Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12
datasets available to search
ShareScore release 0.9.0
Dataset results
12 results for “Eilat”
Figure 6 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 6 - Sinularia lamellata RMNH Coel no. 12864. Sclerites of the polypary. A clubs of surface layer B–C point sclerites D spindles from surface E spindles from interior F tuberculation. Scale bars: 0.10 mm (A–D, F), 1 mm (E).
Figure 2 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 2 - Sinularia mesophotica sp. n., holotype ZMTAU Co 37425. Sclerites from the polypary. A tentacle rods B straight collaret spindles C bent collaret spindles D clubs E larger clubs. Scale bar: 0.10 mm.
Figure 3 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 3 - Sinularia mesophotica sp. n., holotype ZMTAU Co 37425. Sclerites of the base of colony. A clubs of the surface layer B spindles of interior C tuberculation of the spindles. Scale bars: 0.10 mm (A, C), 1 mm (B).
Figure 3 from: Benayahu Y, McFadden CS, Shoham E (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny: I. A new sclerite-free genus from Eilat, northern Red Sea. ZooKeys 680: 1-11. https://doi.org/10.3897/zookeys.680.12727
Figure 3 - Altumia delicata gen. n. sp. n. live colonies. A, B colonies growing over branch of black coral with expanded polyps C colonies growing on PVC net (arrow heads).
Figure 5 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 5 - Maximum likelihood tree of concatenated mtMutS and igr1+COI mitochondrial gene sequences. Clade numbering system follows McFadden et al. (2009); some clades have been collapsed to facilitate readability. Asterisks indicate species with a funnel-shaped morphology similar to that of Sinularia mesophotica sp. n. Bootstrap values >50% are indicated adjacent to nodes.
Figure 1 from: Benayahu Y, McFadden CS, Shoham E (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny: I. A new sclerite-free genus from Eilat, northern Red Sea. ZooKeys 680: 1-11. https://doi.org/10.3897/zookeys.680.12727
Figure 1 - Phylogenetic relationships among species of octocorals that lack sclerites (red asterisks) and members of family Clavulariidae (blue labels), including Altumia delicata gen n. sp. n. (red label). Solid circles at nodes indicate strong support from both maximum-likelihood (bootstrap value >70%) and Bayesian (posterior probability >0.90) analyses; split circles indicate strong support from one analysis only (left half solid: supported by ML; right half solid: supported by Bayesian analysis). Strongly supported clades that include no clavulariid or sclerite-free species have been collapsed. Hexacorallian outgroup taxa used to root tree are not shown. For a comprehensive list of taxa and sequences included in the analyses see McFadden and Ofwegen (2012).
Figure 2 from: Benayahu Y, McFadden CS, Shoham E (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny: I. A new sclerite-free genus from Eilat, northern Red Sea. ZooKeys 680: 1-11. https://doi.org/10.3897/zookeys.680.12727
Figure 2 - Altumia delicata gen. n. sp. n. holotype ZMTAU CO 37427. A Colony growing over a branch of a black coral B close up of holotype. Scale 10 mm at A, 1 mm at B.
Figure 7 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 7 - Sinularia lamellata RMNH Coel no. 12864. Sclerites of the colony base; A clubs of surface layer B–C spindles from interior base D tuberculation of spindles. Scale bars: 0.10 mm (A, D), 1 mm (B, C).
Figure 1 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 1 - Sinularia mesophotica sp. n.; A Holotype ZMTAU Co 37425 B paratypes ZMTAU Co 37492. Scale bar: 1 cm (A also applies to B).
Figure 4 from: Benayahu Y, McFadden CS, Shoham E, van Ofwegen LP (2017) Search for mesophotic octocorals (Cnidaria, Anthozoa) and their phylogeny. II. A new zooxanthellate species from Eilat, northern Red Sea. ZooKeys 676: 1-12. https://doi.org/10.3897/zookeys.676.12751
Figure 4 - Underwater photographs of Sinularia mesophotica sp. n. A patch of colonies B funnel-shaped morphology of colonies.
ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat
<p><strong>Description:</strong></p> <p>This dataset consists of approximately 46,928 synchronized image pairs collected by the Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ) and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap.</p> <p>The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.</p> <p> </p> <p><strong>Context:</strong></p> <p>This dataset is the second installment in our collection of synchronized multi-sensor underwater datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea, off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a research project focused on developing fusion models for improved obstacle detection and navigation in AUVs.</p> <p> </p> <p><strong>Content:</strong></p> <p>The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths ranging from 3 to 12 meters at different times from dawn to dusk.</p> <p><strong> </strong></p> <p><strong>Dataset Composition:</strong><strong><br></strong></p> <div> <table> <tbody> <tr> <td> <p>Site</p> </td> <td> <p>Recording Session</p> </td> <td> <p>Image Pairs</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Tropical Site 1</p> </td> <td> <p>20221211_092506</p> <p>20221211_133252</p> </td> <td> <p>10,915</p> <p>7,978</p> </td> <td> <p>Pier, rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 2</p> </td> <td> <p>20221212_095821</p> <p>20221212_141308</p> </td> <td> <p>9,900</p> <p>8,475</p> </td> <td> <p>Man-made structure, </p> <p>rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 3</p> </td> <td> <p>20221213_102542</p> </td> <td> <p>9,390</p> </td> <td> <p>Rocks, corals</p> </td> </tr> <tr> <td> <p>Total</p> </td> <td> </td> <td> <p>46,928 </p> </td> <td> </td> </tr> </tbody> </table> </div> <p><strong> </strong></p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, data is further categorized into modalities: camera (FLC images), sonar (FLS images), and depth. Each modality directory contains the corresponding data files in PNG format for images and CSV format for depth data.</p> <p><strong> </strong></p> <p>Each modality directory includes:</p> <ul> <li> <p>A `camera.csv` file for the camera modality that maps each image file to its respective timestamp.</p> </li> <li> <p>A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp.</p> </li> <li> <p>The depth data in `depth.csv` formatted with `timestamp` and `value`.</p> </li> </ul> <p>Additionally, a `samples.json` file documents the relationship between uni-modal and multi-modal samples, enabling easy association of data from different modalities.</p> <p><strong> </strong></p> <p><strong>Technical Details:</strong></p> <ul> <li> <p>Camera: IDS UI-3260CP-C-HQ</p> </li> <ul> <li> <p>Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset)</p> </li> <li> <p>Sensor type: Sony IMX249 1/1.2" CMOS</p> </li> <li> <p>Lens: Tamron M112FM06</p> </li> <li> <p>Captured bit depth: 8-bit</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Sonar: BluePrint Oculus M1200d</p> </li> <ul> <li> <p>Operating frequency: 1.2 MHz (low frequency mode)</p> </li> <li> <p>Maximum range: 40 m (set to 15 m for this dataset)</p> </li> <li> <p>Horizontal aperture: 130°</p> </li> <li> <p>Vertical aperture: 20°</p> </li> <li> <p>Number of beams: 512</p> </li> <li> <p>Angular resolution: 0.6°</p> </li> <li> <p>Beam separation: 0.25°</p> </li> <li> <p>Image resolution: 544x300 pixels</p> </li> <li> <p>Coordinate system: Polar</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder</p> </li> <ul> <li> <p>Frequency: 115 kHz</p> </li> <li> <p>Source Level: 198 dB re 1µPa @ 1m</p> </li> <li> <p>Beamwidth: 25 degrees</p> </li> <li> <p>Typical Minimum Range: 0.3 m (1 ft)</p> </li> <li> <p>Typical Usable Range: 100 m (328 ft)</p> </li> <li> <p>Range Resolution: 0.5% of range</p> </li> <li> <p>Depth Rating: 300 m (984 ft)</p> </li> <li> <p>Data format: CSV</p> </li> <li> <p>Columns:</p> </li> <ul> <li> <p>timestamp: Unix timestamp (seconds)</p> </li> <li> <p>value: Depth value (meters)</p> </li> </ul> <li> <p>Sample rate: 5 Hz</p> </li> </ul> </ul> <p><strong> </strong></p> <p><strong>Example File Tree Layout:</strong></p> <p>```<br>${session}/<br>${dataset}/<br>camera/<br>camera.csv<br>00000001.png<br>00000002.png<br>…<br>sonar/<br>sonar.csv<br>00000001.png<br>00000002.png<br>…<br>depth/<br>depth.csv<br>samples.json<br>```<strong> <br><br>Example File Content:</strong></p> <p><strong> </strong>camera.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>sonar.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>depth.csv<br>```<br>timestamp,value<br>1644234340.181234,5.4<br>1644234343.375667,6.1<br>```</p> <p><strong> </strong>samples.json</p> <p>```<br>{<br> "samples": [<br> {<br> "camera": [<br> 0<br> ],<br> "depth": [<br> 0<br> ],<br> "sonar": [<br> 0<br> ]<br> },<br> {<br> "camera": [<br> 1<br> ],<br> "depth": [<br> 1<br> ],<br> "sonar": [<br> 1<br> ]<br> }<br>]</p> <p>```</p> <p>By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Figure 1 in Spring Bird Migration Phenology in Eilat, Israel
Figure 1. Data of first capture of 34 species of birds in Eilat in 1984–2003.
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.