Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,308
datasets available to search
ShareScore release 0.9.0
Dataset results
1,308 results for “Vehicle”
Vehicle-to-grid Response on 13 February 2024 in Australian National Electricity Market
<p>This data captures the response of 16 Nissan LEAF electric vehicles to a frequency contingency in the Australian National Electricity Market on the 13th of February 2024, which led to widespread blackouts in Melbourne. The data comes from the bidirectional Wallbox Quasar chargers, as well as six high speed power meters located at the grid connection of each of the properties in which the vehicles were charging.</p>
Vehicle Stock Numbers and Survival Functions for On-road Exhaust Emissions Analysis in India: 1993-2018
<p>Guttikunda, S. K. Vehicle Stock Numbers and Survival Functions for On-road Exhaust Emissions Analysis in India: 1993-2018. <br>Final publication online @ <a href="https://www.mdpi.com/2071-1050/16/15/6298">https://www.mdpi.com/2071-1050/16/15/6298</a> <br><br>Preprints 2024, 2024051393. <a href="https://doi.org/10.20944/preprints202405.1393.v1">https://doi.org/10.20944/preprints202405.1393.v1</a></p> <p>Older publication:<br>Re-fueling road transport for better air quality in India<br><a href="https://www.sciencedirect.com/science/article/abs/pii/S0301421514000020">https://www.sciencedirect.com/science/article/abs/pii/S0301421514000020</a></p> <p>An informed emissions inventory can help define the baseline, use that baseline to formulate an effective air quality management plan and track progress or lack thereof, and use the results for research, innovation, and public awareness. In air quality management, at urban and regional levels, road transport remains the cornerstone of residential, commercial, and industrial activities, and the vehicle exhaust emissions maintain the position of one of the key contributing sources.</p> <p>In Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads, contribute to as much as 50% of particulate matter pollution in a year. So, having access to a vehicle exhaust emissions inventory that is reliable and replicable is critical for air quality management and one of the key inputs to this exercise is vehicle stock numbers. These numbers are typically obtained from vehicle registration databases, traffic surveys, and other governmental records, and often require time consuming data cleaning protocols before they can used for emissions analysis.</p> <p>This database provides a clean and open-access vehicle stock database, as registered and in-use fleet, for the period covering 1993 and 2018, for all India and states, along with an estimate of age-mix of the vehicles using survival functions.</p> <p>VAPIS Excel players included here are</p> <ul> <li>·A method to convert fleet average speeds and fleet average travel time per day into vehicle km travelled per day.</li> <li>·A method to calculate how many additional buses are required to support odd-even or an equivalent scheme (with and without fuel mix exemptions).</li> <li>·A method to calculate total fuel wasted from idling in the city and to calculate savings from traffic management.</li> <li>·A method to calculate fuel and emission benefits of shifting a share of 2-wheeler and 4-wheeler trips to buses and non-motorized transport.</li> <li>·A method to estimate vehicle exhaust emission factors using emission standards and deterioration rates.</li> <li>·An example set of survival rates based on vehicle age for nine broad vehicle categories to convert registered number of vehicles to in-use number of vehicles.</li> <li>·A method to spatially disaggregate (grid) the total vehicle exhaust emissions using multiple grid-level proxies as weights such as density (km per grid) of various road types, population density, landuse-landcover, and information on commercial and industrial activities.</li> <li>·A library of emission factors for aerosols and gaseous species.</li> </ul>
Grounding line remote operated vehicle (GROV) exploration of the ice shelf cavity of Petermann Glacier, Greenland
<p>The melting of ice by ocean waters along the periphery of ice sheets is a major physical process driving their evolution in a warming climate. Using the fiber-optic-tethered Grounding line Remote Operated Vehicle (GROV), we explored the ice shelf cavity of Petermann Glacier, in Northwestern Greenland, in May 2023, using a novel interferometric multibeam sonar operating at 117 KHz with 360° viewing capability. The seafloor depth is uniform at 820 m and 200 m deeper than anticipated. At the ice shelf base, we find a succession of terraces interrupted by 20-40 m ice cliffs that have no signature at the surface, but are consistent with double-diffusive convection. The central melt channel deviates by ± 80 m from flotation, is smoother than indicated by the surface, and reveals asymmetric melt. The results demonstrate the fundamental importance of surveying the geometry of ice shelf cavities to document ice-ocean interaction.</p>
Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05
<i>This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.54 GB of MP4 files, which were trimmed into 11362 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.65% of these extracted images are useful and 0.35% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.52 %, Q2: 5.31 %, Q5: 1.17 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 2.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.137 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 28.1 GB of MP4 files, which were trimmed into 9884 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 52.75% of these extracted images are useful and 47.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 94.76 %, Q2: 5.02 %, Q5: 0.23 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 3.0 m and 30.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.787 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 32.76 GB of MP4 files, which were trimmed into 12042 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 2.63% of these extracted images are useful and 97.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.32 %, Q2: 4.33 %, Q5: 2.35 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.699 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions
<p>This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China.</p> <p>See our published paper for more information: </p> <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>
Vehicle trajectories collected from a swarm of drones in a Swiss city
<p>This is an open dataset of naturalistic vehicle trajectories that have been collected by two drones flying over the city of Pully, Switzerland. This detailed dataset includes 30 datapoints per second for each vehicle, covering three types: Car, Bus, and Truck.</p> <p><strong>How are the files organized?</strong></p> <p>The name of the file follows the format: DX_TIMEDAYZ_LOCATION</p> <ul> <li>DX - indicates the drone number, either Drone 1 or Drone 2</li> <li>TIMEDAYZ - indicates whether the dataset is collected during morning (AM), noon (NOON) or afternoon (PM) and then Z for the number of session.</li> <li>LOCATION - indicates the location that each drone was flying. As Drone 1 was only in 1 location, this value changes only for Drone 2.</li> </ul> <p><strong>How are the .csv files organized?</strong></p> <div>For each .csv file the following apply:</div> <div> <ul> <li>each row represents one datapoint</li> <li>the first column includes the unique track_id (per file)</li> <li>the second column includes the type of the specific vehicle</li> <li>the third and fourth column includes the longitude and latitude</li> <li>the fifth column includes the local time in ISO 8601 format</li> </ul> </div>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 24.11 GB of MP4 files, which were trimmed into 8401 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.99% of these extracted images are useful and 0.01% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 18.95 %, Q2: 63.13 %, Q5: 17.92 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.32 GB of MP4 files, which were trimmed into 10244 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 86.22% of these extracted images are useful and 13.78% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 24.24 %, Q2: 71.19 %, Q5: 4.57 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
Vehicle-to-cell hierarchical overview of a typical electrified powertrain architecture
<p>Schematic depicting the vehicle-to-cell hierarchical overview of a typical electrified<br> powertrain architecture. This represents the system-level context within which the proposed<br> layer optimisation framework has been developed. Two xEV powertrains — a) a Battery Electric<br> Vehicle (BEV), and b) a series Plug-in Hybrid Electric Vehicle (PHEV) are chosen as examples to<br> demonstrate how the methodology facilitates common module designs for such battery packs.</p>
Greek Government Vehicles
<p>A dataset that contains aggregated information about the government vehicles in Greece. The dataset is serialized in RDF and uses the RDF Data Cube Vocabulary (W3C recommendation). The dataset contains information such as the count of vehicles, the average/minimum/max age, average/minimum/max engine displacement and average/minimum/max seating capacity. All the information are described based on the vehicle type, fuel type and governmental agency that own the vehicle. </p>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
Bridge Inspecting with Unmanned Aerial Vehicles R&D
<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>
Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data
<p>Corresponding data set for Tran-SET Project No. 18PLSU08. Abstract of the final report is stated below for reference:</p> <p>"Automated and timely road pavement damage inspection is critical to the preventive maintenance and the long-term sustainability and resilience of roads in Region 6. Current road inspection practices rely heavily on a manual process. Sensor-based methods (e.g., LiDAR scanning) are promising but can be too expensive for a wider adoption. This study employs a crowdsourcing approach of using the vibration patterns of regular vehicles in inferring specific types of road damages. A cloud-based smart phone app and system was developed to collect real-time vehicle vibrations, location data, and road damage images for training the detection model. However, there is a great challenge in using classic classification methods with crowdsourced vibration data containing high level of noises, as vehicle vibrations are greatly affected by the types and conditions of the vehicles, as well the varying driving behaviors of drivers. The study thus employed the recent developments in Deep Learning methods, including a Self-Taught Learning (STL) algorithm and Sparse Coding to tackle with the low-quality issues of collected data. A total of 310 miles of road-induced vehicle vibration data was collected in Texas and Louisiana, and the road damage detection model was trained on Texas A&M University (TAMU) supercomputing server. The results show that the features generated from Sparse Coding greatly contribute to enhancing detection performances, by addressing low-quality data issues."</p>
Figure 2 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer
Figure 2. The distribution of accidentally killed mammals on the road course.
Figure 1 in Identifying the hotspots of wildlife-vehicle collision on the Çankırı-Kırıkkale highway during summer
Figure 1. Study area.
EMS3D-KITTI-Synthetic: A Synthetic 3D Dataset in KITTI Format with Balanced EMS Vehicle Distribution for Autonomous Driving AI Model Training
<p>A 3D synthetic dataset in KITTI format, focused on emergency vehicles such as ambulances and police cars. The dataset was generated across 8 towns within the CARLA simulator and converted into the KITTI format, ensuring compatibility for direct use in AI model training for autonomous driving applications.</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.