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374
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ShareScore release 0.9.0
Dataset results
374 results for “traffic”
Behavioral Economics to Implement a Traffic Light Nutrition Ranking System: Study 2
ClinicalTrials.gov study NCT06298253. IPD Sharing: YES. Countries: 1. Publications: 1.
Visual obstruction, but not moderate traffic noise, increases reliance on heterospecific alarm calls
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Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
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Data from: Climate change and pathways of vessel traffic create marine protected area networks of invasion
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Vessel traffic observations and associated waterbird surveys, March 2020 – February 2021, Roberts Point and Ogden Point, Victoria, BC, Canada
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IoT network traffic dataset using the custom flow representation
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Recreational trail traffic counts and trail proximity as a driver of ungulate landscape utilization
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Habitat type drives the distribution of non-indigenous species in fouling communities regardless of associated maritime traffic
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Estimated roadway segment traffic data by vehicle class for the United States: A machine learning approach
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Data from: Experimental investigation of ant traffic under crowded conditions.
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Data from: Stressful city sounds: glucocorticoid responses to experimental traffic noise are environmentally dependent
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Data for: Effect of low-traffic roads on abundance of ground-nesting birds in sub-Arctic habitats
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Data from: Sex-linked gene traffic underlies the acquisition of sexually dimorphic UV color vision in Heliconius butterflies
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Livorno, Urban driving, Automated vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p> </p> <p><strong>Session description</strong>:</p> <p> </p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, Automated and connected vehicle and smart traffic light
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light.</p> <p><strong>Session description</strong>:</p> <p>A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
German Traffic Sign Detection Benchmark - 3 different types of backdoor patterns added
<p>Datasets are split in Training and Test datasets</p> <p>GTSRB_backdoor_green_1.zip - a green squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_green_0_5.zip - a green squared pattern sized 0.5% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p> <p>GTSRB_backdoor_black_1.zip - a black squared pattern sized 1% of the images area is added randomly around the center in an interval of +/-20% of time images width and height</p>
Recording of video on demand traffic
<p>The recording was created with the General Purpose Network Testing Framework:</p> <p><a href="https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework">https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework</a></p>
Recording of one directional marketplace traffic
<p>The recording was created with the General Purpose Network Testing Framework:</p> <p><a href="https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework">https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework</a></p>
Recording of web browsing traffic
<p>The recording was created with the General Purpose Network Testing Framework:</p> <p><a href="https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework">https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework</a></p>
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (>2M images) traffic sign recognition dataset (<a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the <a href="https://github.com/olivesgatech/CURE-TSR">CURE-TSR</a> dataset were cropped from the <a href="https://github.com/olivesgatech/CURE-TSD">CURE-TSD</a> dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool. Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our <a href="https://github.com/olivesgatech/CURE-TSR">GitHub page</a> for code, papers, and more information.</p> <p>Instructions: </p> <p>The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp"</p> <ul> <li> <p>sequenceType: 01 - Real data 02 - Unreal data</p> </li> <li> <p>signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking</p> </li> <li> <p>challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze</p> </li> <li> <p>challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge.</p> </li> <li> <p>Index: A number shows different instances of traffic signs in the same conditions.</p> </li> </ul>
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.