cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15787/vtt1/qsfiig
title: Mask Head Pose Validation Study Dataset (Clipped)

description: Project Description Driver head pose is an important factor in transportation safety research. As the scale of naturalistic driving studies has grown, it has become increasingly important to develop automated methods for estimating driver head pose. The objective of this study was to evaluate the capability of the currently deployed Virginia Tech Transportation Institute (VTTI) head pose estimation system (mask). To this end the following tasks were completed: Identified conditions in which the mask system estimated head pose accurately and precisely. Determined if the mask system quality confidence measure accurately identified cases in which head pose estimates should not be used. Evaluated the ability of the mask system to predict eye glance location and provide a course estimate of head pose. Identified system limitations that should be considered by researchers using mask head pose data. Data Request Scope The version of the dataset referenced herein is the "clipped" version. Per an agreement with Oak Ridge National Laboratory, the Federal Highway Administration, and the Transportation Research Board, a portion of the mask validation data and video are currently being withheld from release. Information and some public data about the study can also be accessed within the InSight website (https://insight.shrp2nds.us/projectBackground/index). Overview documentation can be accessed at https://drive.google.com/a/vt.edu/file/d/0B0WDAFahhsCGMVB1aTNXejBQOUU/. Data Specification Dataset/Dataset/Eye glance data; Front, Rear, Hands and Face video streams; Facial landmark annotations, Geometric method head rotation estimates; Mask head rotation estimates; Vehicle kinematics
DOI: 10.15787/vtt1/sgbq0l
title: Iowa State University CTRE S08: Analysis of the SHRP2 Naturalistic Driving Study Data S08-D; Assessing Lane Departures on Rural Two-Lane Curves

description: Project Description An analysis of lane departures on rural two-lane curves Data Request Scope Dataset/CSV Files with Kinematics Data; Clipped Forward Video, Clipped Rear Video, Excel File with Subsets of Driver Questionnaires, Excel File with Vehicle Data Data Specification Time-Series System.Time_Stamp vtti.timestamp vtti.file_id vtti.abs vtti.accel_x vtti.accel_x_fast vtti.accel_y vtti.accel_y_fast vtti.accel_z vtti.accel_z_fast vtti.airbag_driver vtti.alcohol_interior vtti.cruise_state vtti.year_gps vtti.month_gps computed.day_of_week computed.time_bin vtti.pdop vtti.driver_button vtti.esc vtti.elevation_gps vtti.head_confidence vtti.head_position_x vtti.head_position_x_baseline vtti.head_position_y vtti.head_position_y_baseline vtti.head_position_z vtti.head_position_z_baseline vtti.light_level vtti.left_line_right_distance vtti.right_line_left_distance vtti.right_marker_probability vtti.left_marker_type vtti.right_marker_type vtti.left_marker_probability vtti.lane_distance_off_center vtti.lane_width vtti.latitude vtti.longitude vtti.number_of_satellites vtti.pedal_gas_position vtti.pedal_brake_state vtti.gyro_y vtti.gyro_y_fast vtti.prndl vtti.range_rate_x_t0 vtti.range_rate_x_t1 vtti.range_rate_x_t2 vtti.range_rate_x_t3 vtti.range_rate_x_t4 vtti.range_rate_x_t5 vtti.range_rate_x_t6 vtti.range_rate_x_t7 vtti.range_rate_y_t0 vtti.range_rate_y_t1 vtti.range_rate_y_t2 vtti.range_rate_y_t3 vtti.range_rate_y_t4 vtti.range_rate_y_t5 vtti.range_rate_y_t6 vtti.range_rate_y_t7 vtti.object_id_t0 vtti.object_id_t1 vtti.object_id_t2 vtti.object_id_t3 vtti.object_id_t4 vtti.object_id_t5 vtti.object_id_t6 vtti.object_id_t7 vtti.gyro_x vtti.gyro_x_fast vtti.seatbelt_driver vtti.speed_gps vtti.speed_network vtti.steering_wheel_position vtti.traction_control_state vtti.turn_signal vtti.video_frame vtti.wiper vtti.gyro_z vtti.gyro_z_fast vtti.headlight Vehicle Info Vehicle year Vehicle model Vehicle make Vehicle track width Driver Info Age Gender Education level Annual miles driven Years of driving Number of moving violations Number of crashes
DOI: 10.15787/vtt1/xde0vr
title: IAP Project: Evaluation of Work Zone Safety Using the SHRP2 Naturalistic Driving Study Data

description: Project Description This research was sponsored by Federal Highway Administration Implementation Assistance Program. The purpose of this effort is to support the use of various components of the SHRP2 Naturalistic Driving Study database by personnel from CTRE/ISU. The main objective of this project is to identify factors contributing to roadway work zone crashes/near crashes and develop a proof of concept to evaluate work zone risk for three to four roadway types. Data Request Scope This study will look at driver, environmental, roadway, and vehicle factors that contribute to work zone crashes and near crashes. The study will also evaluate how drivers negotiate work zones. Data Specification The dataset delivered for this AASHTO Implementation Assistance Program project consisted of a sample of detailed event tables (including trip summaries, vehicle, and driver survey results), forward video, and time series variables for 243 crash-related events and 420 baseline events from the SHRP2 NDS.
DOI: 10.18739/a23m22
title: Toolik Lake snowpack gases
DOI: 10.5066/f7rf5s5k
title: Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2015

description: This data release is the update of the data release Bera and Over (2016) with the processed data through September 30, 2015. The primary data for each year is downloaded from the ANL website (http://gonzalo.er.anl.gov/ANLMET/numeric/) and is processed following the guidelines documented in Over and others (2010). References Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/pdf/ofr2010-1220.pdf Bera, M., and Over, T. M., 2016, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2014: U.S. Geological Survey data release, ​http://dx.doi.org/10.5066/F7Q52MQG.
DOI: 10.14469/hpc/1851
title: Interactive Data table: Calculated properties for (S)-Streptorubin and (R,R)-Rosephilin
DOI: 10.7910/dvn/l5qwqr
title: Western Ghats SL Calculated Household Indicators

description: This dataset was moved to: https://doi.org/10.34725/DVN/L5QWQRCalculated household indicators for the Western Ghats Sentinel Landscape.
DOI: 10.14469/hpc/1852
title: Carboxylic first cycle, dimer

description: Gaussian Calculation
DOI: 10.14469/hpc/1853
title: Me-OPh, tert-butyl imine + mCPBA, oxziridine formation

description: Gaussian Calculation
DOI: 10.5066/f7pz56xz
title: Data and shapefiles for the sedimentation survey of Lago La Plata, Toa Alta, Puerto Rico

description: This data release contains spatial data associated the sedimentation survey conducted by the U.S. Geological Survey cfor the Lago La Plata, Toa Alta, Puerto Rico, during March and April 2015 to provide up-to-date information about the relation of pool elevation and storage volume in the reservoir. The survey was conducted in cooperation with the Puerto Rico Aqueduct and Sewer Authority. A total of 264 navigation lines were surveyed, using a depth sounding device coupled to a global positioning system. The results of the survey were used to prepare a bathymetric map showing the reservoir bottom referenced with respect to the spillway elevation.
DOI: 10.14469/hpc/1855
title: TI2 -987.126766 (+15.3) 10.14469/hpc/1885

description: Gaussian Calculation
DOI: 10.14469/hpc/1856
title: (1Z,4Z)-4,8-dibenzyl-N2,N2,N'2,N'2,N6,N6,N'6,N'6-octamethyl-2H,6H-1Î»3,3,5Î»3,7,2Î»4,6Î»4-tetraoxadiborocine-2,2,6,6-tetraamine

description: Gaussian Calculation
DOI: 10.14469/hpc/1857
title: Carboxylic first cycle, dimer + 3H2O

description: Gaussian Calculation
DOI: 10.15787/vtt1/alexmi
title: IAP Project: Pedestrian Safety and High Visibility Markings

description: Project Description This research study was sponsored by USDOT/AASHTO with money flowing through CUBRC. The study proposed to assess the safety benefits of various High Visibility Crosswalk (HVC) designs and implementations. Data Request Scope The dataset delivered consists of a sample of time series variables collected as part of the SHRP2 Naturalistic Driving Study. Time series data (de-identified) were requested from traversals of six HVCs in Buffalo, NY. 1,575 traversals of the intersection are included, representing 85 drivers. The HVCs were installed during the data collection period. For comparison, the sample of traversals include traversals of the HVC area before and after the installation of the HVC. Data Specification The times series data include: vehicle trajectory data (e.g., GPS coordinates, acceleration, and velocity), other vehicle performance data (e.g., brake pedal activation, steering wheel position), and video of the forward roadway. The time series data for each HVC traversal covers the portion of time the driver is on the two roadway links constituting each traversal. Time series data exclude data near the beginning and end of a trip (defined as a pre-determined distance from a trip origin or destination). The distance contains a limited random noise element to further anonymous the trip.
DOI: 10.15787/vtt1/cduju5
title: IAP Project: Concept to Countermeasure- Research to Deployment Using the 2nd Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) Data: Adverse Weather and Speeding

description: Project Description This research study was sponsored by Federal Highway Administration Implementation Assistance Program with funds flowing through the Wyoming Department of Transportation. This study proposed to enhance the understanding of how drivers respond to adverse weather and road conditions (e.g. speed adaptation, lane maintenance, car following, etc.). Phase 1 focused only on driver response in rain/ heavy rain in the States of Florida and Washington. Data Request Scope The dataset delivered consisted of a 150 Naturalistic Driving Study (NDS) trips from these two States. A subset of these trips (N=50) will occur during rain/ heavy rain on freeway segments with the remaining trips (N=100) occurring in clear weather conditions on the same segments and preferably for the same subjects. Time-series data and corresponding front/rear-view camera trip views (de-identified) were provide for over 150 events. In particular, any instrumented vehicles with the available wiper signal variables were used to determine the presence or absence of rain. Any instance where wiper activation occurs for over 10% of the segment will be considered a “rain” instance. Any instance where wiper activation is not present will be considered a “clear” instance. Data Specification The time series data included: vehicle trajectory data (e.g. latitude, longitude, speed, acceleration, and velocity), vehicle control data (e.g. wiper, light level, ABS, traction control, state, pedal gas position, pedal brake state, steering wheel position), vehicle lane position data (e.g. lane markers distances), and raw radar data.
DOI: 10.15787/vtt1/o5teix
title: Predictive Safety - Analysis, whether statistical data can be used for driving safety purposes

description: Data Request Scope This dataset contains time series and event data for all crashes, near-crashes, and balanced baseline events in the SHRP2 database. It also contains forward video for crash and near-crash events. Data Specification EVENT DATA: All available variables TIME SERIES DATA: timestamp file_ID speed_gps speed_network accel_x accel_y accel_z gyro_z pedal_brake_state pedal_gas_position steering_wheel_position turn_signal abs esc traction_control_state wiper headlight TARGET_ID X_POS_PROCESSED Y_POS_PROCESSED X_VEL_PROCESSED Y_VEL_PROCESSED X_ACC_ESTIMATED TARGET_TRAVEL_DIRECTION LANE IS_LEAD_VEHICLE HEADWAY
DOI: 10.15787/vtt1/ojskmf
title: FHWA EAR Project: Automated Feature Extraction Data Sets: Event Record and Seatbelt Usage Data

description: Project Description Oak Ridge National Laboratory (ORNL) is functioning as a technical advisor and independent system validator for the Federal Highway Administration for Broad Agency Agreement (BAA) call DTFH61-13-R-00011, “Exploratory Advanced Research Program”. In this document, we refer to BAA participants as “Performer Teams” (PT). One particular area of interest in the BAA where ORNL will assist is the Automated Feature Extraction (AFE) of naturalistic driving data, from available video streams and other sensor information. AFE refers to algorithms that process video data and automatically extract behavior characteristics of the driver and surroundings. There are several examples of features which could be extracted automatically, such as: the number of passengers in the vehicle with the driver; the traffic density of the surrounding vehicles; outside weather conditions; head pose; driver drowsiness; etc. Many of these features are derived from lower-level image analysis algorithms that detect motion, objects of interest, and other events in video. Due to the large size of the data set, automating the coding of the data through AFE can yield tremendous value to safety research and dramatically improve the utility of the SHRP-2 NDS data. The purpose of this analysis is not directly concerned with safety analysis or attempting to find solutions or study a particular safety or traffic problem. Instead, we are striving to develop automation methods, which, in turn, will simplify the tasks of finding data in the NDS to solve traffic problems. Data Specification Dataset/Dataset/Excel spreadsheet with event detail table from InSight (September 2014 release), MP4 forward video files for said events, CSV files with time series data for said events.
DOI: 10.15787/vtt1/okdefu
title: IAP Project: Concept to Countermeasure- Research to Deployment Using the 2nd Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) Data: Examination of Episodic Speeding on Washington State Road

description: Project Description This research study was sponsored by Federal Highway Administration Implementation Assistance Program. This study proposed to examine the effects of specific roadway locations and infrastructure features on episodic speeding (e.g. 10 MPH or more over the posted speed) by NDS participants in and around Seattle, WA. Data Request Scope The dataset delivered consisted of a sample of detailed event tables (including trip summaries, vehicle, and driver demographic survey results) and time series variables collected for multiple drivers as part of the SHRP2 Naturalistic Driving Study. Time-series data for over 9000 normal driving traversals through 10 Battelle-identified road locations (matched with Road Inofrmation Database Link IDs). These datasets are in a CSV (text) file format with timestamps reported on a rectangularized at between 1 and 10 Hz frequency clock and measures aligned to the closest timestamp. Also, each event traversal will have up to ±2 minutes time buffer around the specified road segments. Data Specification The time series data included: vehicle trajectory data (e.g. heading, latitude, longitude, speed, acceleration, and velocity), vehicle control data (e.g. wiper, light level, ABS, traction control, state, pedal gas position, pedal brake state, steering wheel position), and vehicle lane position data (e.g. lane markers distances)among other. Dataset/Dataset/CSV Files with Kinematics Data for 10 Locations, Excel File with Subsets of Trip Summaries, Vehicle and Driver Questionnaires
DOI: 10.15787/vtt1/rcxyjv
title: IAP Project: Driver Behavior and Performance in the Vicinity of Closely Spaced Interchange Ramps on Urban Freeways - Proof of Concept

description: Project Description This research study was sponsored by U.S.DOT/AASHTO with money flowing through Utah Department of Transportation. This study proposed to explore how driver behavior and performance was impacted while in the vicinity of closely spaced interchange ramps. More specifically, this study was focused on identifying differences in driver behaviors and performance in freeway merging and diverging areas near interchange ramps, and estimating the impacts of ramp spacing and auxiliary lane presence on driver behavior and performance. Additionally, this study investigated whether drivers adjust the their cell phone usage depending on driving demands, and evaluated the effects of cell phone use on driving behavior and driver crash risk near freeway merging and exit locations. Data Request Scope The study scope covered the scenario of an entrance ramp followed by a downstream exit ramp (EN-EX), with or without an auxiliary lane between entrance and exit ramps. Drivers of interest will include “merging” drivers (i.e., merging onto the freeway), “exiting” drivers (i.e., exiting the freeway), and through moving drivers (i.e., traveling on the freeway mainline prior to and after the ramp combinations). Data SpecificationThe dataset delivered consisted of a sample of timeseries variables collected for multiple drivers as part of the SHRP2 Naturalistic Driving Study. Time-series data (de-identified) was requested from 8 roadway segments - 4 located in Washington, and 4 located in North Carolina. 240 trips were delivered for each segment, where the trips were evenly divided (80 trips each) among "Entering" vehicles, "Exiting" vehicles, and "Through" vehicles. "Entering" vehicles enter the analysis area from the on-ramp and leave the analysis area on the highway, while "Exiting" vehicles enter the analysis area on the highway and leave the analysis area via the off-ramp. “Through" vehicles enter and leave the analysis area on the highway, not using the ramps. Time series data was delivered for a combined total of 1920 trips along all segments. The time series data included: vehicle trajectory data (e.g., GPS coordinates, acceleration, and velocity), vehicle lane position data (e.g., distance from lane markings, steering wheel position), driver head position data (head position, head angle), and radar range data for nearby vehicle positions. The time series data was needed for the portion of a driver’s trip over the study road segment, so the requested time series data started 10 seconds before entering the study road segment and ended 10 seconds after exiting the study road segment. Cell phone usage data included indicators noting whether a driver is talking on the cell phone (both with phone and handsfree phone), manually manipulating (dialing/texting) a cell phone, or if the driver was not distracted while traveling through the study location.
DOI: 10.5066/f7319t1p
title: Landsat 8 six spectral band data and MODIS NDVI data for assessing the optimal regression tree models

description: In this study, we developed a method that identifies an optimal sample data usage strategy and rule numbers that minimize over- and underfitting effects in regression tree mapping models. A LANDFIRE tile (r04c03, located mainly in northeastern Nevada), which is a composite of multiple Landsat 8 scenes for a target date, was selected for the study. To minimize any cloud and bad detection effects in the original Landsat 8 data, the compositing approach used cosine-similarity-combined pixels from multiple observations based on data quality and temporal proximity to a target date. Julian date 212, which yielded relatively low "no data and/or cloudy" pixels, was used as the target date with Landsat 8 observations from days 140-240 in 2013. The 30-m Landsat 8 composited data were then upscaled to 250 m using a spatial averaging method. Six Landsat 8 spectral bands (bands 1-6) at 250-m resolution were used as independent variables for developing the piecewise regression-tree models to predict the 250-m eMODIS NDVI (dependent variable). Furthermore, to ensure the high quality of the derived 250-m Landsat 8 data, and avoid any additional cloud and atmospheric effects, the percentage of 30-m pixels with "0" values within a 250-m pixel was calculated. Only those 250-m pixels with 0% of "0" values (i.e., all the 30-m pixels within a 250-m pixel have no zero values pixels) were selected to develop the regression-tree model.The 7-day maximum value composites of 250-m MODIS NDVI for the year 2013 were obtained from the USGS expedited MODIS (eMODIS) data archive (https://lta.cr.usgs.gov/emodis). Pixels with bad quality, negative values, clouds, snow cover, and low view angles were filtered out based on the MODIS quality assurance data to ensure high quality eMODIS NDVI data. The 2013 weekly NDVI data were then stacked and temporally smoothed using a weighted least-squares approach to reduce additional atmospheric noise. Temporal smoothing helps to ensure reliable NDVI values for weekly or longer cloudy periods. Finally, the 250-m MODIS NDVI for the date around Julian date 212 (similar date as the Landsat scene date) was extracted and used as the dependent variable in the regression tree model. ArcGIS, MS Excel, and Cubist are required to read and process the data.
DOI: 10.5066/f7445jn8
title: Water-quality data from four Indian Creek sites, Johnson County, Kansas, July 22-25, 2014 and August 21-27, 2015

description: This U.S. Geological Survey (USGS) Data Release provides continuously measured water-quality data collected from four Indian Creek sites in Johnson County, Kansas during July 22-25, 2014 and August 21-27, 2015. Water-quality monitors were used to measure water temperature, dissolved oxygen, pH, specific conductance, turbidity, chlorophyll, phycocyanin, and nitrate at fifteen-minute intervals. This data release was produced in compliance with the federal open-data requirements as a way to make scientific products associated with USGS research efforts and publications available to the public. This dataset includes all continuously-measured data collected at short-term fixed site locations as part of a study to describe the spatiotemporal variability of nutrients in Indian Creek.
DOI: 10.5066/f77w69cp
title: Spatial water-quality data for Indian Creek, Johnson County, Kansas, May 23, 2013, July 23, 2014, July 30, 2015, and August 26, 2015

description: This U.S. Geological Survey (USGS) Data Release provides spatial water-quality data collected from Indian Creek in Johnson County, Kansas, on May 23, 2013, July 23, 2014, July 30, 2015, and August 26, 2015. Continuous water-quality monitors were used to measure water temperature, specific conductance, pH, turbidity, dissolved oxygen, chlorophyll, phycocyanin, nitrate, and fluorescent dissolved organic matter (fDOM) at 30-second intervals along a 13.3-kilometer reach of Indian Creek. This data release was produced in compliance with the federal open-data requirements as a way to make scientific products associated with USGS research efforts and publications available to the public. This dataset includes all continuously (30-second intervals) measured spatial data collected as part of a study to describe the spatiotemporal variability of nutrients in Indian Creek.
DOI: 10.14469/hpc/1862
title: 2 methylboronic acid + 2 ethanoic acid

description: Gaussian Calculation