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1,308 results for “Vehicle”
Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift
<p>Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management.</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth.
Acceleration Data at Various Locations on Vehicle On Four Post Test Rig over Different Roads and at Different Tyre Pressures
<p>Dataset of acceleration data at various locations on sport utility vehicle on four post test rig over different roads and at different tyre pressures. This dataset can be used for driving comfort evaluation. </p>
A Point Cloud Dataset of Vehicles Passing Through a Toll Station for use in Training Classification Algorithms
<p>This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total object classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems.</p> <table> <tbody> <tr> <td>Type</td> <td>Description</td> <td>Quantity</td> </tr> <tr> <td>1</td> <td>Cars, campers, vans and 2-axle trucks with<br>a single tire on the rear axle</td> <td>31,432</td> </tr> <tr> <td>2</td> <td>Minibuses with a single tire on the rear axle</td> <td>452</td> </tr> <tr> <td>3</td> <td>Buses</td> <td>1,158</td> </tr> <tr> <td>4</td> <td>Trucks with 3 or more axles</td> <td>1,008</td> </tr> <tr> <td>5</td> <td>2-axle small trucks</td> <td>1,179</td> </tr> <tr> <td>6</td> <td>2-axle large truck</td> <td>797</td> </tr> <tr> <td>Total</td> <td> </td> <td>36,026</td> </tr> </tbody> </table>
Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle
<p>Dataset for conference paper "Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle"</p>
Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios
<h1>Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios</h1> <p>Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying the key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ($2D$) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each key feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values which are associated with effectiveness can be used for selection and prioritization of effective test cases.</p>
Dataset: Envirotech Vehicles, Inc. (EVTV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco Electric Vehicle Metals Commodity Strategy No K-1 ETF (EVMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Autonomous & Electric Vehicles ETF (DRIV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Commercial Vehicle Group, Inc. (CVGI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust S-Network Future Vehicles & Technology ETF (CARZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 3 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 3: Variation (mean ± S.E.) in species richness, diversity (H') and total density among sectors and depth ranges.
Fig. 1 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 1: Map of the study area in the southern Tyrrhenian Sea. The three sectors of the Aeolian Archipelago (1=Western sector; 2=Central sector; 3=Eastern sector) and locations of the ROV transects () and the main fishing ports () are indicated in the inset maps.
Fig. 2 in The demersal fish assemblages of the infra and circalittoral coastal rocky bottoms of the Aeo- lian Archipelago (Central Mediterranean Sea) studied by Remotely Operated Vehicle (ROV) Abstract
Fig. 2: Scatter plot of the canonical discriminant analysis on the effects of (a) sector and (b) depth range. Species contribution to the observed patterns is shown with directional vectors. Aant=Anthias anthias, Afil=Aulopus filamentosus, Crub=Callanthias ruber, Cchr=Chromis chromis, Cjul=Coris julis, Dgib=Dentex gibbosus, Dvul=Diplodus vulgaris, Gkol=Gobius kolombatovici, Hdac=Helicolenus dactylopterus, Mhel=Muraena helena, Scab=Serranus cabrilla, Teph=Thorogobius ephippiatus.
Dataset: Zapp Electric Vehicles Group Limited (ZAPPW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Zapp Electric Vehicles Group Limited (ZAPP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset used in "Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle". https://doi.org/10.5194/hess-2017-625.
<p>Dataset used in</p> <p>Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle</p> <p>Filippo Bandini<sup>1</sup>, Daniel Olesen<sup>2</sup>, Jakob Jakobsen<sup>2</sup>, Cecile Marie Margaretha Kittel<sup>1</sup>, Sheng Wang<sup>1</sup>, Monica Garcia<sup>1</sup>, and Peter Bauer-Gottwein<sup>1</sup></p> <ul> <li><sup>1</sup>Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark</li> <li><sup>2</sup>National Space Institute, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark</li> </ul> <p><strong>Hydrol. Earth Syst. Sci.</strong></p> <p><strong>https://doi.org/10.5194/hess-2017-625</strong></p> <p> </p> <p>The dataset contains</p> <p>-data/observations that were used to obtain the figures shown in the paper. Data have .mat extension (Binary data container format used by MATLAB; may include arrays, variables, functions, and other types of data;)</p> <p>-scripts to compute statistics and plot data, with .m extension (contain MATLAB code, either in the form of a script or a function)</p> <p>-shape files (shp — shape format; the feature geometry itself, .shx — shape index format, .dbf — attribute format, .prj — projection format; .sbn and .sbx — spatial index of the features, .cpg — used to specify the code page, .<em>qpj</em> QGIS projection file) or raster files (.geotiff) to reproduce the map contents reported in the referenced paper.</p> <p>The repository is subdivided into directories containing the dataset shown in the paper. These directories are named with the figures and/or tables numbers of the referenced paper. </p>
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments [dataset]
<p>This dataset is related to "Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments" in IEEE RA-L,2019.</p> <p> </p> <p>Data Capture<br> ========================<br> Data is obtained by manually flying the UAV through the redwood forest environment using a FrSky Taranis (Plus) Digital Telemetry Radio System. In total, 81,674 frames were captured together with the flight behaviour that comprehends flights under and above the forest canopy, navigation inside caves and on river beds, lakes and mountains.</p> <p> </p> <p>Folder Structure<br> ========================<br> |-manual_0 - manual_5: sequences containing training data</p> <p>|-test_0 - sequences containing testing data</p> <p> </p> <p>Data Protection<br> ========================<br> Gathered by simulated flight using Microsoft AirSim (2019) and released in accordance with MSR Aerial Information and Robotics Simulator (AirSim) lisence, which is described in details bellow:</p> <p> </p> <blockquote> <p>The MIT License (MIT)</p> <p>MSR Aerial Informatics and Robotics Platform<br> MSR Aerial Informatics and Robotics Simulator (AirSim)<br> Copyright (c) Microsoft Corporation<br> All rights reserved.<br> MIT License</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ""Software""), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br> THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p> </blockquote>
SUMO intersection model and vehicle trip data
<p>There are two parts of the data: 1) a SUMO model of a typical intersection that consists of 4 approaches, each of which consists of 3 movements (left turn, right turn, and straight); 2) the vehicle trip information data generated by SUMO under different volume files, which is used to train the intersection signal control algorithm.</p> <p>The SUMO model contains five ".xml" files (node, edge, connection, net, and additional files) which are used to construct and configure the model. One can refer to the official SUMO tutorial for the format and functions of these files: (<a href="https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo">https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo</a>) </p> <p>The vehicle trip data is generated by SUMO as an output (which is specified in ".sumocfg" file). One can refer to the official tutorial (<a href="https://sumo.dlr.de/wiki/Simulation/Output/TripInfo">https://sumo.dlr.de/wiki/Simulation/Output/TripInfo</a>) to understand the data format.</p> <p>Note that readers capable to read ".xml" files like Notepad++ are required to read the SUMO model and vehicle trip data.</p>
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.