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622 results for “Underwater”
3/9_Housing_UDG (Navy SEAL underwater revolver)
Part 3 of 9 Free download! I'll be uploading a part every day for anyone to dowload. I'll upload a paid store model as eventually. I came across this mysterious underwater revolver researching for the design of the astronaut pistol for Ad Astra. https://medium.com/war-is-boring/yep-the-navy-had-an-underwater-revolver-9d09d25f46c7 Source: Objaverse 1.0 / Sketchfab
2/9_Cylinder_UDG (Navy SEAL underwater revolver)
Part 2 of 9 Free download! I'll be uploading a part every day for anyone to dowload. I'll upload a paid store model as eventually. I came across this mysterious underwater revolver researching for the design of the astronaut pistol for Ad Astra. https://medium.com/war-is-boring/yep-the-navy-had-an-underwater-revolver-9d09d25f46c7 Source: Objaverse 1.0 / Sketchfab
Underwater Panther, c. 1500 CE
Ceramic Ugahxpa (Quapaw) Underwater Panther, c. 1500 CE, now in the collection of the Minneapolis Institute of Art. From the object's description on artsmia.org: *"The prominent colored swirls and eye motifs mark this animal as an Underwater Panther, one of the primary beings in the ancient Mississippian belief system and that of their descendants. The swirling pattern on its sides signifies water, while the eye markings allude to the animal's unusually keen vision. Red and white were symbolically significant colors that represented fundamental oppositions such as peace and war, light and dark and the on-going struggle between the celestial and subterranean realms."* More information about the model here: https://collections.artsmia.org/art/89843/vessel-ugahxpa Source: Objaverse 1.0 / Sketchfab
Supplementary material data for: Unstable environmental conditions constrain the fine-tune between opsin sensitivity and underwater light in an Amazon forest stream fish
<p>Visual adaptations can stem from variations in amino acid composition, chromophore utilization, and differential opsin gene expression levels, enabling individuals to adjust their light sensitivity to environmental lighting conditions. In stable environments, adaptations often involve amino acid substitutions, whereas in unstable conditions, differential gene expression may be a more relevant mechanism. Amazon forest streams present diverse underwater lighting conditions and experience short-term water colour fluctuations. In these environments, it is less likely for genetic and amino acid sequences to undergo modifications that tailor opsin proteins to the prevailing lighting conditions, particularly in species having several copies of the same gene. The sailfin tetra, <em>Crenuchus spilurus</em>, inhabits black and clear water Amazon forest streams. The long wavelength sensitivity (LWS) is an important component for foraging and courtship. Here, we investigated LWS opsin genes in the <em>sailfin tetra</em>. Three copies of LWS1 and two copies of LWS2 genes were found. The maximum absorbance wavelength (λmax) estimated from the amino acid sequences of LWS1 genes exhibited variation among the different copies. In contrast, the copies of LWS2 genes showed identical expected λmax values. Although the amino acid positions affecting λmax varied among LWS genes, they remained consistent among populations living in different water colours. The relative expression levels of LWS genes differed between gene copies. While not formally tested, our results suggest that in fluctuating environments, visual adaptations may primarily stem from alterations in gene expression profiles and/or chromophore usage rather than precise genetic tuning of protein light sensitivity to environmental lighting conditions.</p>
Experimental data associated with publication "Small-scale roughness entraps water and controls underwater adhesion"
<p>This folder contains:</p> <ol> <li>Experimental adhesion data and the JKR Fit parameters.</li> <li>Raw SFG data used to create Figure 2.</li> <li>Power spectral densities of the 4 rough diamond surfaces. </li> <li>Summary sheets containing results for dry and underwater adhesion measurements. </li> </ol>
The processing results, dataset and original codes of Coral reef PtCloud segmentation model (based on proposed underwater camera systems)
<p><strong># Data Introduction</strong><br>The proposed camera system includes three operation modes: Surface Mode, Horizontal Mode, and Curved Mode.</p> <ul> <li><strong>Surface Mode</strong>: Provides 3D reconstructions (point cloud), DEM, and orthophoto maps of the seabed (covering a line of 70m in length).</li> <li><strong>Horizontal Mode</strong>: Provides 3D reconstructions (point cloud and mesh) and orthophoto maps of a single transect line.</li> <li><strong>Curved Mode</strong>: Offers 3D reconstructions (mesh) of various targets, including artificial coral reefs, coral reefs with snails, and coral reefs with starfish.</li> </ul> <p>All mesh results are saved in <code>.FBX</code> format, and point cloud results are saved in <code>.TXT</code> format.</p> <p><strong># Code Introduction</strong><br>The backbone of our point cloud segmentation model is the KPConv model.<br>If you encounter any issues during code deployment, please refer to the original KPConv repository (<a href="https://github.com/HuguesTHOMAS/KPConv" target="_new" rel="noopener">Original Code: https://github.com/HuguesTHOMAS/KPConv</a>).<br>We provide our modified code (customized for our task) along with the complete point cloud dataset.</p>
Biofouling data from underwater surveys of commercial ships in Canada
<p>Ship biofouling is a major vector for the introduction and spread of harmful marine species globally. Comprehensive underwater sampling and video recording of ship hulls was conducted to assess biofouling extent (percent cover, total abundance and species richness) on a subset of ships arriving to Canadian waters. The dataset includes underwater biofouling assessments from 53 commercial ships arriving at Halifax, Nova Scotia (20 international ships), Vancouver, British Columbia (20 international ships) during 2007–2009, and Churchill, Manitoba (11 international and 2 domestic ships) in 2010–2011. Potential explanatory variables in the dataset include ship size, typical sailing speed, port residence time, age of antifouling coating system and travel history (number of biogeographic realms visisted, and average, minimum and maximum port latititude).</p>
An underwater Serengeti: Seagrass-mediated effects on intake and cultivation grazing behavior of a marine megaherbivore
<p>Populations of green turtles (<em>Chelonia mydas</em>), a megaherbivore that consumes seagrasses via cultivation grazing, are recovering worldwide. Information on plant-mediated effects on herbivore foraging behavior is critical to understanding plant-herbivore interactions and sustainability of grazing as ecosystems continue to change. In a Caribbean seagrass ecosystem, we use stationary cameras and benthic surveys to evaluate effects of seagrass morphology and leaf nitrogen content on green turtle grazing behavior. Thalassia testudinum leaf morphology has significant effects on forage intake (mg dry mass [DM] min-1) for green turtles, whereas leaf nitrogen content has no effect. Intake increases in grazed areas with shorter leaves and higher leaf biomass concentration (mg DM cm-3), indicating more efficient foraging under these conditions. Bite rate (bites min-1) increases in grazed areas with short leaves, a result of reduced search time. Bite size (mg DM bite-1) increases in grazed areas with short but dense canopies, because a turtle crops more shoots with each bite. Increased foraging efficiency and reduced search time in grazed areas with high biomass concentrations collectively maximize intake. Ingested leaves are shorter than the mean height of all available leaves in grazed areas, indicating herbivore selection for shorter leaves. Our estimate for daily intake is 86.1 g DM d-1 per 33-kg turtle. Our study provides a novel contribution on the effects of plant-level cues on the grazing behavior of a marine megaherbivore, and how cultivation grazing behavior optimizes the green turtle foraging strategy by maximizing foraging efficiency and intake.</p>
Cham ZG, Switzerland - Täubmatt Underwater Mound
[Deutsche Version siehe [hier](https://skfb.ly/6UrzO).] <br> <br> Part of the top of the artificial Täubmatt underwater mound in Lake Zug, near Cham (for a bathymetric map see [https://s.geo.admin.ch/8aee71d9ba](https://map.geo.admin.ch/?lang=de&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.swissbathy3d-reliefschattierung&E=2677288&N=1225420&zoom=10&crosshair=marker)). The wooden pile dates to the Middle Ages, the stone mound itself is still undated. Photography by Daniel Freund in December 2019, 3D modelling by Jochen Reinhard. <br> <br> **Literatur** J. Reinhard/D. Freund/A. Meier, Die "Steinberge" im Flachwasser des nördlichen Zugerseeufers. Tugium 36, 2020, 89-100. URL: [https://www.researchgate.net/publication/344777440_Steinberge_im_Flachwasser_des_nordlichen_Zugerseeufers](https://www.researchgate.net/publication/344777440_Steinberge_im_Flachwasser_des_nordlichen_Zugerseeufers). Source: Objaverse 1.0 / Sketchfab
4/9_Housing_UDG (Navy SEAL underwater revolver)
Part 4 of 9 Free download! I'll be uploading a part progressively for anyone to dowload. I'll upload complete paid store model eventually. I came across this mysterious underwater revolver researching for the design of the astronaut pistol for Ad Astra. https://medium.com/war-is-boring/yep-the-navy-had-an-underwater-revolver-9d09d25f46c7 Source: Objaverse 1.0 / Sketchfab
Video of the Ecosur Hamlet, Hypoplectrus ecosur, swimming underwater at Contoy Island, Yucatan, Mexico
<p>Video of the Ecosur Hamlet, Hypoplectrus ecosur, swimming underwater at Contoy Island, Yucatan, Mexico, filmed by Bruce Carlson.</p>
Underwater video of Hypoplectrus aff. puella in Liberte Bay, Haiti
<p>Underwater video of Hypoplectrus aff. puella in Liberte Bay, Haiti.To be described in upcoming article in JOSF.</p>
Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05
<i>This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.54 GB of MP4 files, which were trimmed into 11362 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.65% of these extracted images are useful and 0.35% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.52 %, Q2: 5.31 %, Q5: 1.17 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 2.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.137 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-27.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 28.1 GB of MP4 files, which were trimmed into 9884 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 52.75% of these extracted images are useful and 47.25% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 94.76 %, Q2: 5.02 %, Q5: 0.23 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 3.0 m and 30.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.787 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28
<i>This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 32.76 GB of MP4 files, which were trimmed into 12042 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 2.63% of these extracted images are useful and 97.37% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 93.32 %, Q2: 4.33 %, Q5: 2.35 % <br> <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://ceruleansonar.com/products/sounder-s500" target="_blank">S500</a>. <br> We only keep the values which have a GPS correction in Q1.<br> We keep the points that are the waypoints.<br> We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. <br> The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.<br> At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. <br> The size of the grid cells is 0.699 m. <br> The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Compilation of existing underwater PAM repositories, libraries, and applications for sound processing
<p>Resources for passive acoustic monitoring (PAM) are continuously expanding and being developed, yet a major challenge for users is staying up-to-date and finding the best software or application for their acoustics investigation. We expand on previous efforts (Rhinehart & Nicholson, 2022; Felgate, 2023) with the aim of providing a current, comprehensive list of 1) underwater sound repositories of raw sound data without significant processing, 2) biological sound reference libraries, with species or taxa identification, and 3) sound processing tools for visualization, annotation, or analysis. This spreadsheet contains three pages, one dedicated to each of the aforementioned items, along with some descriptive information to help users identify the best resources for their needs.</p> <p>This work was done to support the Global Library of Underwater Biological Sounds (GLUBS) project and funded in part by the Richard Lounsbery Foundation and from funding to SCOR WG #169 (GLUBS) provided by national committees of the Scientific Committee on Oceanic Research (SCOR) and from a grant to SCOR from the US National Science Foundation (OCE--2140395), with support from the International Quiet Ocean Experiment.</p> <p> </p>
Underwater video of lake whitefish Coregonus clupeaformis spawning in lake Michigan
<p>The lake whitefish <em>Coregonus clupeaformis,</em> <em>dikameg</em> in Anishinaabemowin, holds cultural importance, and is a mainstay of commercial, recreational, and subsistence fisheries throughout North America. In the Laurentian Great Lakes, declines in recruitment, since the early 2000s, have raised concerns among stewards and fishery managers. A more detailed understanding of the lake whitefish mating system could help resolve potential recruitment bottlenecks and thus inform appropriate stewardship actions. Herein, we describe, for the first time, a single lake whitefish spawning event captured using high-resolution underwater videography. From 94 h spent on the water, we captured and analyze a 4.5 min video clip that shows pre-mating, mating, and post-mating behaviour of a male and female lake whitefish from Lake Michigan. The clip shows a number of what we interpret as courtship, site-selection, and spawning behaviours culminating in release of about 20 eggs in a single spawning event. Behaviours that included travelling, physical contact, chasing, circling, orienting, and gamete release are described and time-referenced to a video supplement. This single observation is part of a larger project to assess lake whitefish spawning behaviour in the wild but is noteworthy in that it provides new insights into the spawning behaivour of lake whitefish and appears consistent with reproductive behaviours observed in European coregonines.</p>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 24.11 GB of MP4 files, which were trimmed into 8401 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 99.99% of these extracted images are useful and 0.01% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 18.95 %, Q2: 63.13 %, Q5: 17.92 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31
<i>This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2>Image acquisition</h2> This session has 30.32 GB of MP4 files, which were trimmed into 10244 frames (at 2997/1000 fps). <br> The frames are georeferenced. <br> 86.22% of these extracted images are useful and 13.78% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. <br> Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <br> <h2> GPS information: </h2> The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. <br> Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. <br> Device GPS : Emlid Reach M2 <br> Quality of our data - Q1: 24.24 %, Q2: 71.19 %, Q5: 4.57 % <br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</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.