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Feb 5, 2025 - Mason Living Lab
Ungvari, Judit, 2025, "Arlington Campus Daily Electricity", https://doi.org/10.13021/ORC2020/KZWMEN, George Mason University Dataverse, V1
The data are available for classroom and research purposes, provided by the Facilities and Office of Sustainability. Report type:Meter Compare Trend For: Arlington Campus Analysis Period / Resolution: Day / 60 min Delivery Interval: Daily Meter Type: Other meters,Submeter,Sub 2 V...
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"
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.
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.
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...
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)
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...
Dec 23, 2024
Xiao, Xuesu, 2024, "Global Navigation Dataset", https://doi.org/10.13021/ORC2020/JUIW5F, George Mason University Dataverse, V9
Global Navigation Dataset (GND) contains multi-modal robot perception and action data across many university campuses with multiple traversability labels (open space, obstacles, stairs, off-road, and roadway).
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
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.
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