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1,855 results for “Autonomous”
Skin autonomous antibody production regulates host-microbiota interactions
<p>Supplemental Tables containing bulk BCR-sequencing clonotype results for all samples and for sequences with somatic hypermutations. Data, analysis and results are described in more detail in the accompaning publication Gribonika et al., "Host-microbiota interaction is regulated by autonomous skin-intrinsic germinal centers", Nature, 2024</p>
Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-07
<i>This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-07.</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 78.22 GB of MP4 files, which were trimmed into 17724 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 29.87% of these extracted images are useful and 70.13% 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: 45.47 %, Q2: 8.05 %, Q5: 46.48 % <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 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 0.68 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 44, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True} </code> <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 36.25 GB of MP4 files, which were trimmed into 8316 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.77% of these extracted images are useful and 0.23% 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: 18.63 %, Q2: 80.96 %, Q5: 0.41 % <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 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 0.408 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 44, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True} </code> <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 Boucan, Réunion - 2023-11-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-22.</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 35.76 GB of MP4 files, which were trimmed into 9704 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: 95.98 %, Q2: 3.96 %, Q5: 0.06 % <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 0.451 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 50, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True} </code> <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 Boucan, Réunion - 2023-11-21
<i>This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-21.</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 21.24 GB of MP4 files, which were trimmed into 7790 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 98.45% of these extracted images are useful and 1.55% 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: 33.31 %, Q2: 65.47 %, Q5: 1.22 % <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 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 0.346 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 44, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True} </code> <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 St-Leu, Réunion - 2023-11-10
<i>This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-10.</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 22.68 GB of MP4 files, which were trimmed into 7786 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: 84.7 %, Q2: 15.17 %, Q5: 0.13 % <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 0.208 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 50, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True} </code> <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>
Interactive Widget – Acceptance of autonomous vehicles dataset exploration
<p><strong><a href="https://research-data.shinyapps.io/CAV_Acceptance/">https://research-data.shinyapps.io/CAV_Acceptance/</a></strong></p> <p>The following widget gives citizens access to data collected with the Connected and Autonomous Vehicle Acceptance Assessment Tool (CAVA) developed in the PAsCAL project (Public acceptance of Connected and Autonomous vehicles). The aim of the CAVA is to measure autonomous vehicle acceptance via evaluation of expected autonomous vehicle consequences. A survey was employed with over 5000 participants from 11 countries.</p>
Non-cell autonomous small RNA silencing in Arabidopsis female gametes
<p>Raw data tables as well as R script</p>
Acoustic Current Profiler data from Multi-Year (2020-2022) Autonomous Underwater Glider Surveys in the Anegada Passage
<p>This dataset contains glider acoustic doppler profiler observations from four glider deployments in the Anegada Passage region from 2020-2022. The 2020-2021 data are from a Nortek AD2CP and the 2022 data are from a Teledyne RDI Pathfinder. The RDI Pathfinder .PD0 files can be read directly in the code that is used for this analysis. The Nortek AD2CP .ad2cp files are processed using Nortek's MIDAS software to generate NetCDFs. <br> </p>
PAsCAL WP6 Pilot 1 High-Capacity Autonomous Bus Operations Video
<p>A summary video documenting and introducing the activities of pilot 1 of the PAsCAL real-world pilots. The part of the pilot which is documented in the video took place at the E-Bus Competence Center in Liverange, Luxembourg between June 2021 and September 2021. The participants were subjected to a so-called "Wizard of Oz" experiment, in which a regular high-capacity bus was modified and prepared in such a way that the human driver of the bus was hidden and not visible to the passenger, who believed that the bus was fully autonomous. Several malfunctions during the bus ride were simulated to test a Human-Machine-Interface and its' adequacy to replace human driver support for passengers on-board.</p>
PAsCAL WP6 Pilot 2 Autonomous Driving Training Video
<p>A summary video documenting and introducing the activities of pilot 2 of the PAsCAL real-world pilots. The part of the pilot which is documented in the video took place at the Automobile Club d'Italia Vallelunga circuit in Rome, Italy between January 2022 and February 2022. The participants took part in a driving training for autonomous vehicles and specifically for advanced ADAS systems. Some took part in the training before and some after completing a driving course, where they had to overcome several different challenging conditions and manoeuvres.</p> <p> </p>
A traffic graph dataset for autonomous driving research: Commonroad-Nuplan-Dataset
<p>We provide a standardized graph dataset for traffic based on the large-scale <a href="https://www.nuscenes.org/nuplan">NuPlan </a>v1.1 dataset, converted to a <a href="https://pytorch-geometric.readthedocs.io/en/latest/">PyTorch-Geometric </a>dataset using our <a href="https://commonroad.in.tum.de/tools/commonroad-geometric">CommonRoad-Geometric </a>tool. The dataset is collected from 3 megacities: Singapore, Boston and Pittsburgh.</p>
Genbank and BOLD accession numbers of sequences from "New insights into the diversity of cryptobenthic Cirripectes blennies in the Mascarene Archipelago sampled using Autonomous Reef Monitoring Structures (ARMS)"
<p><strong>Abstract:</strong></p> <p>Autonomous Reef Monitoring Structures (ARMS) are artificial mini-reefs designed for standardised sampling of sessile and small motile cryptobenthic organisms. ARMS are also effective for collecting small cryptobenthic fishes, such as the combtooth blennies of the genus <em>Cirripectes</em>. Recent studies discovered several <em>Cirripectes</em> species endemic to islands or archipelagos, in spite of the generally broad distributions of tropical and subtropical blennies. Thus, in order to evaluate the diversity and distribution of <em>Cirripectes</em> species in the Mascarene Archipelago, a little studied region but an important biodiversity hotspot, complete mitochondrial genomes and nuclear rhodopsin genes were sequenced for 39 specimens collected with ARMS deployed on outer reef slopes at Reunion and Rodrigues islands. Mitochondrial COI sequences were analysed to integrate these specimens within the largest dataset of publicly available sequences. Three species were found in the Mascarene Archipelago, <em>Cirripectes castaneus, C. randalli, </em>and <em>C. stigmaticus</em>. <em>C. castaneus</em> and <em>C. stigmaticus</em> both have an Indo-Pacific distribution with several haplotypes shared among distant localities. In agreement with the literature, <em>C. randalli </em>shows a small-range endemism restricted to the Mascarenes. We confirmed the presence of <em>C. castaneus, C. randalli </em>and <em>C. stigmaticus </em>in Rodrigues, and the presence of <em>C. stigmaticus</em> in Reunion. This study contributes to fill the gaps in taxonomic and molecular knowledge of the reef cryptobiome in the South-West Indian Ocean, and provides the first complete mitogenomes for the genus, a crucial step for future molecular-based inventories (e.g. eDNA).</p> <p><strong>Content of this repository:</strong></p> <p>Genbank and BOLD accession numbers of sequences from "New insights into the diversity of cryptobenthic <em>Cirripectes</em> blennies in the Mascarene Archipelago sampled using Autonomous Reef Monitoring Structures (ARMS)" published in<em> Ecology and Evolution</em> (2023)</p>
Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm: supplementary dataset
<p>This repository contains the data files used to generate the figures presented in the following article:<br> Rouillé, E. et al. (2023) "Radio-Source Tracker: Autonomous Attitude Determination on a Radio Interferometric Swarm", submitted to Radio Science.<br> (Pre-print Arxiv, TBD)</p> <p>The data are the output of various runs of the simulation pipeline.<br> Simulations were run for various interferometer configurations, frequencies, noise levels and random draws.<br> This pipeline and its related package can be found online at the following address:<br> https://gitlab.obspm.fr/erouille/noire_simulation/</p>
Using standardized fish-specific autonomous reef monitoring structures (FARMS) to quantify cryptobenthic fish communities
<p>1. Biodiversity inventories and monitoring techniques for marine fishes often overlook small (<5cm), bottom-associated ('cryptobenthic') fishes, and few standardized, comparative assessments of cryptobenthic fish communities exist. We sought to develop a standardized, quantitative survey method for cryptobenthic fishes that permits their sampling across a variety of habitats and conditions.</p> <p>2. Fish-specific autonomous reef monitoring structures (FARMS) are designed to sample cryptobenthic fishes using a suite of accessible and affordable materials. To generate a variety of microhabitats, FARMS consist of three layers of stacked PVC pipes in three different sizes, as well as a bottom and top level of loose PVC-pipe fragments in a mesh basket. We deployed FARMS across a variety of habitats, including coral reefs, seagrass beds, oyster reefs, mangroves, and soft-bottom habitats across six locations (Hawai'i, Texas, Panama, Saudi Arabia, Brazil, and Curaçao).</p> <p>3. From shallow estuaries to coral reefs beyond 100 m depth, FARMS attracted distinct communities of native cryptobenthic fishes with strong site or habitat specificity. Comparing the FARMS to communities sampled with alternative methods (enclosed clove-oil stations on coral reefs in Panama and oyster sampling units on oyster reefs in Texas) suggests that FARMS yield a subset of cryptobenthic species representative of those present on local coral and oyster reefs. While FARMS yield fewer individuals per sample and are restricted to cryptobenthic species, they are efficient sampling devices relative to the sampled area.</p> <p>4. We demonstrate that FARMS represent a useful tool for standardized collections of cryptobenthic fishes. While natural substrata are bound to yield more mature communities with a larger number of individuals and wider range of specialist species, the potential to deploy and retrieve FARMS in turbid environments, beyond regular SCUBA depth, and where fish collections using anesthetics or ichthyocides are forbidden suggests that they are a valuable complementary technique to survey fishes in aquatic ecosystems. Deploying FARMS in locations and habitats where cryptobenthic fish communities have not been studied in detail may yield many valuable specimens of unknown or poorly known species.</p>
NexusStreets: a dataset combining human and autonomous driving behaviours
<p>The NexusStreets dataset contains human and autonomous driving scenes. They are collected by monitoring a target vehicle that can be either autonomous or controlled by a human driver. Data is presented in the shape of:</p> <ul> <li>sequences of JPEG images, one image per timestamp</li> <li>target vehicle state information for each timestamp</li> </ul> <p>The dataset has been built on the CARLA simulator, thanks to Baidu Apollo and a Logitech G29 steering wheel for the autonomous and human drivings, respectively.<br>The dataset consists of 520 scenes (260 pairs of mirrored scenarios) of 60 seconds each.<br>The folders are organized as follows:</p> <pre><code>. ├── ... ├── <driving mode> │ ├── <town> │ │ ├── <trial> │ │ │ └── ... │ │ └── ... │ └── ... └── ...</code></pre> <ul> <li>driving mode: corresponds to the control modality of the target vehicle under test and can be either Baidu Apollo or manual driving;</li> <li>town: one of the five default maps in CARLA (e.g., Town01, Town02, etc);</li> <li>trial: 60 different trials per map, they differ in traffic and weather conditions (except Town04). Each trial records 60 seconds of simulation, logging 120 frames per video and an equal number of rows per CSV. In particular, each trial includes: <ul> <li>video: this folder groups the JPEG images;</li> <li>state_features.csv: reports the state information of the target vehicle for each frame;</li> <li>detection_features.csv: reports the 2D bounding box detections obtained from a pre-trained YOLOv7 detector.</li> </ul> </li> </ul>
A quantitative autonomous bioluminescence reporter system with a wide dynamic range for Plant Synthetic Biology
<p>This data set includes: Luminescence (NeoLuc Luminescence), Fluorescence (eGFP) , NeoLuc/eGFP ratios, Area Under The Curve of NeoLuc/eGFP ratios, Normalized Area Under The Curve of Neoluc/eGFP (FBP-RTAs), Firefly Luciferase luminescence (FLuc), Renilla Luciferase luminescence (RLuc), FLuc/RLuc ratios and Normalized FLuc/RLuc values of all experiments in this work. </p>
Figs 1–2 in New records of megachilid bees (Hymenoptera: Megachilidae) from the Nakhchivan Autonomous Republic of Azerbaijan
Figs 1–2. Anthidium (Turkanthidium) unicum Morawitz, 1875, ♀, Azerbaijan: 1 – habitus,
A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System
<p>The field note data related to a case study on AI engineering practices. During the case study, an autonomous stock trading system was developed, while 10 AI engineering practices were applied. We used field notes to document the application of each practice. Additionally, we documented any challenges we experienced.</p> <ul> <li>field-notes-practices.csv: the documented experiences with the AI engineering practices containing the name of the practice, the perceived effectiveness of the practice on an ordinal scale (--, -, 0, +, ++), a description of how it was applied and its effects, and the date when it was applied (in DD.MM.YYYY format).</li> <li>field-notes-challenges.csv: the documented challenges experiences during the case study containing the date (in DD.MM.YYYY format), a description of the challenge, the source that made us became aware of the challenge, the solution we used to overcome the challenge, and a description of the frequency of occurrence.</li> </ul>
Driving environmental images from on-board camera and RTK-based localization for autonomous vehicles
<p>This dataset contains images from an on-board frontal camera as well as information of the vehicle state, including: accurate global localization, heading, speed, acceleration and steering angle position. The data was captured from one of the vehicle instrumenhted vehicles of the <a href="https://autopia.car.upm-csic.es/">AUTOPIA group</a> at the surrounding of the <a href="https://www.car.upm-csic.es/">Centre for Automation and Robotics</a>, in Arganda del Rey (Madrid, Spain).</p> <p>The dataset is aimed to help users in developing and improving image-based road detection algorithms, as well as machine learning end-to-end approaches using driving information provided (steering angle, vehicle speed, etc.).</p> <p>The dataset has also been used to develop algorithms for online map adaptation based on computer vision. Please, refer to:</p> <p>"Artuñedo, A. (2020). Decision-making Strategies for Automated Driving in Urban Environments. In Springer Theses. Springer International Publishing. <a href="https://doi.org/10.1007/978-3-030-45905-5">https://doi.org/10.1007/978-3-030-45905-5</a>"</p> <p><strong>Image information</strong></p> <p>The sequence of images is provided in PNG format, and is named following the pattern: 'l_sA_nsB.png', where A and B are the second and nanosecond when the image was captured, so that A+B*1e-9 is the capture time. The images were captuted with the camera Bumblebee 2 0,8 MP Color FireWire 1394a 3,8mm (Sony ICX204) with the following technical features:</p> <ul> <li>Sensor type: CCD</li> <li>Sensor format: 1/3"</li> <li>Pixel size 4.65 µm</li> <li>Focal length: 3.8mm</li> <li>Fames per second: 20 fps</li> <li>Resolution: 1024 x 768</li> </ul> <p><strong>Camera position</strong></p> <p>The camera is placed at a height 1290 mm measured from the ground plane. With respect to the vehicle frame, it has a yaw of -2º and a pitch 5.5º, in order to focus the field of view in the road.</p> <p> </p> <p><strong>Vehicle localization</strong></p> <p>The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es/">AUTOPIA reseach group</a> at the Centre for Automation and Robotics in Spain. Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc.</p> <p>The vehicle localization file ('vehicle_data.csv') includes the following data at a sample rate of 20 Hz:</p> <ul> <li>Time (s)</li> <li>UTM East (m)</li> <li>UTM North (m)</li> <li>Orientation (º)</li> <li>Speed (m/s)</li> <li>Acceleration(m/s^2)</li> <li>Steering wheel angle (º)</li> </ul> <p>Disclaimer That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.</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.