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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. 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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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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. 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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. 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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. 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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. 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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":"2026-09-02","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. 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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. 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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. 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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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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. 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This web portal is built using the github repository at https://github.com/usnistgov/SEA-DATA.\nThe purpose of the National Advanced Spectrum and Communications Test Network (NASCTN) Citizens Broadband Radio Service (CBRS) Sharing Ecosystem Assessment (SEA) project is to provide data-driven insight into the CBRS sharing ecosystem\u2019s effectiveness between commercial and Department of Defense (DoD) incumbent systems, and to track changes in the spectrum environment over time. CBRS is the \u201cfirst of a kind\u201d nationwide shared spectrum ecosystem in the 3550-3700 MHz band. The implementation of this project included the installation of sensor systems at selected sites on the east and the west coast of the United States, along with a control and prototyping system in Boulder, Colorado. This data provides time and frequency selective power measurements from 3530 MHz -3710 MHz from summer 2024 to summer of 2026. This data is targeted at the commercial, military and civilian stakeholders of Citizen's Broadband Radio Service (CBRS) that would benefit from in-depth real time and longitudinal analysis of a shared spectrum environment. These data were collected with sensors running the IEEE 802.15.22.3 Spectrum Characterization and Occupancy Sensing (SCOS) sensor system. In addition, the data is formatted and saved using  SigMF (signal metadata format). This project was a collaboration between NIST, NTIA / ITS, NASA and MITRE.","distribution":[{"accessURL":"https://github.com/usnistgov/SEA-DATA","description":"The github repository that is used to build the nist.pages site.","format":"github repository","title":"SEA-Data Web Portal Github Repository"},{"accessURL":"https://pages.nist.gov/SEA-DATA/","description":"A static website describing how to access data from the NASCTN CBRS SEA program.","format":"html","title":"NASCTN CBRS SEA DATA INSTRUCTIONS"}],"identifier":"ark:/88434/mds2-4219","issued":"2026-06-24","keyword":["CBRS","ESC","NASCTN","Naval Radar","RF","SAS","Wireless"],"landingPage":"https://data.nist.gov/od/id/mds2-4219","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-06-01 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.2359.pdf","https://www.mitre.org/sites/default/files/2026-01/PR-25-3159-spectrum-monitoring-sensor-radio-service-assessment.pdf"],"theme":["Advanced Communications:Wireless (RF)"],"title":"SEA-DATA Web Portal"},"description":"Data Access Instructions for The NASCTN CBRS SEA program. This web portal is built using the github repository at https://github.com/usnistgov/SEA-DATA.\nThe purpose of the National Advanced Spectrum and Communications Test Network (NASCTN) Citizens Broadband Radio Service (CBRS) Sharing Ecosystem Assessment (SEA) project is to provide data-driven insight into the CBRS sharing ecosystem\u2019s effectiveness between commercial and Department of Defense (DoD) incumbent systems, and to track changes in the spectrum environment over time. CBRS is the \u201cfirst of a kind\u201d nationwide shared spectrum ecosystem in the 3550-3700 MHz band. The implementation of this project included the installation of sensor systems at selected sites on the east and the west coast of the United States, along with a control and prototyping system in Boulder, Colorado. This data provides time and frequency selective power measurements from 3530 MHz -3710 MHz from summer 2024 to summer of 2026. This data is targeted at the commercial, military and civilian stakeholders of Citizen's Broadband Radio Service (CBRS) that would benefit from in-depth real time and longitudinal analysis of a shared spectrum environment. These data were collected with sensors running the IEEE 802.15.22.3 Spectrum Characterization and Occupancy Sensing (SCOS) sensor system. In addition, the data is formatted and saved using  SigMF (signal metadata format). This project was a collaboration between NIST, NTIA / ITS, NASA and MITRE.","distribution_titles":["SEA-Data Web Portal Github Repository","NASCTN CBRS SEA DATA INSTRUCTIONS"],"harvest_record":"https://catalog.data.gov/harvest_record/dd388338-4d7a-446d-820e-31d773bf9ed6","harvest_record_raw":"https://catalog.data.gov/harvest_record/dd388338-4d7a-446d-820e-31d773bf9ed6/raw","has_download":false,"has_spatial":false,"identifier":"ark:/88434/mds2-4219","keyword":["CBRS","ESC","NASCTN","Naval Radar","RF","SAS","Wireless"],"last_harvested_date":"2026-09-02T19:19:39.376606","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"176f2a2d-ca9b-41f2-8df3-d93096ebdb85","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nist.png","name":"National Institute of Standards and Technology","organization_type":"Federal Government","slug":"nist"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"sea-data-web-portal","spatial_centroid":null,"spatial_shape":null,"theme":["Advanced Communications:Wireless (RF)"],"title":"SEA-DATA Web Portal","type":"dataset"},{"_score":13.979166,"_sort":[1788376776584,13.979166,0,"d27c5b54-ac1a-4a97-b940-592d8ec07e37"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Noah Schlossberger","hasEmail":"mailto:noah.schlossberger@nist.gov"},"description":"We investigate the role of magnetic sublevels in Autler\u2013Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent $m_J$ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment.\nWe develop and diagonalize a Hamiltonian including all coupled m_J sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the m_J-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied.\nThese results demonstrate that RF polarization fundamentally modifies Autler\u2013Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/4157_README.txt","mediaType":"text/plain","title":"readme"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig2.csv","mediaType":"text/csv","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig3.csv","mediaType":"text/csv","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig4.csv","mediaType":"text/csv","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4157/Fig5.csv","mediaType":"text/csv","title":"Figure 5"}],"identifier":"ark:/88434/mds2-4157","issued":"2026-07-23","keyword":["Rydberg atoms","atomic physics","electric field","fields strength","receivers","volts/meter"],"landingPage":"https://data.nist.gov/od/id/mds2-4157","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-04-13 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"title":"Data associated with \"Resolving magnetic-sublevel structure in Rydberg Autler-Townes spectra with arbitrary RF polarization\""},"description":"We investigate the role of magnetic sublevels in Autler\u2013Townes spectra of Rydberg atoms driven by radio-frequency (RF) fields with arbitrary polarization. While conventional treatments predict two symmetric sidebands from independent $m_J$ transitions, experiments have reported additional unexplained spectral features. We show that these arise from elliptical RF polarization, which coherently couples multiple magnetic sublevels and requires a full multi-level treatment.\nWe develop and diagonalize a Hamiltonian including all coupled m_J sublevels, predicting polarization-dependent degeneracies that produce two, three, or four resolved peaks. Using long-wavelength transitions and an anechoic environment we realize homogeneous RF fields that for the first time enable complete resolution of the m_J-dependent dressed states. We observe excellent agreement with theory as the RF ellipticity is varied.\nThese results demonstrate that RF polarization fundamentally modifies Autler\u2013Townes spectra and provide a consistent framework for interpreting magnetic-sublevel structure, with implications for Rydberg-based RF electrometry and polarimetry.","distribution_titles":["readme","Figure 2","Figure 3","Figure 4","Figure 5"],"harvest_record":"https://catalog.data.gov/harvest_record/8e91ef57-be55-4f5d-bfb7-cb1137aeb050","harvest_record_raw":"https://catalog.data.gov/harvest_record/8e91ef57-be55-4f5d-bfb7-cb1137aeb050/raw","has_download":true,"has_spatial":false,"identifier":"ark:/88434/mds2-4157","keyword":["Rydberg atoms","atomic physics","electric field","fields strength","receivers","volts/meter"],"last_harvested_date":"2026-09-02T19:19:36.584464","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"176f2a2d-ca9b-41f2-8df3-d93096ebdb85","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nist.png","name":"National Institute of Standards and Technology","organization_type":"Federal Government","slug":"nist"},"parent_identifier":null,"popularity":0,"publisher":"National Institute of Standards and Technology","slug":"data-associated-with-resolving-magnetic-sublevel-structure-in-rydberg-autler-townes-spectr","spatial_centroid":null,"spatial_shape":null,"theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"title":"Data associated with \"Resolving magnetic-sublevel structure in Rydberg Autler-Townes spectra with arbitrary RF polarization\"","type":"dataset"},{"_score":3.0020103,"_sort":[1788376775972,3.0020103,0,"d034f1c1-28d0-4c2e-b434-41f027b9904a"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["006:55"],"contactPoint":{"fn":"Yamil Simon","hasEmail":"mailto:yamil.simon@nist.gov"},"description":"This mass spectrometry (MS) data set encompasses spectra of the extractables and leachables (E&L) of 100 polymer materials sourced from the Scientific Polymer Products Inc Polymer Samples Kit (see: SciPoly_PolymerSampleKit205_Items.xlsx). Extractions of these polymers were carried out using three HPLC-grade solvents of varying polarity (water, isopropanol, and hexane) with 10 mg polymer/mL solvent for 24 hours at 50 \u00b0C with shaking. To account for solvent volatility, extractions were carried out in 8 mL pressure tubes from Ace Glass Incorporated (Product #8648-25) (rated at 150 psig).\n\nExtraction solutions were subsequently analyzed using three ionization techniques: Electron Ionization (EI) with GC-MS and Electrospray Ionization (ESI) and Atmospheric Pressure Chemical Ionization (APCI) via LC-MS/MS on an Agilent QTOF 6530 and a Bruker timsTOF Pro 2, respectively.\n\nSeveral precautionary measures had to be taken following the extraction procedure. Extracts were centrifuged and filtered using 0.45-micron syringe filters to prevent prohibitively large particles from interfering with chromatography and MS analysis. Moreover, a guard column was used before the LC column to avoid contamination. As such, pressure build-up would often be a concern, so the LC and guard column had to be thoroughly flushed after each analysis batch.\n\nThousands of spectra were processed using in-house software, generating lists of identifications using the most recent versions of the NIST MS libraries via both direct and hybrid search methods. Some monomers were detected under the given extraction conditions, and many plastic additives (e.g., plasticizers, antioxidants, and processing agents) could be identified; some common examples found in many of the polymer standards include oleamide and palmitamide. High-quality unidentified spectra obtained from the extracts are included in a library of annotated recurrent unidentified spectra (ARUS).\n\nLC Chromatographic Separation Method: LC runs were performed using reversed-phase chromatography with an ACQUITY UPLC CSH C18 column (130 \u00c5 (13.0 nm), 1.7 \u00b5m, 2.1 mm x 100 mm), mobile phase A: water, and mobile phase B: acetonitrile (both mobile phases containing 0.1 % formic acid). The chromatographic separation method was as follows: injection volume: 10 \u00b5L, column temperature: 35\u00b0C, flow rate: 0.400 mL/min, and gradient: initial condition of 1.00% B, hold for 1.00 min, linear ramp to 80.00% B in 8.00 min, linear ramp to 99.00% B in 3.00 min, hold for 1.50 min, linear ramp to 1.00% B in 0.50 min, hold for 1.00 min. GC Chromatographic Separation Method: Spectra were obtained using an Agilent 5977B instrument, with GC runs performed using a Restek Rxi-5Sil MS GC Capillary Column (15 m x 250 \u00b5m x 0.25 \u00b5m) under 2 mL/min He flow. The initial oven temperature was 40\u00b0C (held for 3 min), with a ramp rate of 20\u00b0C/min, final temperature of 320\u00b0C, and final hold time of 3.75 min. \n\nReferences:\n\nBerthelette, K.; Walter, T. H. Column screening for the UPLC separation of plastic additives as part of extractables and leachables workflows; 720006777EN; Waters Corporation, MA, USA, 2020.\n\nLee, P. J.; Di Gioia, A. J. Rapid analysis of 25 common polymer additives; 720002488EN; Waters Corporation, Milford, MA, USA, 2008.\n\nSim\u00f3n-Manso, Y.; Erisman, E. P.; Mak, T. D.; Burke, M. C.; Zuber, A.; Yang, X.; Liang, Y.; Neta, P.; Bukhari, T.; Williams, A. J.; et al. NIST Mass Spectral Libraries in the Context of the Circular Economy of Plastics. J Am Soc Mass Spectrom 2025, 36 (2), 439\u2013445. DOI: 10.1021/jasms.4c00349.\n","distribution":[{"description":"The file contains a description of the dataset directory.","downloadURL":"https://data.nist.gov/od/ds/mds2-4147/4147_README.txt","format":".txt","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4147/GC_MS_single_quad.zip","mediaType":"application/zip","title":"GC_MS_single_quad"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4147/QTOF_ESI.zip","mediaType":"application/zip","title":"QTOF_ESI"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4147/SciPoly_PolymerSampleKit205_Items.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"SciPoly_PolymerSampleKit205_Items"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-4147/timsTOF_APCI.zip","mediaType":"application/zip","title":"timsTOF_APCI"}],"identifier":"ark:/88434/mds2-4147","issued":"2026-05-13","keyword":["APCI-LC-MS/MS","ESI-LC-MS/MS","GC-MS","extractables and leachables","ion mobility","mass spectrometry","plastic-related compounds","plastics","polymers","tandem mass spectrometry"],"landingPage":"https://data.nist.gov/od/id/mds2-4147","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2026-03-19 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry","Chemistry:Analytical chemistry","Environment"],"title":"LC-MS/MS and GC-MS Measurements of Extractables and Leachables (E&L) from Polymeric Materials"},"description":"This mass spectrometry (MS) data set encompasses spectra of the extractables and leachables (E&L) of 100 polymer materials sourced from the Scientific Polymer Products Inc Polymer Samples Kit (see: SciPoly_PolymerSampleKit205_Items.xlsx). Extractions of these polymers were carried out using three HPLC-grade solvents of varying polarity (water, isopropanol, and hexane) with 10 mg polymer/mL solvent for 24 hours at 50 \u00b0C with shaking. To account for solvent volatility, extractions were carried out in 8 mL pressure tubes from Ace Glass Incorporated (Product #8648-25) (rated at 150 psig).\n\nExtraction solutions were subsequently analyzed using three ionization techniques: Electron Ionization (EI) with GC-MS and Electrospray Ionization (ESI) and Atmospheric Pressure Chemical Ionization (APCI) via LC-MS/MS on an Agilent QTOF 6530 and a Bruker timsTOF Pro 2, respectively.\n\nSeveral precautionary measures had to be taken following the extraction procedure. Extracts were centrifuged and filtered using 0.45-micron syringe filters to prevent prohibitively large particles from interfering with chromatography and MS analysis. Moreover, a guard column was used before the LC column to avoid contamination. As such, pressure build-up would often be a concern, so the LC and guard column had to be thoroughly flushed after each analysis batch.\n\nThousands of spectra were processed using in-house software, generating lists of identifications using the most recent versions of the NIST MS libraries via both direct and hybrid search methods. Some monomers were detected under the given extraction conditions, and many plastic additives (e.g., plasticizers, antioxidants, and processing agents) could be identified; some common examples found in many of the polymer standards include oleamide and palmitamide. High-quality unidentified spectra obtained from the extracts are included in a library of annotated recurrent unidentified spectra (ARUS).\n\nLC Chromatographic Separation Method: LC runs were performed using reversed-phase chromatography with an ACQUITY UPLC CSH C18 column (130 \u00c5 (13.0 nm), 1.7 \u00b5m, 2.1 mm x 100 mm), mobile phase A: water, and mobile phase B: acetonitrile (both mobile phases containing 0.1 % formic acid). The chromatographic separation method was as follows: injection volume: 10 \u00b5L, column temperature: 35\u00b0C, flow rate: 0.400 mL/min, and gradient: initial condition of 1.00% B, hold for 1.00 min, linear ramp to 80.00% B in 8.00 min, linear ramp to 99.00% B in 3.00 min, hold for 1.50 min, linear ramp to 1.00% B in 0.50 min, hold for 1.00 min. GC Chromatographic Separation Method: Spectra were obtained using an Agilent 5977B instrument, with GC runs performed using a Restek Rxi-5Sil MS GC Capillary Column (15 m x 250 \u00b5m x 0.25 \u00b5m) under 2 mL/min He flow. The initial oven temperature was 40\u00b0C (held for 3 min), with a ramp rate of 20\u00b0C/min, final temperature of 320\u00b0C, and final hold time of 3.75 min. \n\nReferences:\n\nBerthelette, K.; Walter, T. H. Column screening for the UPLC separation of plastic additives as part of extractables and leachables workflows; 720006777EN; Waters Corporation, MA, USA, 2020.\n\nLee, P. J.; Di Gioia, A. J. Rapid analysis of 25 common polymer additives; 720002488EN; Waters Corporation, Milford, MA, USA, 2008.\n\nSim\u00f3n-Manso, Y.; Erisman, E. P.; Mak, T. D.; Burke, M. C.; Zuber, A.; Yang, X.; Liang, Y.; Neta, P.; Bukhari, T.; Williams, A. J.; et al. NIST Mass Spectral Libraries in the Context of the Circular Economy of Plastics. J Am Soc Mass Spectrom 2025, 36 (2), 439\u2013445. 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