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Figure 4 from: Fenu G, Ferretti G, Gennai M, Lahora A, Mendoza-Fernández AJ, Mota J, Robles Sanchez J, Luis S, Schwarzer H, Sanchez-Gomez P, Viciani D, Orsenigo S (2017) Global and Regional IUCN Red List Assessments: 4. Italian Botanist 4: 61-71. https://doi.org/10.3897/ib.4.21595
Figure 4 - Geographic range and distribution map of Saxifraga montis-christi. The red points mark the current localities of occurrence; the black point indicate the historical sites.
Figure 2 from: Fenu G, Ferretti G, Gennai M, Lahora A, Mendoza-Fernández AJ, Mota J, Robles Sanchez J, Luis S, Schwarzer H, Sanchez-Gomez P, Viciani D, Orsenigo S (2017) Global and Regional IUCN Red List Assessments: 4. Italian Botanist 4: 61-71. https://doi.org/10.3897/ib.4.21595
Figure 2 - Geographic range and distribution map of Saxifraga caprariae. The red points in the map mark the current localities of occurrence, while the black one indicates an historical site.
Figure 1 in Blue and red light photoemitters as approach to inhibit Staphylococcus aureus and Pseudomonas aeruginosa growth
Figure 1. Effect of blue and red light -exposure on the growth of S. aureus e P. aeruginosa determined by the number of colonies formed after 24 h incubation. *Statistically significant difference using Mann-Whitney U test (p <0.05) for independent samples.
What makes a mimic? Orange, red, and black color production in the mimic poison frog (Ranitomeya imitator)
Open the record for dataset details and reuse information.
fig. 7 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 7. Laguna in the premises of Olho-Porá, Corrientes, collection locality of MHNG 2678.042
fig. 11 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 11. Moenkhausia australis: CI-FML 7800, female, Corrientes, arroyo Pehuajó, río Parana basin
fig. 2. Moenhausia australis, fig. 2 from plate 16 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 2. Moenhausia australis, fig. 2 from plate 16 of Eigenmann (1917)
fig. 5 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 5. Moenkhausia forestii: ZMB 23687b, city of Corrientes, río Paraná, used by Meinken (1937)
fig. 12 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 12. Moenkhausia forestii: CFA-IC 25, Corrientes, Isla del Agipé, laguna Camba Cué
fig. 10 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 10. Moenkhausia australis: CI-FML 7800, male, Corrientes, arroyo Pehuajó, río Parana basin
fig. 8 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 8. Moenkhausia australis: CFA-IC 4428, Misiones, lower arroyo Canal Torto, río Uruguay basin
fig. 6 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 6. Paraná river at Puerto Rzepecki in Corrientes, collection locality of MHNG 2676.092
fig. 4 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 4. Moenkhausia forestii: ZMB 23687a, city of Corrientes, río Paraná, used by Meinken (1937)
fig. 1. Moenhausia forestii, fig. 1 from plate 16 in Who's behind those red eyes? The Moenkhausia oligolepis group in Argentina (Characiformes: Characidae).
fig. 1. Moenhausia forestii, fig. 1 from plate 16 of Eigenmann (1917)
Dataset: Red Violet, Inc. (RDVT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Dipteran Assemblages In Red-Footed Falcon (Falco Vespertinus) Nest Boxes
Fig. 2. Phenology of emergence of Carnus hemapterus imagoes from three years old accumulated nest material (collected in 2009), grouped by the last breeding bird species (n =
Fig. 1 in Osmotic induction marking with Alizarin Red S on juveniles of pejerrey, Odontesthes bonariensis (Atherinopsidae)
Fig. 1. Photographs of untreated pejerrey analyzed structures (30 days after experimental conditions): sagitta otolith (a), scale (b), caudal fin rays (c) under light microscope. Scale bar = 200 µm.
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 2 in First Pacific record of Priolepis goldshmidtae (Gobiidae), previously regarded as a Red Sea endemic
Figure 2. – Priolepis goldshmidtae (OMNH-P 43308, 17.0 mm SL). A, B: Fresh specimen; C: Preserved specimen.
Figure 1 in First Pacific record of Priolepis goldshmidtae (Gobiidae), previously regarded as a Red Sea endemic
Figure 1. – Collection locality off Okinawa Island, southern Japan (star) of the Japanese specimen of Priolepis goldshmidtae.
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.