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622 results for “Underwater”
Collaborative High-Resolution Observation Datasets of an Eddy Using an Underwater Glider Network
<p>This matlab mat data provides data from 12 underwater gliders in the northern South China Sea in 2017.</p> <p>Please contact Haibo Tang at tanghb6@mail2.sysu.edu.cn for any questions. <br> <br>Wish you good luck!</p>
Underwater images collected by Scuba diving in Hermitage, Réunion - 2021-04-01
<i>This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-04-01.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br> <h2>Image acquisition</h2> This session has 17.91 GB of MP4 files, which were trimmed into 3165 frames (at 1 fps). <br> The frames are not georeferenced. <br> 88.63% of these extracted images are useful and 11.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0 %, Q2: 0 %, Q5: 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://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 Scuba diving in Hermitage, Réunion - 2021-03-17
<i>This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-17.</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> <h2>Image acquisition</h2> This session has 8.49 GB of MP4 files, which were trimmed into 1483 frames (at 1 fps). <br> The frames are not georeferenced. <br> 83.61% of these extracted images are useful and 16.39% 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> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0 %, Q2: 0 %, Q5: 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://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 Scuba diving in Hermitage, Réunion - 2021-03-17
<i>This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-17.</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> <h2>Image acquisition</h2> This session has 9.07 GB of MP4 files, which were trimmed into 1584 frames (at 1 fps). <br> The frames are not georeferenced. <br> 78.72% of these extracted images are useful and 21.28% 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> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0 %, Q2: 0 %, Q5: 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://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 Scuba diving in Hermitage, Réunion - 2021-03-10
<i>This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-10.</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> <h2>Image acquisition</h2> This session has 9.2 GB of MP4 files, which were trimmed into 1611 frames (at 1 fps). <br> The frames are georeferenced. <br> 71.88% of these extracted images are useful and 28.12% 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> Base : No Base <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://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 Scuba diving in Hermitage, Réunion - 2021-03-17
<i>This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-17.</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> <h2>Image acquisition</h2> This session has 8.82 GB of MP4 files, which were trimmed into 1540 frames (at 1 fps). <br> The frames are not georeferenced. <br> 80.71% of these extracted images are useful and 19.29% 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> Base : No Base <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 0 %, Q2: 0 %, Q5: 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://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>
Example 3D Underwater Acoustic Pressure Data Sampled Over 24 Hours
Open the record for dataset details and reuse information.
OpenFoam model output for "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications"
<p>Dataset of numerical experiments carried out with OpenFOAM v 10 as used in the manuscript "A Fuel Cell Power Supply System Equipped with Artificial Gill Membranes for Underwater Applications" by Lucas Merckelbach and Prokopios Georgopanos.</p> <p> </p>
Fig 2. Live Chthamalus species photographed underwater. A in Biodiversity and Biogeography of Chthamalid Barnacles from the North-Eastern Pacific (Crustacea Cirripedia)
Fig 2. Live Chthamalus species photographed underwater. A, mixed populations of C anisopoma (CAN), with white-edged tergoscutal flaps, Balanus inexpectatatus (BI) with white tergoscutal flaps and Chthamalus alani nom. nov. (CA) with orange tergoscutal flaps (from La Paz, Baja California). B, C. alani showing orange tergoscutal flaps (from La Paz). C, C. hedgcocki showing orange tergoscutal flaps (from Point Lobos, Todos Santos, Baja California). D, Chthamalus newmani sp. nov. showing brown tergoscutal flaps (from Farfan Point, Panama). E, F, mixed population of C. panamensis (CP) showing orange tergoscutal flaps and C. newmani (CN) showing brown tergoscutal flaps (E, Balboa Statue. Panama City and F, Naos Island). doi:10.1371/journal.pone.0149556.g002
FIGURE 5. Koumansetta hectori, underwater photographs. A in Redescription of the genus Koumansetta (Teleostei: Gobiidae), with description of a new species from the Red Sea
FIGURE 5. Koumansetta hectori, underwater photographs. A: Reunion; B: Maldives; C: Philippines. Photos by A. Diringer (A), J. Greenfield (B), P. Poppe (C).
FIGURE 6. Koumansetta rainfordi, underwater photographs. A in Redescription of the genus Koumansetta (Teleostei: Gobiidae), with description of a new species from the Red Sea
FIGURE 6. Koumansetta rainfordi, underwater photographs. A: West Papua; B: Tulamben, Bali, Indonesia; C: Kimbe Bay, Papua New Guinea; D: Sulawesi, Indonesia. Photos by A. Ryanskiy.
Underwater videos of the sharpnose sevengill shark (Heptranchias perlo) from the Tongue of the Ocean, Bahamas
<p>videos from: <strong>First <em>in situ</em> observations of the sharpnose sevengill shark (<em>Heptranchias perlo</em>) from the Tongue of the Ocean, Bahamas by </strong>Brennan T. Phillips, Oliver N. Shipley, Jason Halvorsen, James K. Sternlicht & Austin J. Gallagher</p>
FIGURE 3. Munidopsis maunga n in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 3. Munidopsis maunga n. sp., A, B, F, L: holotype, ♂, NIWA 21138, C–E: paratype, ♂, NIWA 21139, G–K: paratype, ♂, NMNZ CR. 10022. A, carapace and abdomen, dorsal; B, carapace and abdomen, lateral; C, anterior part of cephalothorax, showing right antennule, antenna and ocular peduncle, ventral; D, lateral view of right antennule showing dorsolateral spine (arrowed); E, right third maxilliped, lateral; F, sternal plastron; G, left cheliped, dorsal; H–J, right pereopods 2–4, lateral; K, dactylus of right pereopod 2, lateral; L, posterior part of abdomen with uropods and telson. Scale: 2 mm (scale 1 for A, B, F–J, L; scale 2 for C–E, K).
FIGURE 6. Munidopsis papanui n in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 6. Munidopsis papanui n. sp., A, holotype, ♂, NMNZ CR. 10023, dorsal view. B, paratype, ♀ NIWA 21140, dorsal view.
FIGURE 2. Munidopsis kaiyoae Baba 1974 in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 2. Munidopsis kaiyoae Baba 1974, paratype, ♀, NMNZ CR. 1925, right antennule, ventral. Scale: 2 mm.
FIGURE 4. Munidopsis papanui n in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 4. Munidopsis papanui n. sp., holotype, ♂, NMNZ CR. 10023. A, carapace and abdomen, dorsal; B, carapace and abdomen, lateral; C, sternal plastron; D, left cheliped, dorsal; E, right cheliped, dorsal, setae omitted; F–H, right pereopods 2–4, lateral; I, dactylus of right pereopod 2, lateral; J, posterior part of abdomen with uropods and telson. Scale: 2 mm (scale 1 for A–H and J; scale 2 for I).
FIGURE 1 in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 1. Station records of the currently known species of Munidopsis (solid symbols) and new records (open symbols) around New Zealand. 250 m and 2000 m bathymetric contours are shown.
FIGURE 5. Munidopsis papanui n in New records of Munidopsis (Crustacea: Anomura: Galatheidae) from New Zealand with description of two new species from a seamount and underwater canyon
FIGURE 5. Munidopsis papanui n. sp., holotype, ♂, NMNZ CR. 10023: A, right antennule, ventral; B, right antennule, lateral; C, right antennal peduncle, ventral; D, right third maxilliped, lateral. Scale: 2 mm.
Underwater images collected by Scuba diving in La-Saline, Réunion - 2015-10-26
<i>This dataset was collected by Scuba diving in La-Saline, Réunion - 2015-10-26.</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> Underwater Images Collected by Scuba Diving in Réunion Island during the Hyscores Project. <br> For more details, visit the <a href="https://archimer.ifremer.fr/doc/00350/46122/" target="_blank">Hyscores Project</a>.<br><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> <br> 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 Scuba diving in St-Leu, Réunion - 2015-06-17
<i>This dataset was collected by Scuba diving in St-Leu, Réunion - 2015-06-17.</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> Underwater Images Collected by Scuba Diving in Réunion Island during the Hyscores Project. <br> For more details, visit the <a href="https://archimer.ifremer.fr/doc/00350/46122/" target="_blank">Hyscores Project</a>.<br><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> <br> 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.