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622 results for “Underwater”
Figs 36–38 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 36–38. Leg of the last instar larva of Ora depressa (Fabricius, 1801). 36 – prothoracic leg. 37 – tibiotarsus and pretarsus of prothoracic leg. 38 – close-up of tibiotarsal organ. Scale bars: 36 = 0.5 mm; 37 = 0.1 mm; 38 = 0.01 mm.
Figs 8–9 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 8–9.Antenna of the last instar larva of Ora species. 8 – right antenna of Ora depressa (Fabricius, 1801), dorsal aspect. 9 – antennal sensorium of Ora sp. Scale bars = 0.1 mm.
Figs 32–35 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 32–35. Thorax of the last instar larva of Ora depressa (Fabricius, 1801), dorsal aspect, showing long hair-like setae (squares with a cross), club-like setae (squares), ungrooved scale-like setae (circles with dot), and pore-like sensilla (circles). 32 – pronotum. 33 – mesonotum. 34 – metanotum. 35 – close-up of several grooved scale-like setae and one club-like seta of thorax. Scale bars: 32–34 = 0.5 mm; 35 = 0.02 mm.
Figs 25–27 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 25–27. Labium of the last instar larva of Ora depressa (Fabricius, 1801). 25 – labium, ventral aspect. 26 – close-up of the apical portion of right palpus, showing conical basiconic sensillum (Conical B), elongate basiconic sensillum (Elongate B), styloconic sensilla (S), and coeloconic sensilla (C). 27 – close-up of the rectangular area indicated in figure 25, showing the multifid setae of stiff plate (hypopharynx). Scale bars: 25 = 0.2 mm; 26 = 0.002 mm; 27 = 0.01 mm.
Figs 53–58 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 53–58. Pupa of Ora depressa (Fabricius, 1801), ventral aspect. 53 – left side of head and prothorax. 54 – close-up of the rectangular area indicated in figure 53. 55 – close-up of the rectangular area indicated in figure 54. 56 – female attachment organ. 57 – terminal portion of the abdomen of male. 58 – close-up of male attachment organ. Scale bars: 53 = 0.5 mm; 54, 56–57 = 0.2 mm; 55, 58 = 0.05 mm.
Figs 49–52 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 49–52. Pupa of Ora depressa (Fabricius, 1801), dorsal aspect. 49 – thorax. 50–51 – close-ups of the rectangular area indicated in figure 49. 52 – close-up of abdomen. Scale bars: 49 = 0.5 mm; 50–52 = 0.2 mm.
Figs 17–24 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 17–24. Maxilla of the last instar larva of Ora species. 17 – right maxilla of O. depressa (Fabricius, 1801), ventral aspect. 18 – laciniar teeth of O. depressa. 19 – close-up of the rectangular area indicated in figure 17, showing the stalk comb-bristles of the basal portion of galea. 20 – close-up of the rectangular area indicated in figure 17, showing the stalk comb-bristles of the apical portion of galea. 21 – right maxillary palpus of O. depressa, ventral aspect. 22 – right maxillary palpus of O. depressa, dorsolateral aspect. 23 – close-up of the apical portion of the maxillary palpus of O. depressa. 24 – digitiform sensillum of Ora sp. Scale bars: 17, 21–22 = 0.2 mm; 18–20, 23 = 0.02 mm; 24 = 0.01 mm.
Figs 28–31 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 28–31. Hypopharynx of the last instar larva of Ora depressa (Fabricius, 1801). 28 – hypopharynx showing terms used in larval description. 29 – close-up of anterior half of hypopharynx. 30 – close-up of keel sclerite showing six pore-like sensilla (arrows). 31 – close-up of hand-like structures and cushion area. Scale bars: 28 = 0.2 mm; 29 = 0.1 mm; 30–31 = 0.02 mm.
Figs 3–7 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 3–7. Head morphology and chaetotaxy of the last instar larva of Ora depressa (Fabricius, 1801). 3 – head capsule, dorsal aspect, showing long hair-like setae (squares with a cross), short hair-like setae (squares with dot), club-like setae (squares), ungrooved scale-like setae (circles with dot), and pore-like sensilla (circles); FR = frontale; PA = parietale. 4 – close-up of the square area indicated in figure 3. 5 – close-up of the rectangular area indicated in figure 3. 6 – club-like seta. 7 – grooved scale-like seta. Scale bars: 3 = 0.5 mm; 4 = 0.05 mm; 5–7 = 0.01 mm.
Figs 10–13 in The last instar larva and pupa of Ora depressa (Coleoptera: Scirtidae), a marsh beetle with underwater pupation
Figs 10–13. Clypeolabrum of the last instar larva of Ora depressa (Fabricius, 1801). 10 – dorsal aspect. 11 – close-up of left lobe, ventral aspect. 12 – ventral aspect. 13 – close-up of frontal margin. Scale bars: 10, 12 = 0.2 mm; 11, 13 = 0.1 mm.
Nanohydrogel Brushes for Switchable Underwater Adhesion - Dataset
<p>Abstract of the related article: In nature, living systems commonly adopt the switchable friction/adhesion mechanism during locomotion. For example, geckos can move on ceilings, relying on the reversible attachment and detachment of their feet on substrate surfaces. Inspired by this scientists have used different materials to mimic natural dynamic friction/adhesion systems. However, synthetic systems usually cannot work in water environments and are also limited to single-contact interfaces, while nature has provided living systems with complex features to perform energy dissipation and adhere on multiple contact interfaces. Here, for the first time, we report the design, synthesis, and testing of a novel double-sided synthetic construct that relies on nanohydrogel brushes to provide simultaneous friction switching on each side of the membrane that separates the nanohydrogel fibers. This highly tunable response is linked to the swelling and shrinkage of the brushes in basic/acid media. Such a system shows three different friction states, which depend on the combination of pH control of the two membrane sides. Importantly, each side of the membrane can independently provide continuous but stable friction switching from high to ultralow friction coefficients in a wet environment under high load conditions. An in-depth theoretical study is performed to explore the mechanisms governing the hydration state responsible for the observed switching. This novel design opens a promising route for the development of new solutions for intelligent devices, which can adapt to multistimulus-responsive complex environments.</p> <p>The dataset file names are given by the related figure names. Furthermore, the data file are in Origin format (.opj), ASCII data (.dat) and Mathematica (.nb).</p>
Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
<p>Many man-made marine structures (MMS) will have to be decommissioned in the coming decades. While studies on the impacts of the construction of MMS on marine mammals exist, no research has been done on the effects of their decommissioning. The complete removal of an oil and gas platform in Scotland in 2021 provided an opportunity to investigate the response of harbour porpoises to decommissioning. Arrays of broadband noise recorders and echolocation detectors were used to describe noise characteristics produced by decommissioning activities and assess porpoise behaviour. During decommissioning, sound pressure levels in the frequency range 100 Hz to 10 kHz were 30-40 dB higher than baseline, with the presence of vessels being the main source of noise. The study detected small-scale (< 2 km) and short-term levels of porpoise displacement during decommissioning, with porpoise occurrence increasing immediately after this. These findings can inform the consenting process of future decommissioning projects.</p>
Supplentary materials for: "Long-term ecosystem monitoring along the Trabocchi coast (Chieti, Italy): insights from underwater visual surveys (2011-2024)"
<p>This collection of photos and videos showcases the rich biodiversity of marine fauna and flora along the Trabocchi coast reefs in the Chieti district of Italy.</p>
Underwater Plastic dataset
<p>The dataset was generated using the <a href="https://roboflow.com/">Roboflow</a> platform. The annotations are compatible with the <a href="https://github.com/ultralytics/yolov5">PyTorch YOLOv5</a> architecture.</p> <p><em><strong>Dataset details:</strong></em></p> <p><strong># Images:</strong> 1220 images</p> <p><strong>Image Split:</strong></p> <p>Train / Test Split: 92</p> <p>Training Set: 1.1k</p> <p><strong>Preprocessing</strong></p> <p>Auto-Orient: Applied</p> <p>Resize: Stretch to 416x416</p> <p>Augmentations</p> <p>Outputs per training example: 5</p> <p>Flip: Horizontal, Vertical</p> <p>Crop: 0% Minimum Zoom, 49% Maximum Zoom</p> <p>Grayscale: Apply to 47% of images</p> <p>Hue: Between -25° and +25°</p> <p>Saturation: Between -42% and +42%</p> <p>Exposure: Between -22% and +22%</p> <p>Blur: Up to 3.25px</p> <p>Cutout: 8 boxes with 10% size each</p> <p>Mosaic: Applied</p> <p>Details</p> <p>Version Name: 2022-07-24 12:50am</p> <p>Version ID: 1</p> <p>Generated: Jul 24, 2022</p>
Underwater crayfish dataset
<p>The crayfish dataset was generated using the <a href="https://roboflow.com/">Roboflow</a> platform. The annotations are compatible with the <a href="https://github.com/ultralytics/yolov5">PyTorch YOLOv5</a> architecture.</p> <p><em><strong>Dataset details:</strong></em></p> <p><strong>Training Set:</strong> 92%, 1.2k images</p> <p><strong>Validation Set</strong>: 5%, 69 images</p> <p><strong>Testing Set</strong>: 3%, 35 images</p> <p><strong>Dimensions:</strong> 416x416</p> <p><em><strong>Augmentations:</strong></em></p> <p><strong>Flip:</strong> Horizontal</p> <p><strong>90° Rotate:</strong> Clockwise, Counter-Clockwise</p> <p><strong>Crop:</strong> 0% Minimum Zoom, 20% Maximum Zoom</p> <p><strong>Grayscale:</strong> Apply to 50% of images</p> <p><strong>Hue:</strong> Between -48° and +48°</p> <p><strong>Saturation:</strong> Between -25% and +25%</p> <p><strong>Brightness:</strong> Between -25% and +25%</p> <p><strong>Exposure:</strong> Between -25% and +25%</p> <p><strong>Blur:</strong> Up to 10px</p> <p><strong>Noise:</strong> Up to 5% of pixels</p> <p><strong>Cutout</strong>: 3 boxes with 10% size each</p> <p><strong>Mosaic:</strong> Applied</p>
Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"
<p>open access data related to the paper "<strong>Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery</strong>" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology)</p> <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> <p>To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a></p>
Vortex shedding topology for oscillating heavy spherical pendulums underwater
<p>Relevant data of vortex shedding topology for oscillating heavy spherical pendulums underwater to reproduce the major findings of the article "Dynamics of heavy subaqueous spherical pendulums" published in Journal of Fluid Mechanics. The article has been published as open access: <a href="https://doi.org/10.1017/jfm.2023.170">https://doi.org/10.1063/5.0086557</a></p>
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 2. Laternula elliptica: А – раковина вЗрослого моллюска иЗ морЯ Дейвиса, L=87 мм, вид сбоку; Б – вид с дорсальной стороны (по: Егорова [1982]); В – расположение пустых раковин Laternula elliptica в осыпаюЩемсЯ песчаном грунте на склоне подводного холма (по рисунку иЗ полевого дневника Б.И. Сиренко, ЗИН РАН); Г – наружные отверстиЯ вводного и выводного сифонов Laternula elliptica (King, 1832) на поверхности грунта. Fig. 2. Laternula elliptica: А – shell of adult mollusc from the Davis Sea, L=87 mm, lateral view; Б – dorsal view (after: Егорова [1982]); В – empty shells of Laternula elliptica in friable sand on a slope of underwater hill (after sketch in the field journal of Dr. B.I. Sirenko, Zool. Inst. RAS); Г – external openings of inhalant and exhalant siphons of Laternula elliptica on surface of bottom deposits.
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 11.28 GB of MP4 files, which were trimmed into 1912 frames (at 1 fps). <br> The frames are georeferenced. <br> 80.02% of these extracted images are useful and 19.98% 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 10.69 GB of MP4 files, which were trimmed into 1874 frames (at 1 fps). <br> The frames are not georeferenced. <br> 89.22% of these extracted images are useful and 10.78% 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> 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>
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.