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1 to 10 of 3,753 Results

Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Jul 18, 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, V3, UNF:6:r17k7W0Br3DIoZ+WTQQUpg== [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...
README.md Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Markdown Text - 3.5 KB - MD5: 6e33b0d6851bab9d65d0271c37360f6f
ITK-SNAP protocol.pdf Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Adobe PDF - 395.6 KB - MD5: aefb4370a0135a56955cfddb1552c16f Research protocol describeing the step-by-step process of mask generation.
pacs-osteometrics.csv Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Tabular Data - 2.5 KB - 34 Variables, 33 Observations - UNF:6:VXziZwjcbrt0BD7cMz7N4A== Osteometric measurements taken directly from high-resolution volume renderings to the corresponding ID.
Surface comparison protocol.pdf Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Adobe PDF - 96.8 KB - MD5: a9bfeb85fa906f4c89a7893c73a0a673 Research protocol describing the step-by-step process of surface comparison in 3D Slicer.
surface-statistics.csv Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data Tabular Data - 3.4 KB - 9 Variables, 61 Observations - UNF:6:U6ROibZnyU81mxROmlB6Cg== Results of surface comparison between pre and post. Processes used to conduct surface comparison are outlined in Surface comparison protocol.pdf
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...
1km_dc_population_weighted_exposure.xlsx Jul 13, 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." Tabular Data - 15.4 KB - 3 Variables, 480 Observations - UNF:6:u3RuC4CS2+kIoQVL9Td5cQ==
1km_monthly_Aug.nc Jul 13, 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." Network Common Data Form - 85.1 KB - MD5: 8357b052fe47f425b2a0329b8f29d3d7
1km_monthly_Feb.nc Jul 13, 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." Network Common Data Form - 86.7 KB - MD5: 5c4084db7471f464bb637e246bd97fba

Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Jul 18, 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, V3, UNF:6:r17k7W0Br3DIoZ+WTQQUpg== [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...

README.md

Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Markdown Text - 3.5 KB -

ITK-SNAP protocol.pdf

Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Adobe PDF - 395.6 KB -

Research protocol describeing the step-by-step process of mask generation.

pacs-osteometrics.csv

Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Tabular Data - 2.5 KB - 34 Variables, 33 Observations -

Osteometric measurements taken directly from high-resolution volume renderings to the corresponding ID.

Surface comparison protocol.pdf

Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Adobe PDF - 96.8 KB -

Research protocol describing the step-by-step process of surface comparison in 3D Slicer.

surface-statistics.csv

Jul 18, 2026 - Replication Data for: Adapting super-resolution reconstruction for skeletal analysis of clinical computed tomography data

Tabular Data - 3.4 KB - 9 Variables, 61 Observations -

Results of surface comparison between pre and post. Processes used to conduct surface comparison are outlined in Surface comparison protocol.pdf

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...

1km_dc_population_weighted_exposure.xlsx

Jul 13, 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."

Tabular Data - 15.4 KB - 3 Variables, 480 Observations -

1km_monthly_Aug.nc

Jul 13, 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."

Network Common Data Form - 85.1 KB -

1km_monthly_Feb.nc

Jul 13, 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."

Network Common Data Form - 86.7 KB -

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