cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1466957
title: VABB-SHW (edition VIII)

description: This dataset contains the eighth edition of the <em>Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW)</em>, a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities. Publications in the database are used as one of the parameters of the Flemish performance-based research funding system. Only approved publications are included in this dataset. In addition to the actual data files (<em>*.csv</em>), the data set contains an extensive code book (<em>VABB code book.pdf</em>) and an R script with which the tables can be merged (<em>vabbshw_merge.R</em>; see the code book for details).
DOI: 10.25346/s6/wdrbke/gakqwp
title: YTCA_combined_EIS_uncertainty_Jan.csv
DOI: 10.25346/s6/wdrbke/iytszn
title: demo_variational_chirp.csv
DOI: 10.25346/s6/wdrbke/nmkrly
title: Hu_2020_J._Electrochem._Soc._167_027545.pdf
DOI: 10.25346/s6/wdrbke
title: Rapid Analytical Instrumentation for Electrochemical Impedance Spectroscopy Measurements

description: This work reports a low-cost custom electrochemical instrument capable of performing rapid and accurate electrochemical impedance spectroscopy (EIS) for supercapacitors over a broad frequency band (10 mHz to 2 kHz). Conventionally, EIS is measured via sinusoidal perturbations; however, such an approach suffers from lengthy measurement time. Chirp signals have been shown previously to reduce EIS measurement time for supercapacitors for relative narrow frequency bands (1 Hz to 2 kHz). However, to characterize supercapacitors comprehensively, much broader frequency bands are required. Here, we present a custom instrument with an adaptive measurement algorithm for performing EIS measurements in a wide frequency range of 10 mHz to 2 kHz with low measurement uncertainties. The results obtained using this new technique has been validated here with a commercial instrument on several types of supercapacitors. Furthermore, measurement time on average decreases from 1500 s to less than 400 s. The overall cost of the custom instrument is 90% lower as compared to the commercial instrument. The custom instrument’s accuracy, time efficiency and low cost are expected to benefit electrochemical researchers.
DOI: 10.15787/vtt1/al8aep
title: National Electric Vehicle Assessment

description: Project Description As this analysis is concerned with mainstream adoption of electric vehicles, an empirical foundation must be established for massmarket consumer travel patterns. While many publicly available datasets provide insight into aggregated personal travel patterns, very few longitudinal data samples of travel at the individual level exist. Longitudinal travel patterns are especially critical to analysis of electric vehicles as they enable long distance travel frequency to be quantified (a potentially problematic aspect to consumer adoption of electric vehicles given limited range, long recharge times, and lack of public charging inc). Data Request Scope To aid in this electric vehicle analysis, SHRP2 NDS data will be used to quantify long distance travel frequency across the available regions, driver types, and vehicle ages. In order to accomplish this objective, NREL is requesting copies of the trip summary (n=5,414,063), vehicle detail (n=3,370), and driver demographic tables (n=3,593) with fields discussed in the below "Data Specification" section. Data Specification NREL would be interested in the following fields from the trip, vehicle, and driver tables: TRIP o Trip ID o Anonymous Vehicle ID o Anonymous Driver ID o Trip Start UTC Hour of Day o Trip Start UTC Month o Trip End UTC Hour of Day o Trip Start Local Time Hour of Day o Trip Start Month Local o Trip End Local Time Hour of Day o Trip Day of Week o Trip Day Number in Study o Trip Duration o Trip Distance o Trip Centroid Latitude o Trip Centroid Longitude o Mean Speed o Trip Distance Origin to Destination o Vehicle Model Year o % HSIS Derived Rd Class o % Urb Frwy o % Urb Multi Div Non-Frwy o % Urb Multi Undiv Non-Frwy o % Rur Frwy o % Rur Multi Div Non-Frwy o % Rur Multi Undiv Non-Frwy o % Other Class o % State Data Cov o % No Spd Lim Data o % Spd Lim 35 or Less o % Spd Lim 40-50 o % Spd Lim 55-65 o % Spd Lim 70 or Greater VEHICLE: o Anonymous Vehicle ID o Vehicle Classification o Advanced Technology Vehicle o Model Year o Vehicle Make o Site Name o Powertrain DRIVER: o Anonymous Participant ID o Gender o Age Group o Ethnicity o Race o Birth Country o Education o Marital Status o Head-of-Household o Home Ownership o Work Status o Income o Number in Household o Vehicle 1 Classification o Vehicle 2 Classification o Vehicle 3 Classification o Vehicle 4 Classification o Vehicle 5 Classification o Driver Mileage Last Year o Business Use of Study Vehicle o Length of Vehicle Ownership
DOI: 10.15787/vtt1/aqd0bj
title: Examining Crashes and Near Crashes Collected in a Large-Scale Naturalistic Driving Study

description: Project Description The research team will access the existing SHRP2 data to further examine crashes and near-crashes. Specifically the researchers will conduct analyses to determine crash and near-crash rates by age and crash type and crash severity. Additional analyses will be conducted to determine how near-crashes are related to crashes and to understand the differences in the characteristics of near-crashes and those of crashes. Some of the questions the research team expects to answer are: Should near-crashes be studied for contributing and causal factors, as they are usually observed in larger quantities than crashes? What are some meaningful ways researchers in the future can assess the severity of a near-crash? Are participants with more near-crashes more likely to have more crashes? Data Request Scope All crashes, near-crashes, and baselines available on InSight v2.1.1. Data Specification The data set contains all time series data (excluding GPS), Driver Demographic Questionnaire data for all participants (since all SHRP2 participants are represented in the baselines events), and the Event Detail Table.
DOI: 10.5066/f74j0ddq
title: Land-Cover, Surface-water, and Streambed-sediment data Collected Upstream from and Within an Area of Oil and Natural-Gas Production, South Texas, 2008-17

description: The extensive development of oil and natural-gas resources in south Texas during the past 10 years has led to questions regarding possible environmental effects of processes associated with oil and natural-gas production, in particular the process of hydraulic fracturing, on water and other natural resources. Part of the lower San Antonio River watershed intersects an area of oil and natural-gas production from the sedimentary rocks that compose the Eagle Ford Group. The rapid expansion of infrastructure associated with oil and natural-gas production increases potential pathways for inorganic and organic contaminants to enter surface-water systems. The U.S. Geological Survey, in cooperation with the San Antonio River Authority, analyzed geospatial data from different years (2008 and 2015) to evaluate changes in land cover associated with oil and natural-gas production activities in the lower San Antonio River watershed. Additionally, during 2015-17 surface-water samples collected from 5 sites and streambed-sediment samples collected from 17 sites in the lower San Antonio River watershed were analyzed for a broad range of constituents that might be associated with oil and natural-gas production.
DOI: 10.15787/vtt1/c9xqio
title: Machine Learning Based Fault Determination in Near-crash/Crash Scenarios

description: Project Description The objective of this project was to develop a fault determination classifier which determines whether the subject vehicle or the surrounding vehicle is responsible for the near crash/crash event. Data Request Scope Data for all crashes and near-crashes found on InSight were requested. Data Specification Time-Series Speed (GPS and Network) Acceleration/Deceleration (x and y axis) Odometer Pitch Rate Roll Rate Steering Wheel Status Brake Pedal Status PRNDL (Gear) Details Turn Signal Cruise Control Lane Markings, Lane Width Alcohol Flag Temperature, Interior Details Illuminance, Ambient Details ABS Activation Details Event Detail Table Fault Crash Severity (1 and 2) Precipitating Event Hands on the Wheel Vehicle Contributing Factors Details Secondary Task Lighting Number of Objects Number of Motorists Incident Type Trip Summary Table Cell Phone Flag Time Wipers Used Lights Usage Percentage Number of Lateral Accels &gt; Threshold Distance with Lead Vehicle Time with Lead Vehicle Time Where Headway 1.0 - 1.5 s Time Where Headway 3.0 - 3.5 s Trip Start UTC Hour of Day Vehicle Details Model Year
DOI: 10.15787/vtt1/chtnvo
title: Rail-Highway Grade Crossings - Phase 3

description: Project Description The purpose of this effort was to support the use of various components of the SHRP 2 NDS database by personnel from Michigan Tech, who investigated, railroad crossings, driver reaction to the existing traffic control devices and impacts of the crossing environment, including weather, time of day, and driver familiarity with the crossing. Data Request Scope Michigan Tech received data for within an area extending from about 600 feet prior to a grade crossing, through the crossing, and 100-200 feet beyond the crossing. The dataset consisted of close to 15,000 crossings from nearly 300 locations of interest. Data Specification For each traversal the following time series data was delivered: - vtti.speed_gps - vtti.speed_network - vtti.accel_x - vtti.accel_y - vtti.pedal_gas_position - vtti.pedal_brake_status - vtti.accel_z - computed.day_of_week - vtti.engine_rpm - 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_head_rotation_x - vtti.head_rotation_x_baseline - vtti_head_rotation_y - vtti.head_rotation_y_baseline - vtti_head_rotation_z - vtti.head_rotation_z_baseline - vtti.heading_gps - vtti.latitude - vtti.longitude - vtti.gyro_x - vtti.frame_number - vtti.timestamp - computed.time_of_day - vtti.month - vtti.alcohol - vtti.video_frame - vtti.year Forward and rear video were also included as part of this dataset. Additional data included: - Participant ID - Gender - Age Group - Vehicle ID - Location - Trip ID - Trip Duration - Trip Distance - Cell Phone Flag
DOI: 10.15787/vtt1/clzb0s
title: BAA Speed-Safety Relationships

description: Project Description The overall study objective is to develop speed-safety relationships that can be used in the Highway Safety Manual and in other safety management tools such as Safety Analyst and usRAP models. The relationships will have functional forms consistent with previous research on speed effects and will have coefficient values derived from U.S. data. The speed-safety relationships will be developed with a combination of vehicle speed, roadway characteristics, and crash data for various roadway types using data from the six NDS study sites. Data Request Scope In Phase I of this project, 20 roadway segments of interest were identified based on data from the RID and trip density maps. The segments are 1- to 2-mi continuous segments that incorporate more than one adjacent linkID. This data set contains the data specified below for approximately 300 trips across the 20 identified segments. For Phase II of this project, an additional 100 roadway segments were identified, and approximately 500 traversals for each segment were provided. Data Specification This data set contains the following information for each individual trip that passed through each roadway segment of interest: TRIP: Trip number Vehicle ID Driver ID Vehicle type Driver age and gender Year, Month, and Day of Week Time of day TIME SERIES: Vehicle speed (mph) Vehicle location (latitude and longitude) Forward-radar data Continuous clock time
DOI: 10.15787/vtt1/e6bofi
title: Examination of the Influence of Expectancy for Driver's Recognition of In Road Obstacles

description: Project Description This dataset was generated to support the study which examine the point at which drivers began an emergency maneuver at night time using the given information from the SHRP 2 data. Data Request Scope The dataset exported to Crash Safety Research Center, LLC (CSRC) includes 153 front video files of 30 seconds for crash and near crash events from insight where Event Nature 1 where: Conflict with pedestrian Conflict with pedal cyclist Conflict with animal Conflict with obstacle in roadway Data Specification This dataset contains different types of data from the SHRP 2 dataset: Participants Events Vehicle Forward Video
DOI: 10.15787/vtt1/eesp1y
title: Sleepy

description: Project Description Recent research has found elevated crash risk when drivers experience some level of cognitive distraction compared to when there is no cognitive distraction. However, in many cases, drivers who are cognitively distracted also have their eyes off the forward roadway or are fatigued. Thus, it has not yet been established whether or not elevated risk of cognitive distraction remains after accounting for eyes off the forward roadway and fatigue. The data set from the Second Strategic Highway Research Program (SHRP 2) offers an opportunity to validate the degree to which cognitive distraction risk is tied to eye-glance behavior or fatigue. SHRP 2 recruited and studied more than 3,500 participants, aged 16-98, and collected 35 million miles of driving data over a three-year period. During the study, the participants experienced approximately 1,900 crashes. Thus, using SHRP 2 data, researchers can determine whether or not the elevated crash risk when drivers experience cognitive distraction remains when drivers are not fatigued and still have their eyes on the forward roadway. Data Request Scope The data requested consists of the certain behavioral and environmental factors for each crash and baseline. This will allow us to compare different driver behaviors and environmental factors in terms of whether or not the increase or decrease the risk of crash. Data Specification For all crashes and baselines, the following variables are included in this dataset: Participant ID Event Severity 1 Event Severity 2 Pre-Incident Maneuver Maneuver Judgement Precipitating Event Event Nature 1 Incident Type 1 Crash Severity 1 Event Nature 2 Incident Type 2 Crash Severity 2 Driving Behavior 1 Driving Behavior 2 Driving Behavior 3 Impairments Front Seat Passengers Rear Seat Passengers Sec Task 1 Sec Task 1 Outcome Sec Task 2 Sec Task 2 Outcome Sec Task 3 Sec Task 3 Outcome Hands on Wheel Driver Seatbelt Vehicle Factors Infrastructure Vis Obstructions Light Weather Surface Condition Traffic Flow Traffic Density Traffic Control Relation to Junction Intersection Influence Locality Fault Weather Lighting --
DOI: 10.15787/vtt1/hj4ovz
title: The effects of driver perception reaction times on crash sequence

description: Project Description Crash causation is complicated, as it not only involves driver, vehicle and environment attributes, but also their interactions. In particular, human error is often cited as one of the major contributers (Treat et al., 1977), in which driver information processing and perception reaction time are critical to study these error (Fambro et al., 1998). Although this topic has been studied extensively in the past, limited studies have researched into their effects on crash occurrence in real crash events. As such, this study proposed to utilize SHRP2 NDS data to discuss the following issues: (1) Identify the effects of environmental / driver-related factors on perception reaction times (2) Identify the effects of perception reaction time on cash sequence (3) Identify the effects of perception reaction times and crash sequence on crash occurrence Data Request Scope Rear-end crashes and near-crashes identified as "Rear-end, striking" available on InSight v2.1.1. Data Specification (1) Event Detail Table - all variables (2) Time Series Data - all variables available on the InSight viewer during the period starting 20 sec before the precipitating event and ending 10 sec after the precipitating event (or portion available thereof, in cases where the full period is not available). (3) Forward Video - as available on the InSight website
DOI: 10.15787/vtt1/imc8yv
title: SHRP 2 Safety Implementation: Driver Behavior in the Vicinity of Closely Spaced Interchange Ramps on Urban Freeways - Phase 2

description: Project Description State departments of transportation (DOTs) frequently evaluate requests for new access or modifications to existing access on freeways. Transportation agency concerns regarding new or modified freeway access include ramp spacing on the mainline and its impact to freeway operations and safety. Very little information existed on the safety impacts of ramp spacing until recently, when the University of Utah created both crash modification factors (CMFs) and severity distribution functions (SDFs) for entrance-exit (ENEX) ramp spacing and auxiliary lane presence (Le Porter, 2012; Shea et al., 2015). While these CMFs can be used by state DOTs to quantify the freeway mainline safety effects of ramp spacing and auxiliary lane presence, they are purely empirical findings that represent statistical associations. Underlying, causative reasons for the crash patterns can be inferred, but are not fully understood. A more thorough understanding of the “cause-effect” relationships leading to observed crash patterns is needed so that countermeasures can be identified and installed at current and future locations with closely spaced interchange ramps. This data set was intended to analyze 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 included in the dataset encompass “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 Request Scope This data set encompasses 40-60 freeway roadway segments located in Washington and North Carolina, with approximately 300 trip epochs included for each segment. The trip epochs are evenly divided between "Entering" vehicles, "Exiting" vehicles, and "Through" vehicles scenarios. Traversals in the data set range from 10 seconds before the initial “buffer” cross (or Link ID entrance) until 10 seconds after the final “buffer” cross (or Link ID exit). Data Specification The data set includes a sample of de-identified time-series variables as well as indicators noting whether a driver was talking on a cell phone (both with hand-held phone and hands-free phone), manually manipulating (dialing/texting) a cell phone, or if the driver was not distracted while traveling through the study location. Forward and rearward video views are also included. Time series data files are in a CSV (text) file format, with timestamps reported on a 10 Hz frequency clock and measures aligned to the closest timestamp. Video files are H.264 encoded with a nominal collection rate of 15 frames per second.
DOI: 10.15787/vtt1/kkkykk
title: Utilization of Adverse Weather Data to Evaluate Crashes

description: Project Description This study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. Data Request Scope Data requested included selected crash/near-crash events before adverse weather occurs, specifically at high speeds. In the beginning, 5 relevant events from two states were requested to determine if the datasets were useful to the driver behavior analysis. Subsequent sets of data were requested based on the analysis of the previously requested events. Data Specification Forward Video (clipped) Speed (GPS speed and CAN speed where available)
DOI: 10.15787/vtt1/klj2f1
title: Development of a Structural Equations Model to Understand Factors Influencing the Driver State

description: Project Description Safety research has largely focused on developing crash prediction models to directly relate traffic, roadway, and environmental factors to crash frequency and severity, but could not account for unobserved variables (i.e., engagement in risky driver behaviors) that contribute to a crash. The factors that contribute to engagement in risky driving behaviors are not well understood. This study uses the unique information in the NDS data to model driver state and behaviors as a function of different explanatory factors. Therefore, it is envisioned that the study would support the design of preventive measures that could proactively minimize risky driving state/behaviors to begin with. Data Request Scope This request includes elements of the vehicle, drivers, trips, and events categories found in the "Data" section of the InSight website. The tables included were not filtered in any way. Data Specification Vehicle: Vehicle Details Vehicle Condition Driver: Demographics Driver History Driver Knowledge Risk Perception Risk Taking Sensation Seeking Driver Behavior Sleeping Habits Trips: Trip Summary Events: Event Details Post-crash Interview A full list of requested variables can be found below in the file DataDictionary.pdf.
DOI: 10.15787/vtt1/l8im9r
title: IAP Phase II Evaluation of the interaction between horizontal and vertical alignment on rural two lane roads

description: Project Description The overall goal is to examine driver performance under different combinations of horizontal and vertical alignment and determine countermeasures that may be effective in reducing crashes at these locations. The primary aim of Phase 1 was to determine whether useful data and results can be produced by analyzing the data from SHRP2 naturalistic driving study (NDS), and if further work is likely to provide insights into crash causality or countermeasures effectiveness. Phase 2 is intended to examine driver performance associated with different countermeasures. Phase 1 was completed in September 2015. Phase 2 commenced in August 2016. The focus is on rural two lane roads. Data Request Scope The intent is to obtain kinematics and eye glance data from different types of segments with and without vertical/horizontal curves. The first step in this process is identify the segments from the RID that will be submitted to VTTI to obtain the data. In order to do this, an algorithm is being developed to identify vertical curves (an initial version of this algorithm was developed in Phase 1, and is being refined in Phase 2). While identifying the locations of interest, UNC will also examine the number of NDS trips that were made at the different locations. In Phase 1, we used the maps in the insight website. In Phase 2, we have been informed that the GIS layers of these maps is available. The identified locations will be provided to VTTI. Data Specification Events will range 10 seconds before the initial Link_ID entrance until 10 seconds after the final Link_ID exit Driver demographic data The following time-series variables: DAS timestamp File ID lane_distance_off_center left_line_right_distance right_line_left_distance left_marker_probability right_marker_probability lane_width speed_network speed_gps heading_gps gyro_z steering_wheel_position pedal_brake_state pedal_gas_position accel_x accel_y latitude longitude is_lead_vehicle (identify the RADAR target that represents the lead vehicle) For the lead vehicle, as identified by the RADAR: object_id (processed) range_x (processed) range_y (processed) range_rate_x (processed) range_rate_y (processed) headway (processed) Eye glance data for 3,000 events Shape files that support the heat map visualization on the InSight website