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11 to 20 of 57 Results

A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia Feb 4, 2025 Jeffries, William W, 2025, "A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia", https://doi.org/10.13021/ORC2020/AJXIK0, George Mason University Dataverse, V1 Code used in support of paper "A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia"
Building data - King Hall Jan 30, 2025 - Mason Living Lab Ungvari, Judit, 2025, "Building data - King Hall", https://doi.org/10.13021/ORC2020/XYM4BI, George Mason University Dataverse, V1 Spreadsheets on utilities consumption in King Hall on the Fairfax campus of George Mason University. The data are available for classroom and research purposes, provided by the Facilities and Office of Sustainability.
Building data - Johnson Center Jan 30, 2025 - Mason Living Lab Ungvari, Judit, 2025, "Building data - Johnson Center", https://doi.org/10.13021/ORC2020/EMJUYM, George Mason University Dataverse, V1 Spreadsheets on utilities consumption in the Johnson Center Building on the Fairfax campus of George Mason University. The data are available for classroom and research purposes, provided by the Facilities and Office of Sustainability.
Deformable Object Tracking Dataset (DOT) Jan 21, 2025 Li, Xinyuan; Guo, Yu; Tu, Yubei; Ji, Yu; Liu, Yanchen; Ye, Jinwei; Zheng, Changxi, 2024, "Deformable Object Tracking Dataset (DOT)", https://doi.org/10.13021/ORC2020/XXLVXM, George Mason University Dataverse, V29 The Deformable Object Tracking Dataset, DOT, is a large real-world dataset for tracking deformable objects with little or no texture. The key technology is to use UV fluorescent markers to provide features for correspondence tracking while maintain the object's original appearanc...
Unraveling Glutamine Synthetase Dynamics: Implications for Glutamate Homeostasis in Astrocytes Jan 14, 2025 Peixoto, Nathalia, 2025, "Unraveling Glutamine Synthetase Dynamics: Implications for Glutamate Homeostasis in Astrocytes", https://doi.org/10.13021/ORC2020/TA0SYJ, George Mason University Dataverse, V1 Dataset refers to PlosOne submission (2025)
DEMend: Automating hydrological correction of Digital Elevation Models for enhanced urban flood modeling Jan 9, 2025 Khalid, Zeeshan; P. J. Ruess; Andre de S. de Lima; Arslaan Khalid; Tyler Miesse; Diana Veronez; Celso M. Ferreira; James L. Kinter, 2024, "DEMend: Automating hydrological correction of Digital Elevation Models for enhanced urban flood modeling", https://doi.org/10.13021/ORC2020/50YZLT, George Mason University Dataverse, V3 The DEMend Toolbox is designed to automate the process of hydrological correction for Digital Elevation Models (DEMs). It incorporates existing, publicly available datasets, such as road networks, stream networks, and bridges/culverts, to identify and remove obstructions from DEM...
Blanchard et al. 2024 RPPA intensity values Dec 18, 2024 Gomeiz, Alison T, 2024, "Blanchard et al. 2024 RPPA intensity values", https://doi.org/10.13021/ORC2020/UOWI5D, George Mason University Dataverse, V1, UNF:6:kHcE+cH53m0yNyeTAEbD2Q== [fileUNF] RPPA intensity values used in the publication titled "Signaling dynamics in coexisting monoclonal cell subpopulations unveil mechanisms of resistance to anti-cancer compounds" by Blanchard et al. 2024. https://doi.org/10.1186/s12964-024-01742-3
Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions Sep 30, 2024 Xiao, Xuesu, 2024, "Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions", https://doi.org/10.13021/ORC2020/SP577T, George Mason University Dataverse, V3 This dataset contains multi-modal passive perception data, including thermal, event, and stereo RGB cameras, IMU, GPS, and LiDAR for ground truth, to enable off-road mobility in extreme low-light conditions.
Code and Data for "A General Framework for Regression with Mismatched Data Based on Mixture Modeling" Aug 1, 2024 Slawski, Martin P; West, Brady T; Bukke, Priyanjali; Wang, Zhenbang; Diao, Guoqing; Ben-David, Emanuel, 2024, "Code and Data for "A General Framework for Regression with Mismatched Data Based on Mixture Modeling"", https://doi.org/10.13021/ORC2020/D2YJXX, George Mason University Dataverse, V1, UNF:6:GY2VJDitETivE4j64uQv1g== [fileUNF] This file contains code and data for replicating the simulation studies and the analysis in Sections 7.1 and 7.3 of the paper Slawski, M., West, B.T., Bukke, P., Wang, Z., Diao, G., Ben-David, E. "A General Framework for Regression with Mismatched Data Based on Mixture Modeling",...
Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network Apr 29, 2024 Tong, Daniel Quansong, 2024, "Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network", https://doi.org/10.13021/ORC2020/EOL1WJ, George Mason University Dataverse, V1 This dataset contains daily observations of PM10, PM2.5 and chemical composition measured by the Interagency Monitoring of Protected Visual Environment (IMPROVE) network. In addition the raw IMPROVE data, local dust storms and wildfire events are identified in a separate column b...

A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia

Feb 4, 2025

Jeffries, William W, 2025, "A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia", https://doi.org/10.13021/ORC2020/AJXIK0, George Mason University Dataverse, V1

Code used in support of paper "A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia"

Building data - King Hall

Jan 30, 2025 - Mason Living Lab

Ungvari, Judit, 2025, "Building data - King Hall", https://doi.org/10.13021/ORC2020/XYM4BI, George Mason University Dataverse, V1

Spreadsheets on utilities consumption in King Hall on the Fairfax campus of George Mason University. The data are available for classroom and research purposes, provided by the Facilities and Office of Sustainability.

Building data - Johnson Center

Jan 30, 2025 - Mason Living Lab

Ungvari, Judit, 2025, "Building data - Johnson Center", https://doi.org/10.13021/ORC2020/EMJUYM, George Mason University Dataverse, V1

Spreadsheets on utilities consumption in the Johnson Center Building on the Fairfax campus of George Mason University. The data are available for classroom and research purposes, provided by the Facilities and Office of Sustainability.

Deformable Object Tracking Dataset (DOT)

Jan 21, 2025

Li, Xinyuan; Guo, Yu; Tu, Yubei; Ji, Yu; Liu, Yanchen; Ye, Jinwei; Zheng, Changxi, 2024, "Deformable Object Tracking Dataset (DOT)", https://doi.org/10.13021/ORC2020/XXLVXM, George Mason University Dataverse, V29

The Deformable Object Tracking Dataset, DOT, is a large real-world dataset for tracking deformable objects with little or no texture. The key technology is to use UV fluorescent markers to provide features for correspondence tracking while maintain the object's original appearanc...

Unraveling Glutamine Synthetase Dynamics: Implications for Glutamate Homeostasis in Astrocytes

Jan 14, 2025

Peixoto, Nathalia, 2025, "Unraveling Glutamine Synthetase Dynamics: Implications for Glutamate Homeostasis in Astrocytes", https://doi.org/10.13021/ORC2020/TA0SYJ, George Mason University Dataverse, V1

Dataset refers to PlosOne submission (2025)

DEMend: Automating hydrological correction of Digital Elevation Models for enhanced urban flood modeling

Jan 9, 2025

Khalid, Zeeshan; P. J. Ruess; Andre de S. de Lima; Arslaan Khalid; Tyler Miesse; Diana Veronez; Celso M. Ferreira; James L. Kinter, 2024, "DEMend: Automating hydrological correction of Digital Elevation Models for enhanced urban flood modeling", https://doi.org/10.13021/ORC2020/50YZLT, George Mason University Dataverse, V3

The DEMend Toolbox is designed to automate the process of hydrological correction for Digital Elevation Models (DEMs). It incorporates existing, publicly available datasets, such as road networks, stream networks, and bridges/culverts, to identify and remove obstructions from DEM...

Blanchard et al. 2024 RPPA intensity values

Dec 18, 2024

Gomeiz, Alison T, 2024, "Blanchard et al. 2024 RPPA intensity values", https://doi.org/10.13021/ORC2020/UOWI5D, George Mason University Dataverse, V1, UNF:6:kHcE+cH53m0yNyeTAEbD2Q== [fileUNF]

RPPA intensity values used in the publication titled "Signaling dynamics in coexisting monoclonal cell subpopulations unveil mechanisms of resistance to anti-cancer compounds" by Blanchard et al. 2024. https://doi.org/10.1186/s12964-024-01742-3

Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions

Sep 30, 2024

Xiao, Xuesu, 2024, "Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions", https://doi.org/10.13021/ORC2020/SP577T, George Mason University Dataverse, V3

This dataset contains multi-modal passive perception data, including thermal, event, and stereo RGB cameras, IMU, GPS, and LiDAR for ground truth, to enable off-road mobility in extreme low-light conditions.

Code and Data for "A General Framework for Regression with Mismatched Data Based on Mixture Modeling"

Aug 1, 2024

Slawski, Martin P; West, Brady T; Bukke, Priyanjali; Wang, Zhenbang; Diao, Guoqing; Ben-David, Emanuel, 2024, "Code and Data for "A General Framework for Regression with Mismatched Data Based on Mixture Modeling"", https://doi.org/10.13021/ORC2020/D2YJXX, George Mason University Dataverse, V1, UNF:6:GY2VJDitETivE4j64uQv1g== [fileUNF]

This file contains code and data for replicating the simulation studies and the analysis in Sections 7.1 and 7.3 of the paper Slawski, M., West, B.T., Bukke, P., Wang, Z., Diao, G., Ben-David, E. "A General Framework for Regression with Mismatched Data Based on Mixture Modeling",...

Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network

Apr 29, 2024

Tong, Daniel Quansong, 2024, "Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network", https://doi.org/10.13021/ORC2020/EOL1WJ, George Mason University Dataverse, V1

This dataset contains daily observations of PM10, PM2.5 and chemical composition measured by the Interagency Monitoring of Protected Visual Environment (IMPROVE) network. In addition the raw IMPROVE data, local dust storms and wildfire events are identified in a separate column b...

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