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Supporting files include aerial reference images for twelve distinct data collection \u201cRuns\u201d (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each \u201cRun\u201d (I-294-L2-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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I294 L2.csv\u201d 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 \u201cAnnotation on Regions.zip\u201d. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png.\n\nThis 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 two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.</li>\n<li>I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. 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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 \u201cAnnotation on Regions.zip\u201d. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png.\n\nThis 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 two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. 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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, \u2026, i49.png stored in the folder titled \u201cAnnotation on Regions.zip\u201d) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). \n\nThis 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.\n\nAs part of this dataset, the following files were provided:\n<ul><li>TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion.</li>\n<li>Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments.</li>\t\n<li>Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). 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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, \u2026, i49.png stored in the folder titled \u201cAnnotation on Regions.zip\u201d) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). \n\nThis 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.\n\nAs part of this dataset, the following files were provided:\n<ul><li>TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. 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Supporting files include aerial reference images for six distinct data collection \u201cRuns\u201d (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each \u201cRun\u201d (I-90-stationary-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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I90_94Stationary.csv\u201d for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled \u201cAnnotation on Regions.zip\u201d. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). \n\nThis 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 the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X.</li>\n<li>I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The \"x\" and \"y\" columns represent the horizontal and ve","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/9uas-hf8b/columns.json","describedByType":"application/json","downloadURL":"https://data.transportation.gov/api/v3/views/9uas-hf8b/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/9uas-hf8b/columns.xml","describedByType":"application/xml","downloadURL":"https://data.transportation.gov/api/v3/views/9uas-hf8b/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.transportation.gov/api/v3/views/9uas-hf8b/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.transportation.gov/api/views/9uas-hf8b","issued":"2024-11-04","keyword":["aerial videography","automated vehicles","category: vehicle safety","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems (its)","its joint program office (jpo)","multi-modal trajectories","subcategory: autonomous vehicle safety systems","tgsim","third generation simulation","vehicle trajectory data"],"landingPage":"https://data.transportation.gov/d/9uas-hf8b","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2026-10-05","programCode":["021:042"],"publisher":{"@type":"org:Organization","name":"Federal Highway Administration"},"spatial":"I-90/I94 in Chicago, IL; I-294 near Hinsdale, IL; I-395 in Washington DC; George Washington University Campus, Washington DC (Foggy Bottom)","theme":["Automobiles"],"title":"Third Generation Simulation Data (TGSIM) I-90/I-94 Stationary Trajectories"},"description":"The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. 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Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled \u201cAnnotation on Regions.zip\u201d. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6). \n\nThis 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 the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X.</li>\n<li>I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The \"x\" and \"y\" columns represent the horizontal and ve","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/bf744b3d-acf5-4655-8bcb-1005e2156961","harvest_record_raw":"https://catalog.data.gov/harvest_record/bf744b3d-acf5-4655-8bcb-1005e2156961/raw","has_download":true,"has_spatial":true,"identifier":"https://data.transportation.gov/api/views/9uas-hf8b","keyword":["aerial videography","automated vehicles","category: vehicle safety","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems (its)","its joint program office (jpo)","multi-modal trajectories","subcategory: autonomous vehicle safety systems","tgsim","third generation simulation","vehicle trajectory 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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, \u2026, I395_lane-6) stored in a folder titled \u201cAnnotation on Regions.zip\u201d to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns \u201cx1\u201d to \u201cx5\u201d represent the horizontal pixel values in the reference image, with \u201cx1\u201d being the leftmost boundary line and \u201cx5\u201d 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 \u201cx1\u201d. Lane -5 corresponds to the area between \u201cx1\u201d and \u201cx2\u201d, and so forth to the rightmost lane, which is defined by the area to the right of \u201cx5\u201d (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.\n\nThis 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 was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments.</li>\n<li>I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns \"x1\" to \"x5\" represent the horizontal pi","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/97n2-kuqi/columns.json","describedByType":"application/json","downloadURL":"https://data.transportation.gov/api/v3/views/97n2-kuqi/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/97n2-kuqi/columns.xml","describedByType":"application/xml","downloadURL":"https://data.transportation.gov/api/v3/views/97n2-kuqi/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.transportation.gov/api/v3/views/97n2-kuqi/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.transportation.gov/api/views/97n2-kuqi","issued":"2024-11-04","keyword":["aerial videography","automated vehicles","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems (its)","its joint program office (jpo)","multi-modal trajectories","tgsim","third generation simulation","vehicle trajectory data"],"landingPage":"https://data.transportation.gov/d/97n2-kuqi","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2026-10-05","programCode":["021:042"],"publisher":{"@type":"org:Organization","name":"Federal Highway Administration"},"spatial":"I-90/I94 in Chicago, IL; I-294 near Hinsdale, IL; I-395 in Washington DC; George Washington University Campus, Washington DC (Foggy Bottom)","theme":["Automobiles"],"title":"Third Generation Simulation Data (TGSIM) I-395 Trajectories"},"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, \u2026, I395_lane-6) stored in a folder titled \u201cAnnotation on Regions.zip\u201d to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns \u201cx1\u201d to \u201cx5\u201d represent the horizontal pixel values in the reference image, with \u201cx1\u201d being the leftmost boundary line and \u201cx5\u201d 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 \u201cx1\u201d. Lane -5 corresponds to the area between \u201cx1\u201d and \u201cx2\u201d, and so forth to the rightmost lane, which is defined by the area to the right of \u201cx5\u201d (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.\n\nThis 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 was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments.</li>\n<li>I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). 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Supporting files include aerial reference images for ten distinct data collection \u201cRuns\u201d (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 \u201cRun\u201d (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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I294 L1.csv\u201d 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 \u201cAnnotation on Regions.zip\u201d. 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.\n\nThis 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 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.</li>\n<li>I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/7zjf-a4zf/columns.json","describedByType":"application/json","downloadURL":"https://data.transportation.gov/api/v3/views/7zjf-a4zf/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/7zjf-a4zf/columns.xml","describedByType":"application/xml","downloadURL":"https://data.transportation.gov/api/v3/views/7zjf-a4zf/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.transportation.gov/api/v3/views/7zjf-a4zf/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.transportation.gov/api/views/7zjf-a4zf","issued":"2024-11-04","keyword":["aerial videography","automated vehicles","category: vehicle safety","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems (its)","its joint program office (jpo)","multi-modal trajectories","subcategory: autonomous vehicle safety systems","tgsim","third generation simulation","vehicle trajectory data"],"landingPage":"https://data.transportation.gov/d/7zjf-a4zf","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2026-10-05","programCode":["021:042"],"publisher":{"@type":"org:Organization","name":"Federal Highway Administration"},"spatial":"I-90/I94 in Chicago, IL; I-294 near Hinsdale, IL; I-395 in Washington DC; George Washington University Campus, Washington DC (Foggy Bottom)","theme":["Automobiles"],"title":"Third Generation Simulation Data (TGSIM) I-294 L1 Trajectories"},"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 \u201cRuns\u201d (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 \u201cRun\u201d (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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I294 L1.csv\u201d 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 \u201cAnnotation on Regions.zip\u201d. 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.\n\nThis 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 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.</li>\n<li>I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/5c4b5249-b100-4030-a1f7-b0c5e89f59d3","harvest_record_raw":"https://catalog.data.gov/harvest_record/5c4b5249-b100-4030-a1f7-b0c5e89f59d3/raw","has_download":true,"has_spatial":true,"identifier":"https://data.transportation.gov/api/views/7zjf-a4zf","keyword":["aerial videography","automated vehicles","category: vehicle safety","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems 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JPO","hasEmail":"mailto:data.itsjpo@dot.gov"},"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 \u201cRuns\u201d (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each \u201cRun\u201d (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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I90_94moving.csv\u201d 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 \u201cAnnotation on Regions.zip\u201d. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png.\n\nThis 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.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X.</li>\n<li>I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The \"x\" and \"y\" columns represent the horizontal and vertical locations in the reference image, respectively. The \"ramp\" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes.</li>\n<li>Annotation on Regions.zip, which includes images that visually map lanes (I90_9","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/6a6e-vfvi/columns.json","describedByType":"application/json","downloadURL":"https://data.transportation.gov/api/v3/views/6a6e-vfvi/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.transportation.gov/api/views/6a6e-vfvi/columns.xml","describedByType":"application/xml","downloadURL":"https://data.transportation.gov/api/v3/views/6a6e-vfvi/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.transportation.gov/api/v3/views/6a6e-vfvi/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.transportation.gov/api/views/6a6e-vfvi","issued":"2024-11-04","keyword":["aerial videography","automated vehicles","category: vehicle safety","human-automated vehicle interactions","infrastructure-based videography","intelligent transportation systems (its)","its joint program office (jpo)","multi-modal trajectories","subcategory: autonomous vehicle safety systems","tgsim","third generation simulation","vehicle trajectory data"],"landingPage":"https://data.transportation.gov/d/6a6e-vfvi","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2026-10-05","programCode":["021:042"],"publisher":{"@type":"org:Organization","name":"Federal Highway Administration"},"spatial":"I-90/I94 in Chicago, IL; I-294 near Hinsdale, IL; I-395 in Washington DC; George Washington University Campus, Washington DC (Foggy Bottom)","theme":["Automobiles"],"title":"Third Generation Simulation Data (TGSIM) I-90/I-94 Moving Trajectories"},"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 \u201cRuns\u201d (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each \u201cRun\u201d (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 \u201cTGSIM \u2013 Centerline Data Dictionary \u2013 I90_94moving.csv\u201d 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 \u201cAnnotation on Regions.zip\u201d. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png.\n\nThis 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.\n\nAs part of this dataset, the following files were provided:\n<ul><li>I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles (\"yes\" or \"no\") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.</li>\n<li>I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X.</li>\n<li>I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The \"x\" and \"y\" columns represent the horizontal and vertical locations in the reference image, respectively. The \"ramp\" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). 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The U.S. Fish and Wildlife Service (USFWS) and the National Marine Fisheries Service of U.S. National Oceanic and Atmospheric Organization (NMFS/NOAA) lead federal implementation of the ESA, though they are supported by other federal agencies, including the U.S. Environmental Protection Agency (US EPA). Section 7 of the ESA directs all Federal agencies to conserve endangered and threatened species and to use their authorities to ensure actions do not jeopardized the further existence of threatened and endangered species or adversely modify designated critical habitats. As part of the Section 7 coordination, federal agencies work with USFWS and NMFS to identify species found within the jurisdiction of the United that could be affected by actions carried out by the agency.    Of note, the US EPA\u2019s Office of Pesticide Programs (OPP) is responsible for ensuring that Agency actions under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) are in compliance with ESA. 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Range areas represent more generalized habitat where species are or could be found based on the best available information. For some species, best available information is based on site specific surveys. For others, it will be historical location information based on political boundaries. These areas are, therefore, less geographically explicit than critical habitat. Consideration of both the species range and critical habitat ensures the conservation of the ecosystems upon which endangered and threatened species depend.    To support EPA\u2019s implementation of ESA, critical habitat and range data for species listed under ESA Section 7 were obtained by the US EPA from the USFWS Environmental Conservation Online System (ECOS) database in November 2020. These data were supplemented with areas provided by NOAA\u2019s National Marine Fisheries Service (NMFS) where NOAA has species authority. 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The crystalline-rock aquifer underlies the Milford-Souhegan glacial-drift (MSGD) aquifer\n(a high water-producing aquifer) and the Savage Municipal Water-Supply Well Superfund site. \nResidential water-supply wells are within one-quarter of a mile of the PCE-contaminated \nmonitoring wells and many are likely installed in similar rock types and formations as those of\nthe monitoring wells. The need to understand and quantify flow and transport in the crystalline-\nrock aquifer is crucial in assessing strategies for remediation. The current, area-wide model \nsimulates flow in the crystalline-rock aquifer and covers a much larger area than previous models\nwith the goal of improving the computation of groundwater flow from distal locations to the \nresidential wells and the area. This USGS data release contains all of the input and output files\n for the simulations described in the associated model documentation report \n(https://doi.org/10.3133/sir20205137).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7J102FK","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.226705a8-2a0e-4dda-8e36-84d2074d94c5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_226705a8-2a0e-4dda-8e36-84d2074d94c5","keyword":["Groundwater","Groundwater Model","InlandWaters","MOC3D","MODFLOW-2005","MODPATH","Milford","Milford-Souhegan River Valley","New Hampshire","Savage Municipal Water Supply Well Superfund site","Solute transport","USGS:226705a8-2a0e-4dda-8e36-84d2074d94c5","environment","geoscientificInformation","inlandWaters","remediation","usgsgroundwatermodel"],"modified":"2025-08-05T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-71.761976, 42.800697, -71.642849, 42.889263","theme":["geospatial"],"title":"MODFLOW-2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund Site, Milford, New Hampshire"},"description":"The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and\nthe New Hampshire Department of Environmental Services, developed a model for used with \nMODFLOW-2005 and MODPATH5 to evaluate groundwater flow and advective transport under\npre- and post-remediation conditions in the crystalline-rock aquifer in the vicinity of the Savage\nMunicipal Water-Supply Well Superfund site Milford, New Hampshire. In addition, a previously\ndeveloped model (https://doi.org/10.3133/sir20045176 and https://doi.org/10.3133/ofr20121079)\nwas used with MOC3D to evaluate the solute-transport of tetrachloroethylene (PCE). In 2010 \nPCE, a chlorinated volatile organic compound, was detected in groundwater from monitoring \nwells tapping the deep (more than 300 feet below land surface) fractures in a crystalline-rock \naquifer. The crystalline-rock aquifer underlies the Milford-Souhegan glacial-drift (MSGD) aquifer\n(a high water-producing aquifer) and the Savage Municipal Water-Supply Well Superfund site. \nResidential water-supply wells are within one-quarter of a mile of the PCE-contaminated \nmonitoring wells and many are likely installed in similar rock types and formations as those of\nthe monitoring wells. The need to understand and quantify flow and transport in the crystalline-\nrock aquifer is crucial in assessing strategies for remediation. The current, area-wide model \nsimulates flow in the crystalline-rock aquifer and covers a much larger area than previous models\nwith the goal of improving the computation of groundwater flow from distal locations to the \nresidential wells and the area. 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Filenames have a consistent naming convenion, as follows:\nSpecies name + island + file type + climate trajectory + year.TIF, where the following definitions apply:\nSpecies name = abbreviated code representing genus and species;\nIsland = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai);\nFile type = one of 3 file types: \n(1) RANGE = present species range as of year 2000,\n(2) 80 PCT = binary raster of habitat suitability,\n(3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; \nClimate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); \nYear = one of the following years: 2000, 2040, 2070, or 2090.\nFor example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. 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Filenames have a consistent naming convenion, as follows:\nSpecies name + island + file type + climate trajectory + year.TIF, where the following definitions apply:\nSpecies name = abbreviated code representing genus and species;\nIsland = 1 of the main 7 Hawaiian Islands (Hawaii, Maui, Kahoolwe, Lanai, Molokai, Oahu, and Kauai);\nFile type = one of 3 file types: \n(1) RANGE = present species range as of year 2000,\n(2) 80 PCT = binary raster of habitat suitability,\n(3) CHANGE TO 80 = raster showing the change in suitability between the year 2000 and the year indicated in the file name; \nClimate trajectory = lower (concave upward trajectory of change in rainfall and temperature over the century), middle (linear change in rainfall and temperature), upper (concave downward trajectory of change in rainfall and temperature), or future (where all three trajectories converge in 2090); \nYear = one of the following years: 2000, 2040, 2070, or 2090.\nFor example, consider this filename: Acakoa Hawaii 80 pct future2090.tif. 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Finally, we used a Monte Carlo simulation to incorporate variation in annual sea level due to the El Nino-Southern Oscillation.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P96R8MZQ","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6022fef6d34e31ed20c872d2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6022fef6d34e31ed20c872d2","keyword":["Pacific Ocean","Pohnpei","Senyavin Islands","Tidal Mangrove Forest","USGS:6022fef6d34e31ed20c872d2","biota","digital elevation model","environment","geospatial datasets","sea-level change"],"modified":"2026-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"158.1110, 6.7834, 158.3394, 6.9860","theme":["geospatial"],"title":"Mangrove elevation and species' responses to sea-level rise across Pohnpei, Federated States of Micronesia"},"description":"Future sea-level rise poses a risk to mangrove forests. To better understand potential vulnerability, we developed a new numerical model of soil elevation for mangrove forests. We used the model to generate projections of elevation and mangrove forest composition change under four sea-level rise scenarios through 2100 (37, 52, 67, and 117 cm by 2100). We employed a data-driven modeling approach, utilizing new and existing data to inform model parameters. The model was calibrated using dated soil cores and used a spin-up period to establish the soil column prior to future projections. Additional field data, including water level monitoring and elevation surveys, were used to estimate the initial elevation of the mangrove forest relative to mean sea level, and forest inventory plots were used to estimate mangrove productivity. Finally, we used a Monte Carlo simulation to incorporate variation in annual sea level due to the El Nino-Southern Oscillation.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/55e875ba-945d-47f9-930d-fd5459518084","harvest_record_raw":"https://catalog.data.gov/harvest_record/55e875ba-945d-47f9-930d-fd5459518084/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6022fef6d34e31ed20c872d2","keyword":["Pacific Ocean","Pohnpei","Senyavin Islands","Tidal Mangrove Forest","USGS:6022fef6d34e31ed20c872d2","biota","digital elevation model","environment","geospatial datasets","sea-level change"],"last_harvested_date":"2026-10-05T03:53:05.046900","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":2,"publisher":"U.S. Geological Survey","slug":"mangrove-elevation-and-species-responses-to-sea-level-rise-across-pohnpei-federated-states","spatial_centroid":{"lat":6.86444,"lon":158.20236},"spatial_shape":{"coordinates":[[[158.111,6.7834],[158.111,6.986],[158.3394,6.986],[158.3394,6.7834],[158.111,6.7834]]],"type":"Polygon"},"theme":["geospatial"],"title":"Mangrove elevation and species' responses to sea-level rise across Pohnpei, Federated States of Micronesia","type":"dataset"},{"_score":9.954372,"_sort":[1791172272590,9.954372,0,"b475ef99-041c-4780-982d-c191a1c21f37"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jose Pablo Ortiz Partida","hasEmail":"mailto:joportiz@ucdavis.edu"},"description":"This dataset contains five general categories that contain the water related elements of the Rio Grande/Bravo basin.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1BVDAPW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5926d7f0e4b0b7ff9fb48a0e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5926d7f0e4b0b7ff9fb48a0e","keyword":["Rio Grande","River","USGS:5926d7f0e4b0b7ff9fb48a0e","climate change","environment","geospatial datasets","hydrology","river","streamflow"],"modified":"2026-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5400, 25.8300, -97.1400, 31.8000","theme":["geospatial"],"title":"Rio Grande-Rio Bravo Basin Subset Data"},"description":"This dataset contains five general categories that contain the water related elements of the Rio Grande/Bravo basin.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f370f4ed-0c1f-4049-8c8d-b9191c005788","harvest_record_raw":"https://catalog.data.gov/harvest_record/f370f4ed-0c1f-4049-8c8d-b9191c005788/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5926d7f0e4b0b7ff9fb48a0e","keyword":["Rio Grande","River","USGS:5926d7f0e4b0b7ff9fb48a0e","climate change","environment","geospatial datasets","hydrology","river","streamflow"],"last_harvested_date":"2026-10-05T03:51:12.590614","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"rio-grande-rio-bravo-basin-subset-data","spatial_centroid":{"lat":28.218,"lon":-102.78},"spatial_shape":{"coordinates":[[[-106.54,25.83],[-106.54,31.8],[-97.14,31.8],[-97.14,25.83],[-106.54,25.83]]],"type":"Polygon"},"theme":["geospatial"],"title":"Rio Grande-Rio Bravo Basin Subset Data","type":"dataset"},{"_score":9.696236,"_sort":[1791172241230,9.696236,0,"61edcd47-baa3-4017-a1a2-2dd1557033b9"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adrienne Wooten","hasEmail":"mailto:amwootte@ou.edu"},"description":"Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet\u2019s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM biases and to provide information at finer spatial scales than that of a GCM, thereby yielding data products often considered more suitable for use in climate impacts-related applications.\nThis collection of statistically downscaled future climate projections includes 81 sets of SD-processed projections of daily high temperature, daily low temperature, and daily total precipitation across the south-central United States. The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). Both historical baseline files (1981-2005) and future projections (2006-2099) are provided, as appropriate.\nThough not exhaustive, these downscaled climate projections for the south central US region represent a range of potential future climate trajectories that can serve as a component of climate impacts research studies. That 81 sets of future projections, and not just one, are provided is indicative that some uncertainties exist regarding the trajectory of the 21st century climate change, though all show notable warming. Uncertainties in how human activity may change future atmospheric composition are represented by the different RCP scenarios. Differences in how sensitive the surface climate of this region will be to atmospheric composition changes are sampled by the use of different GCMs. Similarly, because each SD method has different performance characteristics and observational products differ, the use of different SD techniques and training data set combinations acknowledges that SD methodological choices influence the value-added statistically refined climate projection data products. Applied researchers may explore aspects of their applications\u2019 sensitivities to some climate projection uncertainties by sampling from these 81 sets of SD data products. However, this collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the south central US region.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/cprep","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6778032fd34edab7af6e2387.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6778032fd34edab7af6e2387","keyword":["USGS:6778032fd34edab7af6e2387","atmosphere","climate","climate change","climate projections","climatology","dataset","downscaling","environment","geospatial datasets","meteorology","service"],"modified":"2026-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-108.6328, 26.0500, -90.0879, 39.9500","theme":["geospatial"],"title":"South Central Climate Projections Evaluation Project (C-PrEP)"},"description":"Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet\u2019s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM biases and to provide information at finer spatial scales than that of a GCM, thereby yielding data products often considered more suitable for use in climate impacts-related applications.\nThis collection of statistically downscaled future climate projections includes 81 sets of SD-processed projections of daily high temperature, daily low temperature, and daily total precipitation across the south-central United States. The 81 sets can be viewed as a 3x3x3x3 matrix, created based on a combination of three GCMs from the CMIP5 archive (CCSM4, MIROC5, and MPI-ESM-LR), each of which simulated 21st century climate responses for three different future atmospheric composition scenarios (known as representative concentration pathways or RCPs 2.6, 4.5, and 8.5). Three different SD techniques were employed, and each used three gridded observation-based data products to train (i.e. calibrate) the SD methods. The three downscaling techniques include a delta method (DeltaSD), an equi-distant quantile mapping method (EDQM), and a piecewise asynchronous regression method (PARM). The observational data products used for training were Daymet v. 2.1, Livneh v. 1.2, and PRISM AN81d v. D1. The resulting SD-processed projections are on a 10 km by 10 km grid covering the south-central United States (all of AR, KS, LA, NM, OK, TX, and portions of CO and MO). Both historical baseline files (1981-2005) and future projections (2006-2099) are provided, as appropriate.\nThough not exhaustive, these downscaled climate projections for the south central US region represent a range of potential future climate trajectories that can serve as a component of climate impacts research studies. That 81 sets of future projections, and not just one, are provided is indicative that some uncertainties exist regarding the trajectory of the 21st century climate change, though all show notable warming. Uncertainties in how human activity may change future atmospheric composition are represented by the different RCP scenarios. Differences in how sensitive the surface climate of this region will be to atmospheric composition changes are sampled by the use of different GCMs. Similarly, because each SD method has different performance characteristics and observational products differ, the use of different SD techniques and training data set combinations acknowledges that SD methodological choices influence the value-added statistically refined climate projection data products. Applied researchers may explore aspects of their applications\u2019 sensitivities to some climate projection uncertainties by sampling from these 81 sets of SD data products. However, this collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the south central US region.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/916751c9-e3e6-4175-94de-58e0eef29075","harvest_record_raw":"https://catalog.data.gov/harvest_record/916751c9-e3e6-4175-94de-58e0eef29075/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6778032fd34edab7af6e2387","keyword":["USGS:6778032fd34edab7af6e2387","atmosphere","climate","climate change","climate projections","climatology","dataset","downscaling","environment","geospatial datasets","meteorology","service"],"last_harvested_date":"2026-10-05T03:50:41.230840","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"south-central-climate-projections-evaluation-project-c-prep","spatial_centroid":{"lat":31.610000000000003,"lon":-101.21484000000001},"spatial_shape":{"coordinates":[[[-108.6328,26.05],[-108.6328,39.95],[-90.0879,39.95],[-90.0879,26.05],[-108.6328,26.05]]],"type":"Polygon"},"theme":["geospatial"],"title":"South Central Climate Projections Evaluation Project (C-PrEP)","type":"dataset"},{"_score":8.724777,"_sort":[1791172142054,8.724777,1,"dd71b40f-5bbe-4d59-ad74-b1800a70714f"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jason M. Fine","hasEmail":"mailto:jmfine@usgs.gov"},"description":"An existing three-dimensional model (MODFLOW-2000) by Fine, Petkewich, and Campbell (2017) (https://doi.org/10.3133/sir20175128) was used to evaluate 7 water-management scenarios and predict the effects on the groundwater flow and groundwater-level conditions in the Mount Pleasant, South Carolina area. This model was originally developed in 2007, by Petkewich and Campbell (https://pubs.usgs.gov/publication/sir20075126), then updated and recalibrated to conditions from 1900 to 2015. Results of six previous scenario simulations (scenarios 1-6) for the Mount Pleasant Water Works are published in a U.S. Geological Survey (USGS) Scientific Investigations Report (https://doi.org/10.3133/sir20175128). The archived model input and output files are available in a USGS data release (https://doi.org/10.5066/F7S181FC). Seven additional MODFLOW-2000 scenarios (numbered 7-13), using this updated and recalibrated model, were developed to evaluate different withdrawal strategies which are included in this data release: (7) Mount Pleasant Waterworks bringing online a new well (located at the old well 5 location) at 3.51 million gallons per day (Mgal/d) in 2025; (8) Maximizing withdrawals from Mount Pleasant Waterworks wells 2 and 5 (3.51 Mgal/d each) in 2020 and 2025, respectively; (9) Same as Scenario 7, but removing well 3 from production in 2025; (10) Same as Scenario 9, but removing well 4 from production in 2025 (11) Same as Scenario 7, but converting well 3 to an injection well in 2025 (12) Same as Scenario 11, but converting well 4 to an injection well in 2030; and (13) Same as scenario 8, but with two injection wells added (one in 2025 and one in 2035) to Mount Pleasant Waterworks well field. Nine alternate simulations for scenarios 11-13 (three MODFLOW and six MODPATH) were done to evaluate the effects of different porosity on the groundwater flow system, water levels, and the time-of-travel of particles from injection wells to the main water source. This USGS data release contains all the input and output files for the simulations described above and in the readme.txt file of this data release (https://doi.org/10.5066/P9GZEE4E).\n","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9GZEE4E","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6ba49343-2436-45ea-b0d8-ec6f7d84577b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6ba49343-2436-45ea-b0d8-ec6f7d84577b","keyword":["Charleston County","Groundwater","Groundwater Model","InlandWaters","MODFLOW-2000","MODPATH","Middendorf aquifer","Mount Pleasant","South Carolina","USGS:6ba49343-2436-45ea-b0d8-ec6f7d84577b","environment","geoscientificInformation","inlandWaters","usgsgroundwatermodel"],"modified":"2024-10-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-83.7864, 29.9235, -76.2895, 35.8812","theme":["geospatial"],"title":"MODFLOW-2000 and MODPATH model data sets used in scenarios of groundwater flow and pumping (1900-2500) near Mount Pleasant, South Carolina"},"description":"An existing three-dimensional model (MODFLOW-2000) by Fine, Petkewich, and Campbell (2017) (https://doi.org/10.3133/sir20175128) was used to evaluate 7 water-management scenarios and predict the effects on the groundwater flow and groundwater-level conditions in the Mount Pleasant, South Carolina area. This model was originally developed in 2007, by Petkewich and Campbell (https://pubs.usgs.gov/publication/sir20075126), then updated and recalibrated to conditions from 1900 to 2015. Results of six previous scenario simulations (scenarios 1-6) for the Mount Pleasant Water Works are published in a U.S. Geological Survey (USGS) Scientific Investigations Report (https://doi.org/10.3133/sir20175128). The archived model input and output files are available in a USGS data release (https://doi.org/10.5066/F7S181FC). Seven additional MODFLOW-2000 scenarios (numbered 7-13), using this updated and recalibrated model, were developed to evaluate different withdrawal strategies which are included in this data release: (7) Mount Pleasant Waterworks bringing online a new well (located at the old well 5 location) at 3.51 million gallons per day (Mgal/d) in 2025; (8) Maximizing withdrawals from Mount Pleasant Waterworks wells 2 and 5 (3.51 Mgal/d each) in 2020 and 2025, respectively; (9) Same as Scenario 7, but removing well 3 from production in 2025; (10) Same as Scenario 9, but removing well 4 from production in 2025 (11) Same as Scenario 7, but converting well 3 to an injection well in 2025 (12) Same as Scenario 11, but converting well 4 to an injection well in 2030; and (13) Same as scenario 8, but with two injection wells added (one in 2025 and one in 2035) to Mount Pleasant Waterworks well field. Nine alternate simulations for scenarios 11-13 (three MODFLOW and six MODPATH) were done to evaluate the effects of different porosity on the groundwater flow system, water levels, and the time-of-travel of particles from injection wells to the main water source. This USGS data release contains all the input and output files for the simulations described above and in the readme.txt file of this data release (https://doi.org/10.5066/P9GZEE4E).\n","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/75f2e194-2b9b-4729-ba49-c89ea5e235ce","harvest_record_raw":"https://catalog.data.gov/harvest_record/75f2e194-2b9b-4729-ba49-c89ea5e235ce/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6ba49343-2436-45ea-b0d8-ec6f7d84577b","keyword":["Charleston County","Groundwater","Groundwater Model","InlandWaters","MODFLOW-2000","MODPATH","Middendorf aquifer","Mount Pleasant","South Carolina","USGS:6ba49343-2436-45ea-b0d8-ec6f7d84577b","environment","geoscientificInformation","inlandWaters","usgsgroundwatermodel"],"last_harvested_date":"2026-10-05T03:49:02.054086","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"modflow-2000-and-modpath-model-data-sets-used-in-scenarios-of-groundwater-flow-and-pumping","spatial_centroid":{"lat":32.306580000000004,"lon":-80.78764},"spatial_shape":{"coordinates":[[[-83.7864,29.9235],[-83.7864,35.8812],[-76.2895,35.8812],[-76.2895,29.9235],[-83.7864,29.9235]]],"type":"Polygon"},"theme":["geospatial"],"title":"MODFLOW-2000 and MODPATH model data sets used in scenarios of groundwater flow and pumping (1900-2500) near Mount Pleasant, South Carolina","type":"dataset"},{"_score":20.535145,"_sort":[1791171957947,20.535145,0,"99fd0b0a-398c-4b19-8137-94b6a4a9e2f2"],"access_level":"public","dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Iliansherry Santiago Gonzalez","hasEmail":"mailto:isantiagogonzalez@usgs.gov"},"description":"Microplastics (MPs), plastic particles ranging from 1 micrometer (\u00b5m) to 5 millimeters (mm), are considered contaminants of emerging concern (CEC) and are ubiquitous in the environment. MPs occur in a variety of sizes, shapes and polymer types and have been detected in multiple freshwater matrices, including surface water, groundwater, and drinking water. Development of standardized analytical methods for freshwater matrices and information on the occurrence and characterization of MPs in groundwater systems are ongoing.\nIn 2025, the U.S. Geological Survey (USGS) Central Midwest Water Science Center collected samples for MP analysis near the Independence, Missouri and at McBaine Bottoms, near Columbia, Missouri. Data were collected near Independence from the alluvial aquifer and from a groundwater collection system that receives water from several municipal supply wells prior to drinking water treatment (composite site). Sampling included 26 groundwater monitoring well samples, 1 composite site sample, and 6 quality assurance and quality control (QA/QC) samples. Data were collected from the alluvial aquifer and outflow canal at McBaine Bottoms, near Columbia, Missouri. Sampling included 11 groundwater monitoring well samples, 3 outflow canal samples, and 4 QA/QC samples. Particle identification and characterization were provided by the Northern Illinois University (NIU) Microplastic Laboratory.\nThe datasets include particle-level measurements and identifications for MPs and non-microplastic particles provided by the NIU Microplastic Laboratory, as well as summarization of MP data derived from the particle data by particle count, size range, polymer type, sample type, and sampling location. 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There is substantial interest in this \u201cblue carbon\u201d as a carbon mitigation strategy despite the major threat that sea level rise (SLR) poses to these habitats. Many projections of habitat and carbon change with SLR emphasize the potential for inland marsh migration and increased rates of marsh carbon sequestration but do not consider carbon fluxes associated with habitat conversion. We integrated existing data and models to develop a spatial model for predicting habitat and carbon changes due to SLR in six mid-Atlantic U.S. states likely to face coastal habitat loss over the next century due to low tidal ranges and sediment supply. Our primary model projection using an intermediate SLR scenario (1.2 m SLR by 2104) predicts loss of 83% of existing coastal marshes and 3026% of existing seagrasses in the study area. In addition 270000 hectares of forest and forested wetlands in low-lying coastal areas will convert to coastal marshes. These SLR-driven habitat changes cause the study area to shift from a carbon sink to a source in our primary model projection. Given the many uncertainties about the habitat and carbon changes represented in our model we also identified the parameters and assumptions that most strongly affected the model results to inform future research needs. These included: land availability for inland marsh migration the baseline extent and location of coastal marshes proportion of stored carbon emitted from lost habitats (coastal marsh sediments or terrestrial biomass carbon) and methane emissions from freshwater habitats. The study area switched from a net carbon sink to a net carbon source under SLR for all but three model runs; in those runs net carbon sequestration declined by 58-99%.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13E8UGL","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.63f4f3a6d34efa0476b04c72.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_63f4f3a6d34efa0476b04c72","keyword":["Delaware","Maryland","New Jersey","New York","North Carolina","USGS:63f4f3a6d34efa0476b04c72","Virginia","blue carbon","climate","climate change","coastal","environment","external research support","habitat change","salt marsh","sea level","seagrasses"],"modified":"2026-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-79.4531, 36.4566, -70.3125, 45.1511","theme":["geospatial"],"title":"Coastal habitat and blue carbon projections data download"},"description":"Coastal marshes and seagrass beds store millions of tons of carbon in their sediments and sequester carbon at higher per-area rates than most terrestrial ecosystems. 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These SLR-driven habitat changes cause the study area to shift from a carbon sink to a source in our primary model projection. Given the many uncertainties about the habitat and carbon changes represented in our model we also identified the parameters and assumptions that most strongly affected the model results to inform future research needs. These included: land availability for inland marsh migration the baseline extent and location of coastal marshes proportion of stored carbon emitted from lost habitats (coastal marsh sediments or terrestrial biomass carbon) and methane emissions from freshwater habitats. 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The dataset contains 2,445,875 records from nurseries in at least 2,795 unique locations, with the majority of catalogs published between 1890-1950. Nurseries were located in all conterminous states but were concentrated in the eastern U.S. and California. We identified 19,140 unique horticultural taxa, of which 8,642 matched taxa in the USDA Plants database. The USDA Plants database is limited to native and naturalized taxa in the US. Native or introduced status was listed in USDA Plants for 7,018 of included taxa, while 1,642 had an unknown status. The remaining 10,498 taxa are not naturalized according to USDA Plants or are of varieties of native and introduced taxa that did not match to USDA Plants taxonomy. The majority of taxa in the Historical Plant Sales (HPS) database with an identified status are native (65.5%; 4,596 of 7,018 taxa), of which 393 taxa are reported as invasive outside of the U.S. Of the 2381 introduced taxa, 1,103 (46.3%) are reported as invasive somewhere globally. Despite a richer pool of native taxa, most cataloged plant records with an identified status were of introduced taxa (54.1%; 1,045,684 of 1,933,925 records). Plants reported as invasive somewhere globally comprised a large portion of records with an identified status (38.7%; 747,953 of 1,933,925 records) underscoring the large role of ornamental introductions in facilitating plant invasions.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.7275/0t5v-5r18","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.64872b51d34ef77fcafe1230.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64872b51d34ef77fcafe1230","keyword":["Biodiversity Heritage Library","Horticulture","Introduced plants","Invasive plants","Native plants","Nursery sales","Ornamental plants","USGS:64872b51d34ef77fcafe1230","datasets","environment"],"modified":"2026-10-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.5078, 25.2447, -66.7969, 48.8647","theme":["geospatial"],"title":"Historical plant sales (HPS) database: Documenting the spatiotemporal history of plant sales in the conterminous U.S."},"description":"We downloaded, cleaned, and combined records from Biodiversity Heritage Library\u2019s (BHL) Seed and Nursery Catalog Collection  with data from Restoring American Gardens: An encyclopedia of heirloom ornamental plants, 1640-1940 (RAG; Adams 2004) to create a single database of historical nursery sales in the U.S. Each record represents an individual taxon offered for sale at an individual time in a specific nursery\u2019s catalog. 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Range areas represent more generalized habitat where species are or could be found based on the best available information. For some species, best available information is based on site specific surveys. For others, it will be historical location information based on political boundaries. These areas are, therefore, less geographically explicit than critical habitat. Consideration of both the species range and critical habitat ensures the conservation of the ecosystems upon which endangered and threatened species depend.    To support EPA\u2019s implementation of ESA, critical habitat and range data for species listed under ESA Section 7 were obtained by the US EPA from the USFWS Environmental Conservation Online System (ECOS) database in November 2020. These data were supplemented with areas provided by NOAA\u2019s National Marine Fisheries Service (NMFS) where NOAA has species authority. For NMFS species not found in either location, a request was made directly to the NMFS scientists. 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