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622 results for “Underwater”
FIGURE 8 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 8. Skull and left mandible of a Cuban crocodile (Crocodylus rhombifer) of Late Quaternary age from Oleg's Bat Cave, Dominican Republic (MHD 574). Skull in A. dorsal, B. ventral, C. left lateral, and D. posterior views. Left mandible in E. medial and F. lateral views.
FIGURE 6 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 6. Skull and articulated right and left mandibles of a Cuban crocodile (Crocodylus rhombifer) of Late Quaternary age from Ni-Rahu (= Cueva de Lynn), Dominican Republic (MHD 414). The skull and mandibles of this specimen are partially covered with a thick layer of calcite flowstone, obscuring many morphological features. A. Dorsal, B. ventral, C. left lateral, D. right lateral, and E. posterior views.
FIGURE 5 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 5. Underwater photographs of Late Quaternary fossils of the Cuban crocodile (Crocodylus rhombifer) from the Dominican Republic. A–E, Oleg's Bat Cave: A. Skull and articulated mandible. B. Skull of second individual. C. Dentary and postcranial elements. Note very small slender bones of bats. D. Humerus and vertebra in center of photo, surrounded by osteoderms. E. Partial crocodile skull (scale in inches). F. Complete skull and articulated mandibles, from unnamed cave near Oleg's Bat Cave. Specimen is "upside down" with mandibles on top. The skull and jaws in panel F were not collected.
FIGURE 4 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 4. Map of Hispaniola (Dominican Republic and Haiti), showing Late Quaternary fossil localities of the Cuban crocodile (Crocodylus rhombifer), as well as several cities and topographic features mentioned in the text. 1. Oleg's Bat Cave, Altagracia province; 2. Ni-Rahu (= Cueva de Lynn), Guerra province (map modified from Velazco et al., 2013).
FIGURE 3 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 3. Skull and mandible of a Cuban crocodile (Crocodylus rhombifer) from Sawmill Sink (NMB.AB50.171; C52), Abaco, Bahamas, showing the measurements taken on crocodile specimens from the Dominican Republic, Abaco, Grand Cayman, and Cuba. The measurements (letters A–N) are explained in Materials and Methods.
FIGURE 1 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 1. Skull of a Cuban crocodile (Crocodylus rhombifer) of late Holocene age from Sawmill Sink (NMB. AB50.171; C52), Abaco, Bahamas, in A. dorsal, B. ventral, C. left lateral, and D. posterior, views. The individual bones in the skull are identified using the following abbreviations: bo (basioccipital = occipital condyle), ec (ectopterygoid), ex (exoccipital), fr (frontal), ju (jugal,), la (lacrimal), mx (maxilla), na (nasal), pa (parietal), pf (prefrontal), pl (palatine), pm (premaxilla), po (postorbital), pt (pterygoid), qu (quadrate), qj (quadratojugal), so (supraoccipital), sq (squamosal).
FIGURE 2 in The Cuban Crocodile (Crocodylus rhombifer) from Late Quaternary Underwater Cave Deposits in the Dominican Republic
FIGURE 2. Mandible of a Cuban crocodile (Crocodylus rhombifer) of late Holocene age from Sawmill Sink (NMB.AB50.171; C52), Abaco, Bahamas, in A. dorsal, B. ventral, C. left lateral, and D, left medial views. The individual bones in the mandible are identified using the following abbreviations: an (angular), ar (articular), de (dentary), sp (splenial), su (surangular). The coronoid bone is missing from this specimen.
A deep learning dataset for underwater object detection of tropical freshwater fish species in northern Australia
<p>This dataset includes 44,112 images with 82,904 bounding box annotations for 23 tropical freshwater fish taxa from northern Australia. </p> <p>Images were derived from Remote Underwater Video (RUV) deployments in deep channel and shallow lowland billabongs, Kakadu National Park, Northern Territory Australia. RUV deployments were conducted during the <a href="https://www.dcceew.gov.au/science-research/supervising-scientist">Supervising Scientists</a> annual fish monitoring program in the 2016, 2017 and 2018 recessional flow period (dry season). More information can be found <a href="https://www.dcceew.gov.au/sites/default/files/documents/ss-atr-2020-21.pdf">here</a>.</p> <ul> <li>All images are in .jpg format and are 1920x1080 in dimension.</li> <li>Bounding box annotations are in COCO format. </li> </ul> <p>Two .zip files are included:</p> <ul> <li><a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-CompactModel.zip">202210-KakaduFishAI-CompactModel.zip</a>: includes compact model weights in tensorflow format (.pb) trained using Azure's Custom Vision platform. This model is suitable for edge devices due to its reduced size. Code is provided to use the compact model for inferencing. </li> <li> <a href="https://zenodo.org/api/files/990412db-e633-4f82-9b32-6990ef439ccd/202210-KakaduFishAI-TrainingData.zip">202210-KakaduFishAI-TrainingData.zip</a>: includes all images and one COCO (.json) file with annotations. </li> </ul> <p>Fish taxa include: </p> <ol> <li><em>Ambassis agrammus</em></li> <li><em>Ambassis macleayi</em></li> <li><em>Amniataba percoides</em></li> <li><em>Craterocephalus stercusmuscarum</em></li> <li><em>Denariusa bandata</em></li> <li><em>Glossamia aprion</em></li> <li><em>Glossogobius</em> spp.</li> <li><em>Hephaestus fuliginosus</em></li> <li><em>Lates calcarifer</em></li> <li><em>Leiopotherapon unicolor</em></li> <li><em>Liza ordensis</em></li> <li><em>Megalops cyprinoides</em></li> <li><em>Melanotaenia nigrans</em></li> <li><em>Melanotaenia splendida inornata</em></li> <li><em>Mogurnda mogurnda</em></li> <li><em>Nemetalosa erebi</em></li> <li><em>Neoarius</em> spp.</li> <li><em>Neosilurus</em> spp.</li> <li><em>Oxyeleotris</em> spp.</li> <li><em>Scleropages jardinii</em></li> <li><em>Strongylura kreffti</em></li> <li><em>Syncomistes butleri</em></li> <li><em>Toxotes chatareus</em></li> </ol> <p>If you use this data for your own deep learning project we'd love to hear about how you used this dataset: andrew.jansen@environment.gov.au.</p>
Supplementary Videos for "Underwater-sailing locomotion in intertidal gastropods: a comparison of Neotropical species"
<p> </p> <p>The six videos available below accompany my article, "Underwater-sailing locomotion in intertidal gastropods: a comparison of neotropical species", published in <em>Archiv für Molluskenkunde</em> <strong>151:</strong> 93-105 (2022), https://doi.org/10.1127/arch.moll/151/093-105.</p> <p> </p> <p><strong>Supplementary Video 1.</strong> <em>Bullia digitalis</em></p> <p> 0′04′′–0′18′′ Underwater-sailing locomotion</p> <p> 0′14′′ Cruising animal switches direction of movement</p> <p> 0′16′′ Two cruising animals pass each other while moving in opposite directions</p> <p> 0′19′′–0′37′′ Full-sail response in shaking-tray test; foot rotation</p> <p> </p> <p><strong>Supplementary Video 2.</strong> <em>Agaronia propatula</em></p> <p> 0′04′′–0′28′′ Underwater-sailing locomotion</p> <p> 0′29′′–0′59′′ Underwater-sailing locomotion with prey in metapodial pouch</p> <p> 1′00′′–1′12′′ Full-sail response triggered by minor vibration</p> <p> 1′13′′–1′26′′ Full-sail response triggered by flush of water</p> <p> </p> <p><strong>Supplementary Video 3.</strong> <em>Pachyoliva semistriata</em>,<em> P. columellaris</em></p> <p> 0′04′′–0′27′′ Underwater-sailing locomotion (<em>P. semistriata</em>)</p> <p> 0′28′′–0′52′′ Full-sail response triggered by shaking (<em>P. columellaris</em>)</p> <p> 0′53′′–1′04′′ Full-sail response triggered by falling (<em>P. columellaris</em>)</p> <p> </p> <p><strong>Supplementary Video 4.</strong> <em>Hastula cinerea</em>,<em> H. salleana</em></p> <p> 0′04′′–0′23′′ Underwater-sailing locomotion (<em>H. cinerea</em>)</p> <p> 0′24′′–0′52′′ Full-sail response triggered by current (<em>H. salleana</em>)</p> <p> 0′53′′–1′24′′ Full-sail response triggered by shaking (<em>H. salleana</em>)</p> <p> </p> <p><strong>Supplementary Video 5.</strong> <em>Mazatlania fulgurata</em></p> <p> 0′04′′–0′22′′ Apparent full-sail response triggered by shaking; foot rotation</p> <p> 0′23′′–0′43′′ Apparent full-sail response triggered by shaking in deep sand; foot rotation</p> <p> </p> <p><strong>Supplementary Video 6.</strong> Animals with expanded foot glide down a water column</p> <p> 0′07′′–0′33′′ <em>Agaronia propatula</em></p> <p> 0′34′′–0′56′′ <em>Pachyoliva semistriata</em></p> <p> 0′57′′–1′13′′ <em>Hastula cinerea</em></p> <p> 1′14′′–1′30′′ <em>Mazatlania fulgurata</em></p> <p> </p> <p> </p>
Temperature data of the 2022 MHW and underwater pictures of impacts on bath sponge populations
<p>July-to-August seawater temperature recorded at 18 m depth at the “3PP” station, La Ciotat, near Marseille, South of France (43.163°N; 5.599°E) using <em>in situ</em> autonomous sensors during the 2022 MHW, in the two previous years (grey dotted lines), and during the reference 2003 MHW (orange dotted line). Only one record per day is shown;</p> <p>IMG 1467: Two living sponge species, <em>Ircinia oros</em> and the bath sponge <em>Spongia officinalis</em>, the latter with necrotic spots on its surface (September 7, 2022) - Author Thierry Pérez</p> <p>IMG 1688: The same two sponges after five days (September 12, 2022), <em>S. officinalis</em> is dying, covered by a white bacterial veil. Author Thierry Pérez</p> <p> P9216598: A once rich and healthy population of <em>S. officinalis</em> at 10 m depth, September 21, 2022; Arrows point to <em>S. officinalis</em> dead skeletons left attached to the rocky wall; Author Pierre Chevaldonné</p>
Dynamic robotic tracking of underwater targets using reinforcement learning
To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new approaches and techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study is to investigate the use of deep reinforcement learning as a tool for range-only underwater target tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.
Underwater ambient sound in tropical cyclones
<p>Underwater ambient sound measurements were made in three tropical cyclones: Hurricane Gustav (2008) in the Gulf of Mexico, and Typhoons Fanapi (September 2010) and Megi (October 2010) in the western Pacific Ocean as part of the ITOP (Impact of Typhoons on the Ocean in the Pacific) program. Measurements were made from eight Lagrangian floats, each equipped with one hydrophone, air deployed ahead of these storms by WC-130J aircraft operated by the U.S. Air Force Reserve 53rd Weather Reconnaissance squadron Hurricane Hunters. Floats 50 and 51 were in Gustav, 60, 61 and 62 in Fanapi, and 66, 67 and 68 in Megi. After the storm passage, the Lagrangian floats were recovered by a research vessel. Float positions were determined by interpolating between a few GPS positions taken during the storm passage guided by time-integrated velocity measurements from Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats deployed at the same time.</p> <p>During the passage of tropical cyclones, the hydrophone switched between the work and sleep modes every 30 minutes due to limited data storage. In the work mode, the hydrophones sampled underwater ambient sound twice per second. The sound measurements thus are in 30-min segments. There are 39 (50), 38 (51), 60 (60), 56 (61), 56 (62), 55 (66), 53 (67) and 51 (68) segments (float serial numbers are in brackets), respectively, giving a total of 408 data segments and about 190 hours of sound measurements. Each raw time series has been Fourier transformed to obtain a power spectrum from 40 Hz to 50 kHz, with a spectral resolution of 40 Hz. The sound pressure level (SPL) in decibels (dB) is defined as <span class="math-tex">\(\textrm{SPL} = 20\cdot \textrm{log}(\textrm{P}/\textrm{P}_\textrm{ref}),\)</span> where <span class="math-tex">\(\textrm{P}\)</span> is the hydrophone measured sound pressure, and <span class="math-tex">\(\textrm{P}_\textrm{ref}\)</span> is the reference pressure 1 <span class="math-tex">\(\mu \textrm{Pa}^{2} \textrm{Hz}^{-2}\)</span>. The hydrophones were inter-calibrated in laboratory before and after the deployments, and agreed with a root-mean-square difference of 1–2 dB. The raw sound measurements are labeled SpdbP_raw.</p> <p>Each Lagrangian float carried a variety of instruments including a pumped CTD (conductivity, temperature and depth) sensor, a pumped GTD (gas tension device), a motor to control drogue, and another motor to control the float's buoyancy. These instruments generated noise of different temporal and spectral features. Sound measurements contaminated by noise were removed as described in <em>Zhao et al. (2014 JPO)</em>. One exception is GTD, which ran for 90% of the time for floats 50 and 51 (Gustav) and 66, 67, and 68 (Megi), and caused significant contamination on the < 5-kHz sound data. However, the > 5-kHz sound measurements are not affected by the GTD noise, and thus kept for future studies (detect rain events and breaking waves). The cleaned sound measurements are labeled SpdbP_clean.</p> <p>We decomposed the underwater ambient sound into three components according to their time scales. First, we calculate background sound, defined as the mean of the lowest 10% sound level over the 30 min period. The background sound generally rises and falls with increasing/decreasing wind speed and the presence of bubble clouds. Second, sound fluctuation is obtained by removing the background sound from the original data. Third, the sound fluctuation is divided into two components using two matched temporal filters. The second-scale fluctuation is obtained by high-pass filtering the sound fluctuation using 20-second running mean. The minute-scale fluctuation is obtained by low-pass filtering the sound fluctuation. By this method, the original underwater ambient sound is decomposed into three components: background (SpdbP_background), minute-scale fluctuation (SpdbP_MidFreq), and second-scale fluctuation (SpdbP_HighFreq). We applied the above decomposition method to all 408 30-min sound segments from eight Lagrangian floats, and created 408 figures with the same format. We share here the raw, de-noised, and decomposed sound data for all eight Lagrangian floats (eight Matlab data files) and demonstrate their decomposed sound data (eight PDF files). <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-5-Q101623.pdf?versionId=fee16fdd-adef-4419-98f7-3f05188065dd">Fig-5-Q101623.pdf </a> and <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-6-J091801.pdf?versionId=1607d29d-cb3c-4cbb-bb58-12c09b3a788c">Fig-6-J091801.pdf </a>are Figures 5 and 6 in a recent paper (<a href="https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml</a>). </p>
Dynamic robotic tracking of underwater targets using reinforcement learning
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Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
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DO data underwater glider
<p>This dataset includes profiles in-situ observation obtained by Chinese underwater glider(153 CTD vertical effective profiles, DO profiles and chlorophyll a profiles), sea surface temperature data, wind vector data, HYCOM modeling data, sea level anomaly data and typhoon best track data.</p>
Using genomics to optimise and evaluate the performance of underwater forest restoration
<p>1. Restoration is an emerging intervention to reverse the degradation and loss of marine habitat-formers and the ecosystem services they underpin. Current best practice seeks to restore populations by transplanting donor individuals chosen to replicate genetic diversity and structure of extant, nearby populations. However, genetic characteristics are rarely empirically examined across generations, despite their potential role in influencing restoration success.</p> <p>2. We used genomics to design a restoration program for lost underwater forests of Phyllospora comosa, a dominant forest-forming macroalga that went locally extinct from reefs off Sydney, Australia. Population genetic diversity and structure of nearby extant populations informed choice of donor sites. We tested whether donor provenance influenced adult transplant survival, condition (via metrics of epibiosis) and the genetic characteristics of recruits at restoration sites.</p> <p>3. Extant populations of Phyllospora within a 100 km radius of Sydney comprised three distinct genetic clusters with similar levels of genetic diversity. We transplanted reproductive adults from two of these sites, with the aim of restoring five Phyllospora forests with levels of genetic structure and diversity similar to donor populations.</p> <p>4. Donor provenance influenced survival and condition of transplanted adults and recruitment levels varied significantly among restoration sites. Yet, recruitment was rapid and genetic diversity and structure of the F1 generation resembled extant populations. This likely occurred because transplanted individuals reproduced synchronously and rapidly post-transplantation, prior to mortality of adult donor transplants.</p> <p>5. Synthesis and applications: As restoration and the need to "future-proof" marine ecosystems increase globally, it will be critical to understand the role of genetic diversity and structure in restoration success. This study demonstrates that evidence-based selection of donor macroalgal forests for restoration using genomics can result in levels of genetic diversity and structure in the F1 generation that resemble extant populations. However, further refinement of transplantation techniques to maximise recruitment is also needed.02-Jun-2020</p>
Going underwater! Multiple origins and functional morphology of piercing-sucking feeding and tracheal system adaptations in water scavenger beetle larvae (Coleoptera: Hydrophiloidea)
<p>Supplementary videos:</p> <p>Video S1. Typical feeding behavior of chewing larvae.<em> Tropisternus latus</em> Brullé, 1837 first-instar larva. Note that feeding occurs above water surface.</p> <p>Video S2. Alternative chewing feeding strategy of moluscivorous larvae. <em>Hydrophilus (Dibolocelus) palpalis </em>Brullé, 1837 second-instar larva feeding.</p> <p>Video S3. Piercing-sucking feeding behavior of <em>Hemiosus dejeanii </em>(Solier, 1849) third-instar larva. Note that feeding occurs under water surface.</p> <p>Video S4. Piercing-sucking feeding behavior of <em>Oocyclus magnifica </em>Hebauer & Wang, 1998. Note that feeding occurs inside water film.</p>
Underwater images collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30
<i>This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30.</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 0.07 GB of MP4 files, which were trimmed into 35 frames (at 2997/1000 fps). <br> The frames are not georeferenced. <br> 82.86% of these extracted images are useful and 17.14% 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> No GPS. <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 Scuba diving in Nosy-Ve, Madagascar - 2023-04-30
<i>This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30.</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 16.36 GB of MP4 files, which were trimmed into 8635 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 54.44% of these extracted images are useful and 45.56% 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 : GPX file from Garmin watch <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>
Reef underwater photogrammetry - fore reef - ilico site Reunion island. TELEMAC project
<h2>Projet pour la Fédération OMNCG – 2023</h2><h3>Projet TELEMAC: TELEdétection Multi-échelle Appliquée aux récifs Coralliens, pour estimer leur rugosité hydrodynamique et rôle protecteur du littoral</h3><h4>Coral reef photogrammetry outputs - </h4><h4>- Point cloud, 3D model, Digital Elevation Model and Orhtomosaic de la pente externe du site Ilico - L'Hermitage </h4><p>contact : Isabel Urbina Barreto : isabel.urbina-barreto@ird.fr IRD - UMR ENTROPIE ; François Guilhaumon : francois.guilhaumon@ird.fr IRD - UMR ENTROPIE ; Emmanuel Cordier : emmanuel.cordier@univ-reunion.fr, Université de La Réunion OSU-R </p><p>Observatoire de Science de l'Univers de la Réunion (OSU-R) ; Observations des Milieux Naturels et des Changements Globaux (OMNCG)</p>
ScienceDex guides
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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.