC2020/FZ1KOX, George Mason University Dataverse, V1, UNF:6:g0qwglPuUN2VlDGA6uCEZA== [fileUNF]
This dataset contains the processed meteorological evaluation products and air quality modeling outputs supporting the manuscript "Neighborhood-Scale O₃, NO₂, and PM₂.₅ in Washington, D.C. from a 1 × 1 km WRF-SMOKE-CMAQ Framework." The dataset includes processed outputs from WRF (v4.2.1), MCIP (v5.4), SMOKE (v4.7), and CMAQ (v5.4) simulations for F...
Replication Data for: Validation of Different Postmortem Interval Estimation Methods in the Mid-Atlantic Region
Jun 17, 2026
Kalin, Mason; Lyles, Kennedie; Yim, An-Di, 2026, "Replication Data for: Validation of Different Postmortem Interval Estimation Methods in the Mid-Atlantic Region", https://doi.org/10.13021/ORC2020/V9XGTV, George Mason University Dataverse, V1, UNF:6:42tBdt2GY2WJ6S5QTfjsUg== [fileUNF]
Replication data for "Validation of Different Postmortem Interval Estimation Methods in the Mid-Atlantic Region." Each row is one documented observation; the file contains the decomposition score (TBS), temperature measurements, and the externally generated method outputs underlying the analyses. Photographs, documentation notes, or detailed donor...
H&H model framework for Pocomoke City, MD (GIS files)
Jun 12, 2026
Veronez, Diana; de Lima, Andre de Souza; Ferreira, Celso, 2026, "H&H model framework for Pocomoke City, MD (GIS files)", https://doi.org/10.13021/ORC2020/9TH99S, George Mason University Dataverse, V1
The dataset was developed to support flood hazard assessment and adaptation planning. It is part of a broader data collection available through HydroShare, which includes additional supporting materials: https://www.hydroshare.org/resource/c0c1559300cb40a9bc090131e3bc8722/
Exposure of Coastal Protected Areas to Storm Surge and Sea Level Rise in the United States Mid-Atlantic Bight
May 21, 2026
Moghaddame-Jafari, Bahman, 2026, "Exposure of Coastal Protected Areas to Storm Surge and Sea Level Rise in the United States Mid-Atlantic Bight", https://doi.org/10.13021/ORC2020/ZZY1LT, George Mason University Dataverse, V1
This dataset contains geospatial model outputs representing coastal flood exposure associated with Hurricane Irene under two scenarios: (1) present-day conditions and (2) a sea-level rise (SLR) scenario of +1.3 m. The data are provided as polygon shapefiles derived from hydrodynamic modeling results and represent spatial extents of exposed or inund...
Data for: Integrated flood risk management: combining numerical modeling and community perceptions to inform strategy prioritization
May 4, 2026
Veronez, Diana; de Lima, Andre de Souza; Ferreira, Celso, 2026, "Data for: Integrated flood risk management: combining numerical modeling and community perceptions to inform strategy prioritization", https://doi.org/10.13021/ORC2020/KGGXKL, George Mason University Dataverse, V1
This dataset supports an integrated framework that combines two-dimensional flood modeling, participatory mapping, and a spatial integration approach to identify areas where modeled flood exposure intersects with community perceptions to prioritize management strategies in a coastal–urban community in Maryland
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