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 “vehicles”
Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-04
<i>This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-04.</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> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <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 keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 35.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.492 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://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" 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 Sarodrano, Madagascar - 2023-05-05
<i>This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05.</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 34.7 GB of MP4 files, which were trimmed into 9193 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 98.34% of these extracted images are useful and 1.66% 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <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 keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 20.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.463 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://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" 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 Sarodrano, Madagascar - 2023-05-03
<i>This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03.</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 16.67 GB of MP4 files, which were trimmed into 8820 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 81.93% of these extracted images are useful and 18.07% 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <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://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" 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 Sarodrano, Madagascar - 2023-05-05
<i>This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05.</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 20.22 GB of MP4 files, which were trimmed into 5846 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 87.7% of these extracted images are useful and 12.3% 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <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 keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 35.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.352 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://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" 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>
California Electric Vehicle Loads by Feeder Circuit
Open the record for dataset details and reuse information.
Dataset of vehicle emission measurements in real-world subfreezing winter conditions
<p>Dataset of vehicle emission measurements in real-world subfreezing winter conditions. Measured by chasing the measured vehicle. See Info.txt for description of the data.</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
Using snapshot measurements to identify high-emitting vehicles
<p>This repo includes codes and sample data for Qiu and Borken-kleefeld, ERL, 2022.</p> <p><strong>Material for reproducing figures in the paper</strong></p> <ul> <li>R script: plot.r</li> <li>Data for plot 2: <ul> <li><em>RS_Zurich_data.csv</em>: the sample RS data from Zurich.</li> <li><em>algorithm_eu5d_final_iteration.rds</em>: the estimated average emission factor for each city fleet (outputs from the iterative algorithm)</li> </ul> </li> <li>Data for plot 3:<em> </em> <ul> <li><em>Zurich_clean_identification.xlsx</em>: summary of the fraction of clean vehicles being identified by each potential RS threshold. </li> <li><em>Zurich_high_emitter_identification.xlsx</em>: summary of the fraction of high-emitters being identified by each potential RS threshold.</li> </ul> </li> <li>Data for plot 4: <ul> <li><em>validation_test_dataset.csv</em>: the original validation dataset that includes the underlying average emission factor and the simulated instantaneous emissions.</li> <li><em>validation_algorithm_results.rds</em>: algorithm outputs when applied to the validation dataset.</li> </ul> </li> </ul> <p><strong>The iterative algorithm and sample data that can be used for demonstration</strong></p> <ul> <li>Algorithm script: <em>iterative_algorithm.r</em></li> <li>Sample RS data: <em>RS_Zurich_data.csv</em></li> <li>Sample PEMS/Chassis test cycles: <em>sample_pems_chassis_cycles.csv</em></li> </ul>
Roundabout Aerial Images for Vehicle Detection
<p><strong>If you use this dataset, please cite this paper: <em>Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 </em></strong></p> <p>This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.</p> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>Roundabout (scenes)</strong></p> </td> <td> <p><strong>Frames</strong></p> </td> <td> <p><strong>Car</strong></p> </td> <td> <p><strong>Truck</strong></p> </td> <td> <p><strong>Cycle</strong></p> </td> <td> <p><strong>Bus</strong></p> </td> <td> <p><strong>Empty</strong></p> </td> </tr> <tr> <td> <p>1 (00001)</p> </td> <td> <p>1,996</p> </td> <td> <p>34,558</p> </td> <td> <p>0</p> </td> <td> <p>4229</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>2 (00002)</p> </td> <td> <p>514</p> </td> <td> <p>743</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>157</p> </td> </tr> <tr> <td> <p>3 (00003-00017)</p> </td> <td> <p>1,795</p> </td> <td> <p>4822</p> </td> <td> <p>58</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>4 (00018-00033)</p> </td> <td> <p>1,027</p> </td> <td> <p>6615</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>5 (00034-00049)</p> </td> <td> <p>1,261</p> </td> <td> <p>2248</p> </td> <td> <p>0</p> </td> <td> <p>550</p> </td> <td> <p>0</p> </td> <td> <p>81</p> </td> </tr> <tr> <td> <p>6 (00050-00052)</p> </td> <td> <p>5,501</p> </td> <td> <p>180,342</p> </td> <td> <p>1420</p> </td> <td> <p>120</p> </td> <td> <p>1376</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>7 (00053)</p> </td> <td> <p>2,036</p> </td> <td> <p>5,789</p> </td> <td> <p>562</p> </td> <td> <p>0</p> </td> <td> <p>226</p> </td> <td> <p>92</p> </td> </tr> <tr> <td> <p>8 (00054)</p> </td> <td> <p>1,344</p> </td> <td> <p>1,733</p> </td> <td> <p>222</p> </td> <td> <p>0</p> </td> <td> <p>150</p> </td> <td> <p>222</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>15,474</p> </td> <td> <p>236,850</p> </td> <td> <p>2,262</p> </td> <td> <p>4,899</p> </td> <td> <p>1,752</p> </td> <td> <p>552</p> </td> </tr> <tr> <td> <p><strong>Data augmentation</strong></p> </td> <td> <p>x4</p> </td> <td> <p>x4</p> </td> <td> <p>x4</p> </td> <td> <p>x4</p> </td> <td> <p>x4</p> </td> <td> <p>x4</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>61,896</p> </td> <td> <p>947,400</p> </td> <td> <p>9048</p> </td> <td> <p>19,596</p> </td> <td> <p>7,008</p> </td> <td> <p>2,208</p> </td> </tr> </tbody> </table>
References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)
<p>The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-23.</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 13.17 GB of MP4 files, which were trimmed into 5413 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 97.84% of these extracted images are useful and 2.16% 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: 95.29 %, Q2: 4.62 %, Q5: 0.09 % <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 6.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.083 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 Aldabra-Gigi, Seychelles - 2022-10-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Gigi, Seychelles - 2022-10-22.</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 20.08 GB of MP4 files, which were trimmed into 8062 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 98.76% of these extracted images are useful and 1.24% 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: 94.17 %, Q2: 5.18 %, Q5: 0.65 % <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 40.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.175 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 Aldabra-Arm06, Seychelles - 2022-10-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22.</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 24.62 GB of MP4 files, which were trimmed into 9233 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 41.21% of these extracted images are useful and 58.79% 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: 96.03 %, Q2: 3.86 %, Q5: 0.11 % <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 40.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 1.352 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 Aldabra-Arm06, Seychelles - 2022-10-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22.</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 23.38 GB of MP4 files, which were trimmed into 9125 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 87.56% of these extracted images are useful and 12.44% 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: 57.44 %, Q2: 42.55 %, Q5: 0.01 % <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 40.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.928 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 Aldabra-Dubois, Seychelles - 2022-10-23
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Dubois, Seychelles - 2022-10-23.</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 24.93 GB of MP4 files, which were trimmed into 12191 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 54.2% of these extracted images are useful and 45.8% 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: 98.28 %, Q2: 1.68 %, Q5: 0.05 % <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 2.0 m and 20.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.465 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 Aldabra-Arm01, Seychelles - 2022-10-24
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm01, Seychelles - 2022-10-24.</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 24.82 GB of MP4 files, which were trimmed into 8638 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 97.42% of these extracted images are useful and 2.58% 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: 68.72 %, Q2: 30.93 %, Q5: 0.34 % <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 15.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 0.451 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 Aldabra-Arm06, Seychelles - 2022-10-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22.</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 21.06 GB of MP4 files, which were trimmed into 7993 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 51.06% of these extracted images are useful and 48.94% 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: 75.76 %, Q2: 24.12 %, Q5: 0.12 % <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 30.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. <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 1.259 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>
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.