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1,308 results for “Vehicle”
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
Real-world Optimization Benchmark from Vehicle Dynamics - Data and Code
<p>Data and Code of five 2D single-objective optimization problems from vehicle dynamics design for benchmarking</p> <p>Conference Paper at ECTA Real-world Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-)Optimized Algorithm Parameters</p>
Linked collectors and determiners for: Notes on Afrotropical Cydnidae (Heteroptera) with emphasis on vehicle-mounted net samples and description of a new species from Liberia, West Africa.
Natural history specimen data linked to collectors and determiners held within, "Notes on Afrotropical Cydnidae (Heteroptera) with emphasis on vehicle-mounted net samples and description of a new species from Liberia, West Africa". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0d3eae8f-98c7-42cb-884d-8091713dabb8">https://bionomia.net/dataset/0d3eae8f-98c7-42cb-884d-8091713dabb8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0d3eae8f-98c7-42cb-884d-8091713dabb8">https://gbif.org/dataset/0d3eae8f-98c7-42cb-884d-8091713dabb8</a>. Formatted as a Frictionless Data package.
Matriculació de vehicles nous des del 2015 fins al 2023
<p>Aquest dataset conté informació de la web <a href="https://datosmacro.expansion.com/negocios/matriculaciones-vehiculos" rel="nofollow">https://datosmacro.expansion.com/negocios/matriculaciones-vehiculos </a>sobre les matriculacions i venta de vehicles mensuals i anuals pels anys: 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 i 2023 i pels païssos: Alemania, Brasil, Espanya, Grècia, Irlanda, Itàlia i Reine Unit.</p>
UF & UAB's Phase I Demonstration Study: Older Driver Experiences with Autonomous Vehicle Technology (Project D2)
<p>Enclosed you will find the data collected during our STRIDE Phase I research project (D2) and a data dictionary.</p>
Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles - summary results
<p>Summary results for submitted article: Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles</p> <p> </p>
Identity Based Proxy Re-encryption Source Code for Security and Privacy in Connected Vehicle
<p>This is a source code for identity based proxy re-encryption using special string attribute for connected vehicle and Privacy, designed in python using Charm Cryptographic library using Pairing Group ss512 and 1024 bits.</p>
DATABASE: Electric Vehicle, Battery and Smart Grid patent citation networks and main paths.
<p>This dataset comprises the original patent citation networks that were created to calculate the main citation paths for the technologies of Electric Vehicle, Battery and Smart Grid.</p> <p>For each technology (1- Electric Vehicle, 2- Battery, 3- Smart Grid), four outputs are provided:</p> <p>a- Patent extraction: USPTO patents filtered by IPC or CPC and found in the Triadic Patent Families database (OECD, 2021) </p> <p>b- Full nodes and links reconstructed by following patent citations through a snowball method (until no further patents found)</p> <p>c- Filtered nodes and links according to keywords</p> <p>d- Main path nodes and links (with citation weights).</p> <p>For a detailed explanation of the methodology please refer to the submitted paper:</p> <p><strong>Transitions as a coevolutionary process: the urban emergence of electric vehicle inventions</strong></p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem
<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem. <br> The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.</p>
Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins
<p><span>Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals for visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs or drones) to collect video of dolphins to characterize availability and investigate errors surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with the Australian humpback dolphin </span><em><span>Sousa sahulensis</span></em><span> off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (</span><span>se</span><span> = 0.26) and 39.27 sec (</span><span>se</span><span> = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7-sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions, and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.</span></p>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
Data and code from: Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti
<p>This is the first release. Potential updates will be available on GitHub: <a href="https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area">https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area</a></p>
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
<p>Add citation file.</p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Maneuverability Characterization of Autonomous Surface Vehicle (ASV): ITTC zig-zag test dataset
<p>The two files refer to the same dataset: the .csv file is the raw format that is acquired by the ASV robotic platform. The .nc file contains the same data but in a standard format and with global and variable metadata generated using a standardization workflow (based on FAIR Principles) developed at CNR INM which uses controlled and standard vocabularies (ACDD and standard CF).</p> <p>The data refer to the execution of zig-zag maneuvers of the ASV following the ITTC standards</p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
Paper data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
<p>This repo contains the study and appendix data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443.</p>
PoqueiraVehicleLPR: A Dataset of Vehicle Detection Sensors in the region of Barranco de Poqueira in the Alpujarra Granadina.
<p>This dataset is linked to the analysis of different aspects related to the conservation of the Sierra Nevada National Park through advanced digital systems. The devices have been deployed along the road that passes through the municipalities of Pampaneira, Bubión and Capileira, in the Alpujarra region of Granada. Data is collected by 4 devices equipped with vehicle detection sensors. These devices are Hikvision LPR IP cameras with Automatic number-plate recognition (ANPR) based on Deep Learning. The devices have a 2MP resolution, 2.8-12 mm varifocal optics, and IR LEDs with a range of 50 m.</p> <p>To cover the entrances and exits of each village in the target area, we strategically positioned the cameras. The locations are entrance to Pampaneira from the western part of the Alpujarra (PAM1), entrance to Pampaneira from the eastern part of the Alpujarra (PAM2), entrance to Bubión via a single road (BUB), and entrance to Capileira via a single road (CAP). </p> <p>The attached data in the CSV files DATA_VEHICLES_2022 and DATA_VEHICLES_2023 contain information about vehicle passages in the years 2022 (from February to December) and 2023 (from January to August) for each installed camera. It includes anonymized license plate information based on an identifier to facilitate cross-referencing and in-depth analysis. The collected variables include:</p> <ul> <li> <p>camera_ID: License Plate Recognition (LPR) camera identifier.</p> </li> <li> <p>date: Timestamp indicating the date and time of the vehicle passage through the camera.</p> </li> <li> <p>num_plate_id: Anonymized license plate identifier. A value of -1 indicates that the vehicle was not correctly identified by the camera.</p> </li> <li> <p>direction: Binary value (IN/OUT) indicating whether the vehicle is entering or exiting the municipality referenced by the camera.</p> </li> </ul>
Simulated Highway Lane Change Data with Uncertain Vehicle Parameters
<p>This data set consists of closed-loop simulations for highway lane changes by means of time series data. Thereby, the lane changes are planned with three different planners (state variable filter, fifth order polynomial, optimization-based), and executed with four different controllers (flatness-based feedforward with PD feedback control, input/output linearization, higher order sliding mode controller, inversion & invariance based control). The vehicle dynamics model is a non-linear bicycle model. The lane change scenarios vary in lane change duration, vehicle velocity, vehicle parameters, and odometry errors.<br>Additionally to the data, the Matlab code to repeat the analysis on your own is provided.<br>For more details, refer to:</p> <p>M. Gurtner, J. Weber, P. Zips and A. Kugi, "The Role of Trajectory Planners in Lane Change Tracking Control: A Monte Carlo Evaluation of Four Controllers under Uncertainty," <em>2024 European Control Conference (ECC)</em>, Stockholm, Sweden, 2024, pp. 3847-3853, doi: 10.23919/ECC64448.2024.10590871.</p> <p> </p> <p>The research leading to these data has received funding from the Mobility of the Future programme (Grant No. 884344).<br>Mobility of the Future is a research, technology and innovation funding programme of the Republic of Austria, Ministry of Climate Action.<br>The Austrian Research Promotion Agency (FFG) has been authorised for the programme management.</p>
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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.