cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21949/1404212
title: Third Generation Simulation Data (TGSIM) I-90/I-94 Moving Trajectories

description: This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.
description: The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png.
DOI: 10.21949/1404230
title: Third Generation Simulation Data (TGSIM) Foggy Bottom Trajectories

description: This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset.
description: The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset).
DOI: 10.21949/1404217
title: Third Generation Simulation Data (TGSIM) I-294 L1 Trajectories

description: This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset.
description: The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png.
DOI: 10.21949/1404222
title: Third Generation Simulation Data (TGSIM) I-395 Trajectories

description: This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset.
description: The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos.
DOI: 10.21949/1404219
title: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 2

description: This data set series include the following data sets: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 1; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-2 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-1 Rep 2
description: WFCW-3 FCW Slow Moving Vehicle Rep 2
DOI: 10.21949/1404221
title: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 1

description: This data set series include the following data sets: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 1; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-2 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-1 Rep 3
description: WFCW-3 FCW Slow Moving Vehicle Rep 1
DOI: 10.21949/1404207
title: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-2 Rep 2

description: This data set series include the following data sets: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 1; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-2 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-1 Rep 4
description: WFCW-2 Stopped Vehicle Message Prioritization Rep 2
DOI: 10.21949/1404216
title: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-1 Rep 2

description: This data set series include the following data sets: LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-3 Rep 1; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-2 Rep 2; LTE-V2X Wyoming Connected Vehicle Pilot test ID WFCW-1 Rep 5
description: Test case WFCW-1 Results - FCW Stopped Vehicle Rep 2
DOI: 10.21949/1404198
title: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data

description: This data set series include the following data sets: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched
description: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is a raw sample of Maryland roadway speed data
DOI: 10.21949/1404187
title: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data

description: This data set series include the following data sets: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched
description: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.
DOI: 10.21949/1404195
title: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents

description: This data set series include the following data sets: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched
description: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data
DOI: 10.21949/1404197
title: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019

description: This data set series include the following data sets: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched
description: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data
DOI: 10.21949/1404200
title: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched

description: This data set series include the following data sets: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Speed Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Processed Integrated Traffic Work Zone Data; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019; Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Incidents Matched
description: Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland roadway incident data without rows where road_tmc and road are inconsistent.
DOI: 10.15468/dl.mdsvxy
title: Occurrence Download

description: A dataset containing 9832 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Colaptes chrysoides)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 9832 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086657-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.craqmu
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heracleum persicum",
 "Country is Finland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 177 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086659-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkxmke
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ceyx nigromaxilla)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 27 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086658-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24430447
title: Supporting data for "Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity"

description: This repository contains all supporting data, including R scripts, data for figure generation, nucleotide sequences, amino acid sequences, alignments and Newick phylogenetic tree files for the study "Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity"
DOI: 10.6084/m9.figshare.24430447.v3
title: Supporting data for "Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity"

description: This repository contains all supporting data, including R scripts, data for figure generation, nucleotide sequences, amino acid sequences, alignments and Newick phylogenetic tree files for the study "Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity"
DOI: 10.6084/m9.figshare.24430447.v2
title: Supporting data for "Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity"

description: This repository contains all supporting data, including R scripts, data for figure generation, nucleotide sequences, amino acid sequences, alignments and Newick phylogenetic tree files for the study "Systematic identification of cargo-mobilizinggenetic elements reveals new dimensions of eukaryotic diversity"