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11 to 20 of 29 Results

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...
improve_events_2023_all.txt Apr 29, 2024 - Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network Plain Text - 260.4 MB - MD5: 7718dcb7a8d0b5012f33dbd450c3735e IMPROVE dust and wildfire events
Readme_IMPROVE_Events_20240428.pdf Apr 29, 2024 - Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network Adobe PDF - 44.6 KB - MD5: e5fc7cdf409af88bdda53343cd228526 Readme
Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation" 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...
mfa_tg.tar.bz2 Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation" Compressed Archive - 3.0 MB - MD5: 9acf688c4867b034bfb77296f1515ae4
test_tg.tar.bz2 Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation" Compressed Archive - 41.7 MB - MD5: 22bccff875b3414f048ee53f2963647a
train_tg.tar.bz2 Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation" Compressed Archive - 245.9 MB - MD5: aad1b9d97eb50140984e00bcea8e4bd0
val_tg.tar.bz2 Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation" Compressed Archive - 17.9 MB - MD5: a62a85d53f4a318711c75682a16e6286
Road density, road features, and in-vehicle PM2.5 during daily trips taken by Washington, DC metro area commuters 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...
readme.pdf May 11, 2023 - Road density, road features, and in-vehicle PM2.5 during daily trips taken by Washington, DC metro area commuters Adobe PDF - 58.6 KB - MD5: 182bf9d2eb1026696b42c040944be213 README file for roadden.csv

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...

improve_events_2023_all.txt

Apr 29, 2024 - Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network

Plain Text - 260.4 MB -

IMPROVE dust and wildfire events

Readme_IMPROVE_Events_20240428.pdf

Apr 29, 2024 - Aerosol Composition during Dust Storms and Wildfires from the IMPROVE Network

Adobe PDF - 44.6 KB -

Readme

Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"

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...

mfa_tg.tar.bz2

Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"

Compressed Archive - 3.0 MB -

test_tg.tar.bz2

Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"

Compressed Archive - 41.7 MB -

train_tg.tar.bz2

Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"

Compressed Archive - 245.9 MB -

val_tg.tar.bz2

Oct 23, 2023 - Replication Data for "The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation"

Compressed Archive - 17.9 MB -

Road density, road features, and in-vehicle PM2.5 during daily trips taken by Washington, DC metro area commuters

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...

readme.pdf

May 11, 2023 - Road density, road features, and in-vehicle PM2.5 during daily trips taken by Washington, DC metro area commuters

Adobe PDF - 58.6 KB -

README file for roadden.csv

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