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11 to 20 of 29 Results
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...
Plain Text - 260.4 MB - MD5: 7718dcb7a8d0b5012f33dbd450c3735e
IMPROVE dust and wildfire events
Adobe PDF - 44.6 KB - MD5: e5fc7cdf409af88bdda53343cd228526
Readme
Oct 23, 2023
Kelley, Matthew C; Perry, Scott James; Tucker, Benjamin V, 2023, "Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"", https://doi.org/10.13021/ORC2020/PDSAP7, George Mason University Dataverse, V1
These are the TextGrid outputs of the alignments run during the evaluation process for the MAPS paper. They are separated into train, validation, and test sets. The results from training and running the Montreal Forced Aligner (MFA) are also provided. The tar file for MFA contain...
May 11, 2023
Krall, Jenna R, 2023, "Road density, road features, and in-vehicle PM2.5 during daily trips taken by Washington, DC metro area commuters", https://doi.org/10.13021/ORC2020/9EA00H, George Mason University Dataverse, V1, UNF:6:+x5TcYrmAs3MUZthKeXuRQ== [fileUNF]
This dataset was used to conduct all analyses in the working paper, "Short-term associations of road density and road features with in-vehicle PM2.5 during daily trips" by Jenna R. Krall, Jonathan Thornburg, Ting Zhang, Anna Z. Pollack, Yi-Ching Lee, Michelle McCombs, and Lucas R...
Adobe PDF - 58.6 KB - MD5: 182bf9d2eb1026696b42c040944be213
README file for roadden.csv
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