cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6468842
title: Safety of Vulnerable Road Users (VRU's) in Light-Rail Transit (LRT) Environment

description: Light-rail transit (LRT), which includes modern streetcars, trolleys, and heritage trolleys, is one of the fastest growing modes of public transportation in the United States. To reduce the cost and complexity of construction, most LRT systems have their tracks placed on city streets, in medians, or in separate at-grade rights-of-way with at-grade crossings. Operating light-rail vehicles (LRVs) along these alignments introduces new conflicts and increases the risk of collisions with vulnerable road users (VRUs) including pedestrians, bicyclists, and electric scooter riders.<br> This study has two main objectives: (1) to review and evaluate the existing body of knowledge and the state of practice regarding safety of VRUs in LRT environments; and (2) to synthesize this information and package the results in a “Best Practices Resource Guide” and a companion “PowerPoint Presentation” for use in improving the safety of VRUs in existing LRT systems and advancing the professional capacity of transit workforce. Metropolitan Planning Organizations and State DOTs should also benefit from this resource information in the planning and design of new LRT systems.<br> This report presents a wide range of physical, educational, and enforcement treatments for improving the safety of VRUs in LRT environments. The selection of a particular treatment for use at an LRT grade crossing or station should be based on an engineering study whose scope and complexity depend on local conditions. Factors that should be considered during device selection include 1) pedestrian‐LRV collision experience, 2) pedestrian volumes and peak flow rates, 3) train speeds, frequency of trains, number of tracks, and railroad traffic patterns, 4) sight distances available to pedestrians and LRV operators approaching the crossing, and 5) skew angle, if any, of the crossing relative to the LRT tracks.
description: Tran-SET Project: 20SAOSU06
DOI: 10.5281/zenodo.6468841
title: Safety of Vulnerable Road Users (VRU's) in Light-Rail Transit (LRT) Environment

description: Light-rail transit (LRT), which includes modern streetcars, trolleys, and heritage trolleys, is one of the fastest growing modes of public transportation in the United States. To reduce the cost and complexity of construction, most LRT systems have their tracks placed on city streets, in medians, or in separate at-grade rights-of-way with at-grade crossings. Operating light-rail vehicles (LRVs) along these alignments introduces new conflicts and increases the risk of collisions with vulnerable road users (VRUs) including pedestrians, bicyclists, and electric scooter riders.<br> This study has two main objectives: (1) to review and evaluate the existing body of knowledge and the state of practice regarding safety of VRUs in LRT environments; and (2) to synthesize this information and package the results in a “Best Practices Resource Guide” and a companion “PowerPoint Presentation” for use in improving the safety of VRUs in existing LRT systems and advancing the professional capacity of transit workforce. Metropolitan Planning Organizations and State DOTs should also benefit from this resource information in the planning and design of new LRT systems.<br> This report presents a wide range of physical, educational, and enforcement treatments for improving the safety of VRUs in LRT environments. The selection of a particular treatment for use at an LRT grade crossing or station should be based on an engineering study whose scope and complexity depend on local conditions. Factors that should be considered during device selection include 1) pedestrian‐LRV collision experience, 2) pedestrian volumes and peak flow rates, 3) train speeds, frequency of trains, number of tracks, and railroad traffic patterns, 4) sight distances available to pedestrians and LRV operators approaching the crossing, and 5) skew angle, if any, of the crossing relative to the LRT tracks.
description: Tran-SET Project: 20SAOSU06
DOI: 10.5519/qd.80it41xh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.6468853
title: Preventing Struck-by Hazards: Defying Risk-habituation via Virtual Accident Simulation

description: Repeated exposure to struck-by hazards in road work zones generates workers’ habituation to risks related to those hazards, a key contributor to accidents in road work zones. Thus, analyzing the development of risk habituation and providing proper intervention are crucial to preventing accidents in road work zones. In this context, this study employs a virtual reality (VR) environment as a behavioral intervention tool to investigate its effect on mitigating a decline in workers’ vigilance with habituation to hazards in workplaces. A virtual environment that simulates a road maintenance task was developed and used to repeatedly expose subjects to struck-by hazards in road construction sites. A VR accident was simulated in response to the emergence of habituation to hazards within the virtual environment. The intervention effect was investigated to analyze the frequency and threshold of subjects’ vigilant behaviors. The results indicated that the developed VR environment evoked a decline in subjects’ attentiveness as a result of risk habituation within a relatively short period of experiment time, and the simulated VR accidents generated a sustained effect in reducing risk habituation. The findings of this study provide the understanding of how workers’ risk habituation can be observed using VR and how a behavioral intervention in a VR environment can reduce risk habituation to repeatedly exposed workplace hazards.
description: Tran-SET Project: 20SATAMU20
DOI: 10.5281/zenodo.6468852
title: Preventing Struck-by Hazards: Defying Risk-habituation via Virtual Accident Simulation

description: Repeated exposure to struck-by hazards in road work zones generates workers’ habituation to risks related to those hazards, a key contributor to accidents in road work zones. Thus, analyzing the development of risk habituation and providing proper intervention are crucial to preventing accidents in road work zones. In this context, this study employs a virtual reality (VR) environment as a behavioral intervention tool to investigate its effect on mitigating a decline in workers’ vigilance with habituation to hazards in workplaces. A virtual environment that simulates a road maintenance task was developed and used to repeatedly expose subjects to struck-by hazards in road construction sites. A VR accident was simulated in response to the emergence of habituation to hazards within the virtual environment. The intervention effect was investigated to analyze the frequency and threshold of subjects’ vigilant behaviors. The results indicated that the developed VR environment evoked a decline in subjects’ attentiveness as a result of risk habituation within a relatively short period of experiment time, and the simulated VR accidents generated a sustained effect in reducing risk habituation. The findings of this study provide the understanding of how workers’ risk habituation can be observed using VR and how a behavioral intervention in a VR environment can reduce risk habituation to repeatedly exposed workplace hazards.
description: Tran-SET Project: 20SATAMU20
DOI: 10.5281/zenodo.6468865
title: Data underlying Figure 1

description: Accompanying Scientific Article "Diabatic Upwelling in the Tropical Pacific: Seasonal and subseasonal variability"
DOI: 10.5281/zenodo.6468864
title: Data underlying Figure 1

description: Accompanying Scientific Article "Diabatic Upwelling in the Tropical Pacific: Seasonal and subseasonal variability"
DOI: 10.7910/dvn/s3tsui
title: Mediating role of internalised oppression in the relationship between psychological factors and intimate partner violence among couples in mixed-orientation marriages

description: Mediating role of internalised oppression in the relationship between psychological factors and intimate partner violence among couples in mixed-orientation marriages
DOI: 10.25345/c5rr1pr1x
title: MassIVE MSV000089273 - The Mouse Eosinophil Proteome determined by Mass Spectrometry
title: Mass spectrometry raw files for mouse eosinophil proteome investigation.
DOI: 10.3886/e151621v1-115300
title: National Health and Nutrition Examination Survey (NHANES) for Dementia Researchers, 2011-2014

description: This repository represents a series of code and corresponding data files intended for use in cognitive decline and Alzheimer’s disease and related dementias (ADRD) research. The files include three datasets derived from the 2011-12 and 2013-14 National Health and Nutrition Examination Survey (NHANES) panels (survey years 2011-12, 2013-14, and 2-year appended data file). NHANES is a nationally representative program of studies that, among many other things, allows researchers to assess the health of adults in the United States. <br><br>The provided datasets include sociodemographic information on respondents’ age, sex, race, and education levels merged onto individual and summary cognition variables generated from 1) the word learning and recall modules from the Consortium to Establish a Registry for Alzheimer's Disease neuropathology protocol (CERAD), 2) the Animal Fluency test, and 3) the Digit Symbol Substitution test (DSST). Cognition variables were then standardized on relevant sociodemographic characteristics (education, race, and age). Brief and detailed summaries of the variables available in these datasets along with more detailed descriptions of performed calculations can be found in the provided data dictionaries. SAS, Stata, and CSV file formats are provided as are the full coding scripts used in both SAS and Stata.<br>
description: Users of this resource should be familiar with complex survey design and appropriately apply the sample weights, strata, and cluster/primary sampling unit for any analyses. While both the exam and interview weights are provided, MEC exam weight (wtmec2yr) should be used for all analyses including cognition data. Interview weight (wtint2yr) has been included for those who wish to add interview datasets to the provided data. Strata should be set to the Masked variable pseudo-stratum (sdmvstra) and cluster/PSU should be set to the Masked variance pseudo-PSU (sdmvpsu).
description: Response Rates: See NHANES website for more information.
description: Presence of Common Scales: 1) the word learning and recall modules from the Consortium to Establish a Registry for Alzheimer's Disease neuropathology protocol (CERAD), 2) the Animal Fluency test, and 3) the Digit Symbol Substitution test (DSST)
description: Complex survey data with a stratified, clustered sampling design. Oversampling of Hispanic, Black, and Asian persons.
description: Nationally representative sample of noninstitutionalized U.S. civilians of all ages. Cognition data only available for older U.S. adults (age 60 and above). Smallest Geographic Unit: U.S.
description: cognitive assessment test; face-to-face interview; mixed mode; on-site questionnaire; Only those age 60 and above were asked the cognition questions (e.g., CERAD). While both the MEC exam and interview weights are provided, <b>the</b> <b>MEC exam weight (</b><b>wtmec2yr</b><b>) should be used for all analyses including cognition data</b>. Interview weight provided for those who wish to add interview datasets to the provided data.
DOI: 10.5281/zenodo.6468893
title: Urban Transportation Infrastructure and Cyclist and Pedestrian Safety

description: The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.
description: Tran-SET Project: 20SAUTSA35
DOI: 10.5281/zenodo.6468892
title: Urban Transportation Infrastructure and Cyclist and Pedestrian Safety

description: The goal of this project was to perform a comprehensive evaluation of crash causes and risk factors to identify the root causes of crashes involving bicyclists and pedestrians in San Antonio, TX. The research included the development of a database of bicycle and pedestrian crash reports in the target area, calculation of crash counts and rates, identifying road segments and intersections with highly concentrated bicycle and pedestrian crashes, and the development of effective safety countermeasures. Several variables and factors were analyzed, including driver characteristics such as age and gender, road-related factors, and environmental factors such as weather conditions and time of the day. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian/bicyclist crashes. Geospatial analysis was used to investigate crash frequency and severity. High-risk locations were identified through heat maps and hotspot analysis. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. The strongest predictors of severe injury include lighting condition, road class, road speed limit, traffic control, collision type, and the age and gender of the pedestrian/bicyclist. Fatal and incapacitating injury risk increased substantially when the pedestrian/bicyclist was at fault. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, and raised medians, and the use of leading pedestrian/bicyclist interval and hybrid beacons are recommended.
description: Tran-SET Project: 20SAUTSA35
DOI: 10.25919/3sqj-4130
title: Parkes observations for project P885 semester 2022APRS_01

description: We request a continued reduced monitoring cadence for the magnetars 1E 1547.0-5408 and Swift J1818.0-1607 at once every 10 days, to track their rotation, flux density, and polarisation. Swift J1818.0-1607 remains very dynamic, and the unique UWL capabilities promise new opportunities to probe still mysterious radio emission mechanisms in magnetars. Our timing results indicate that 1E 1547.0-5408 is currently a more stable rotator, supporting the diminished cadence. We will observe XTE J1810-197 and PSR J1622-4950 less frequently, to complement monitoring elsewhere. The overall request is 18 hours.
DOI: 10.5281/zenodo.6468914
title: Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images

description: Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.
description: Tran-SET Project: 20STLSU12
DOI: 10.5281/zenodo.6468913
title: Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images

description: Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.
description: Tran-SET Project: 20STLSU12
DOI: 10.5281/zenodo.6468930
title: Bridge Cracks Monitoring: Detection, Measurement, and Comparison using Augmented Reality

description: Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.
description: Tran-SET Project: 20STUNM33
DOI: 10.5281/zenodo.6468929
title: Bridge Cracks Monitoring: Detection, Measurement, and Comparison using Augmented Reality

description: Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.
description: Tran-SET Project: 20STUNM33
DOI: 10.6084/m9.figshare.19611783
title: Untitled Item

description: mRNA expression level of inflammatory cytokines
DOI: 10.6084/m9.figshare.19611783.v1
title: Untitled Item

description: mRNA expression level of inflammatory cytokines