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19 results for “Driver Behavior”
Dataset for "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators"
<p>This dataset includes all supporting data and scritps to generate figure panels in the paper "Large scale patterns and drivers of the diving behavior of gill-breathing large pelagic predators" (A. Nuno, J. Guiet, B. Baranek and D. Bianchi)</p>
Dataset for "Large scale patterns and drivers of the diving behavior of large pelagic predators"
<p>This dataset comprises 694 independent diving depth estimates of large pelagic predators extracted from 101 tagging studies. For both daytime and nighttime observations, two unambiguous quantities were reported: 1) the preferred diving depth (D<sub>pref</sub>), representative of the approximate depth at which individuals spend most of the time, or in other words a mean representative depth; and (2) the preferred diving depth range (ΔD<sub>pref</sub>), representative of the portion of the water column where individuals are most commonly recorded by the tags, or, in other words, the vertical range over which they are most commonly observed. Alongside the diving depth data, three additional types of information were extracted to co-locate diving depth observations with environmental variables, and to account for potential ontogenetic behavioral effects. These additional pieces of information include the location and period of the observations, and a representative size of the tagged individual or group of individuals. The extraction process was replicated by two separate analysts to ensure accuracy and reliability. For further details on the dataset and extraction procedure, refer to the manuscript "<em>Large scale patterns and drivers of the diving behavior of large pelagic predators"</em>.</p>
Identifying behavioral and attitudinal barriers and drivers to promote consumption of pulses: A quantitative survey across five European countries
<p>Even though pulses are nutritious and environmentally friendly high-protein crops, they are commonly regarded as old-fashioned. Consumption of pulses is low in developed countries, and it has received very limited attention in agricultural and behavioral research over the past years. Based on a pan-European survey, the present study aims to provide extensive insights into consumer perceptions towards pulses. The objective is to identify effective measures to increase consumer acceptance of pulses, and thus potentially increase their share in the daily diets across Europe. Quantitative data was collected in May 2020 through a web-based survey conducted in five different European countries, Germany, Denmark, Spain, Poland, and the United Kingdom (N = 4,916). Quota-based sampling was used to ensure comparability across samples and wide coverage in terms of age, gender, and locality of residence. Using cluster analysis with a subsequent logistic regression, cross-cultural differences were found in terms of relatively high consumption of pulses among Spanish respondents, somewhat lower for Polish, German and UK respondents, and even lower among Danish respondents. Drivers of consumption significantly differed across surveyed samples, mainly being health first, followed by sensory preferences. For respondents from Poland, Spain, and Germany the main reason for not consuming pulses related to problems with digestion, whereas in the Danish and UK samples a neglect related to preparation. Future product development as well as increased information about nutrition and cooking of pulses could potentially increase consumer acceptance. Though, considering cross-cultural differences is important when developing strategies to promote consumption of pulses.</p>
Data and code for Host and pathogen drivers of infection-induced changes in social aggregation behavior
<p>Raw data and R code </p> <p>DistanceInds.xlsx contains pairwise distances between pairs of flies, measured within groups of 12 every 30 mins for 4 hours post-infection with one of four bacterial pathogens, at either a low or high dose. </p> <p>NND-Boyle.csv contains nearest-neighbor distances between pairs of flies, measured within groups of 12 following infections with Pseudomonas entomophila. </p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Examining drivers' behaviors to connected and automated vehicles
<p>It is envisioned that Connected and Automated Vehicles (CAVs) are the future of transportation as they can assist in minimizing some inefficiencies with the current transport systems. However, it is not clear how drivers of conventional vehicles would interact with CAVs in a mixed traffic environment containing both CAVs and human driven vehicles (HDVs). Thus, this study aims to investigate drivers’ behaviors towards CAVs through driving simulation experiment and national survey study. Two on-ramp and two off-ramp driving simulation scenarios were designed where drivers were asked to merge with two-lane highway in presence of HDVs and CAVs truck platoon in the on-ramp scenarios. In the two off-ramp scenarios, they were asked to take exit in presence of HDVs and CAVs truck platoon in the lane to their right. A before-after survey was conducted among the participant of the driving simulation experiment and an online survey was conducted to investigate their opinion in different traffic, road and environmental condition in presence of CAVs. Furthermore, two driving simulation scenarios were designed to test drivers’ behaviors during automated driving mode and their reaction when the control was shifted to the manual driving mode. Results from the on-ramp scenarios and off-ramp scenarios indicate that more than half of the drivers preferred to merge in front of CAV truck platoon and around two-third of the drivers chose to diverge behind the platoon. The online survey revealed that around two-third of the respondents would not overtake CAV platoon in two-lane two-way road, whereas around 60% would do so in case of three lane highway. During automation failure, drivers demonstrated lower take-over reaction time (TORt), lower deceleration and higher TTC in scenarios with non-driving related tasks (NDRT) compared to the scenarios with no NDRT (manual mode). The before-after survey results suggest that most of the drivers found the navigation with CAV easier after participating in the experiment.</p>
Historical behavioral data disentangle evolutionary and environmental drivers of recent declines in insect attraction to light
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When the mean is meaningless: Drivers of spatial behavior in a generalist carnivore
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Academia as a Driver of Change: A Bibliometric Analysis of Pro-Environmental Behavior in Higher Education Institutions
<p>Web of Science dataset</p>
Data - Leech removal is not the primary driver of basking behavior in a freshwater turtle
<p>Leaving the water to bask (usually in the sun) is a common behavior for many freshwater turtles, with some species also engaging in "nocturnal basking." Ectoparasite removal is an obvious hypothesis to explain nocturnal basking and has also been proposed as a key driver of diurnal basking. However, the efficacy of basking, day or night, to remove leeches has not been experimentally tested. Therefore, we examined the number of leeches that were removed from Krefft's river turtles (<i>Emydura macquarii krefftii</i>) after experimentally making turtles bask at a range of times of day, durations, and temperatures. Turtles had high initial leech loads, with a mean of 32.1 leeches per turtle. Diurnal basking under a heat lamp for 3 h at ~<span>28°C significantly reduced numbers of leeches relative to controls. In diurnal trials, 90.9% of turtles lost leeches (mean loss of 7.1 leeches per turtle), whereas basking for 30 mins under the same conditions was not effective (no turtles lost leeches, and all turtles were still visibly wet). Similarly, "nocturnal basking" at ~23°C for 3 h was not effective at removing leeches. Only 18% of turtles lost leeches (one turtle lost one leech and another lost four leeches). Diurnal basking outdoors under direct sunlight for 20 min (</span>mean temp = 34.5°C) resulted in a small reduction in leeches, with 50% of turtles losing leeches and an average loss of 0.7 leeches per turtle. These results indicate basking can remove leeches if temperatures are high or basking durations are long. However, it was only effective at unusually long basking durations in this system. Our data showed even the 20-min period was longer than 70.1% of natural diurnal basking events, many of which took place at cooler temperatures. Therefore, leech removal does not appear to be the purpose of the majority of basking events.</p>
Dataset Questionnaire E-Commerce Uncovered Exploring Key Drivers of Consumer Impulse Buying Behavior
<p>The following dataset is a dataset from a study that investigated Perceived Ease of Use and Perceived Usefulness on Impulsive buying throught Attitude Towards E-commerce.</p>
A dataset on the physiological state and behavior of drivers in conditionally automated driving
<p>A detailed description of the data collected, the experimental design, materials and methods used in each experiment, and the references associated with this work can be found in the manuscript published in the journal Data in Brief, available in Open Access <a href="https://doi.org/10.1016/j.dib.2023.109027">here</a><strong>.</strong> <strong>Please cite this publication if you use the dataset.</strong></p> <p><strong>Reference : </strong>Quentin Meteier, Marine Capallera, Emmanuel de Salis, Leonardo Angelini, Stefano Carrino, Marino Widmer, Omar Abou Khaled, Elena Mugellini, Andreas Sonderegger. A dataset on the physiological state and behavior of drivers in conditionally automated driving, Data in Brief, 2023, 109027, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2023.109027">https://doi.org/10.1016/j.dib.2023.109027</a>.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This is a set of physiological and behavioural data collected in 6 fixed-base driving simulator experiments from 346 drivers. The data was collected as part of the AdVitam project (for Adaptive Driver-Vehicle Interaction to Make future driving safer), a joint research project funded by the Hasler Foundation, led by the Human Tech Institute (HEIA-FR), the He-Arc, the EPFL+ECAL Lab, and the University of Fribourg (Switzerland).</p> <p>All experiments simulated conditional automation (Level 3 of automation according to the taxonomy released by the <a href="https://www.sae.org/standards/content/j3016_202104/">Society of Automotive Engineers</a>), except for experiment 1 (Level 0 - manual driving).</p> <p>Each folder contains raw and preprocessed data collected in each experiment:</p> <ul> <li>Exp1: Experimental manipulation of relaxation before driving and presence of passenger while driving (manual driving)</li> <li>Exp2: Experimental manipulation of cognitive workload at 2 levels using a verbal task (backwards counting)</li> <li>Exp3: Experimental manipulation of cognitive workload at 3 levels using visual and auditory tasks (N-back task)</li> <li>Exp4: Experimental manipulation of fatigue (sleep deprivation) and driving environment (rural vs. urban scenario)</li> <li>ExpTOR: Multiple takeovers requested through different modalities (visual, auditory, haptic), while performing different non-driving related tasks</li> <li>ExpFinal: Testing a contextual multimodal system for maintaining situation awareness and takeover quality in conditionally automated driving</li> </ul> <p>Physiological data (Electrocardiogram, electrodermal activity and respiration) were collected in all experiments. They were preprocessed in Python with the <a href="https://github.com/neuropsychology/NeuroKit">Neurokit</a> library. It was also used to process the physiological raw signals and calculate a large range of physiological indicators.</p> <p>Some of these measures were also collected in the various experiments: situation awareness, takeover performance (reaction, time, maximum steering wheel angle, ..), affective state, mental workload, non-driving-related task performance, sleepiness..</p> <p>More details on each experiment are provided in the ExpX_README.md of each folder.</p>
Data from: Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model
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Data - Leech removal is not the primary driver of basking behavior in a freshwater turtle
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Data from: Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care
<p>Inadequate quality of care in healthcare facilities is one of the primary causes of patient mortality in low- and middle-income countries, and understanding the behavior of healthcare providers is key to addressing it. Much of the existing research concentrates on improving resource-focused issues, such as staffing or training, but these interventions do not fully close the gaps in quality of care. By contrast, there is a lack of knowledge regarding the full contextual and internal drivers–such as social norms, beliefs, and emotions–that influence the clinical behaviors of healthcare providers. We aimed to provide two conceptual frameworks to identify such drivers, and investigate them in a facility setting where inadequate quality of care is pronounced. Using immersion interviews and a novel decision-making game incorporating concepts from behavioral science, we systematically and qualitatively identified an extensive set of contextual and internal behavioral drivers in staff nurses working in reproductive, maternal, newborn, and child health (RMNCH) in government public health facilities in Uttar Pradesh, India. We found that the nurses operate in an environment of stress, blame, and lack of control, which appears to influence their perception of their role as often significantly different from the RMNCH program's perspective. That context influences their perceptions of risk for themselves and for their patients, as well as self-efficacy beliefs, which could lead to avoidance of responsibility, or incorrect care. A limitation of the study is its use of only qualitative methods, which provide depth, rather than prevalence estimates of findings. This exploratory study identified previously under-researched contextual and internal drivers influencing the care-related behavior of staff nurses in public facilities in Uttar Pradesh. We recommend four types of interventions to close the gap between actual and target behaviors: structural improvements, systemic changes, community-level shifts, and interventions within healthcare facilities.</p>
ED Intervention to Reduce Risky Behaviors in Drivers
ClinicalTrials.gov study NCT00164294. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Categorizing and assessing comprehensive drivers of provider behavior for optimizing quality of health care
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Domestic dog lineages reveal genetic drivers of behavioral diversification
GEO Series GSE213053. Canis lupus dingo; Canis lupus; Canis lupus familiaris. 956 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
Social-environmental, Psychosocial, Behavioral, Clinical and Biological Drivers of Disparities in Liver Disease Progression Among Korean American With Chronic Hepatitis B Infection
ClinicalTrials.gov study NCT05117541. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.