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346 results for “ships”
FIGURE 10 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 10. Cladaster rudis (Goniasteridae) A. In situ. B. Abactinal C. Abactinal surface with pedicellariae. D. Actinal E. Adambulacral furrow.
FIGURE 7 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 7. Bathyceramaster spp. in situ. (Goniasteridae) A. and B. Same individual. Bathyceramaster sp. 1. B shows different angle highlighting abactinal surface. Sp. 1. feeding on cladorhizid sponge, North Bermuda Tritop, 2578 m. C. Sp. 2. feeding on encrusting organism, Yakutat seamount, 2096 m.
FIGURE 12. Corallivorous Goniasteridae. A in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 12. Corallivorous Goniasteridae. A. Evoplosoma scorpio (Goniasteridae), Kurchatov Ridge, 1725 m. B. Floriaster maya (Goniasteridae), Key West Scarp, 1112 m.
FIGURE 8 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 8. Circeaster americanus (Goniasteridae) New Observations, East Atlantic. A. Kuchatov Ridge, 1756 m. B. Kuchatov Ridge, 1658 m.
FIGURE 9 in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 9. Circeaster americanus (Goniasteridae) A. Cachalote Guyote, 1462 m. B. Million Mounds (east coast of Florida), 816 m.
FIGURE 6. Bathyceramaster kelliottae n in Two New Taxa of Goniasteridae (Asteroidea, Echinodermata) and Noteworthy Observations of Deep-Sea Asteroidea by the NOAA Ship Okeanos Explorer in the North and Tropical Atlantic
FIGURE 6. Bathyceramaster kelliottae n. sp. (Goniasteridae) A. Abactinal. B. Abactinal plates, closeup. C. In situ image. D. Abactinal-superomarginal edge. E. Actinal. F. Actinal view, oral region, large bivalve pedicellariae. G. Actinal viewadambulacral plates, large bivalve pedicellariae. Scale: A, E=10.0 mm, B,F,G=2.0 mm, D=3.0 mm,
Experiment results for the paper "Uncertainty-Aware Ship Location Estimation using Multiple Cameras in Coastal Areas" to appear in MDM'2024
<p>After decompression, there are 16 folders which corresponding to the 16 multi-camera settings in the paper.</p> <p> </p> <p>Under each folder, there are two files: trajs.csv and trajsGuess.csv.</p> <p> </p> <p>1. trajs.csv contains the trajectories of ships that are located inside the monitored area of the mult-camera setting.</p> <p> The first four columns are MMSI (ship identity), timestamp, lon, and lat.</p> <p> The following columns are the corresponding pixel of the coordinate (lon, lat) in each camera, where (-1,-1) means (lon, lat) is outside the monitored area by a camera.</p> <p> A pixel is a pair of integers. </p> <p> xPos1 and yPos1 are for the 1st camera, and xPos2 and yPos2 are for the 2nd camera, and so on so forth.</p> <p> </p> <p>2. trajsGuess.csv contains the estimated ship locations by using the proposed approach in the paper.</p> <p> There are 6 columns.</p> <p> The 1st column is timestamp.</p> <p> The 2nd column is used to distinguish between the different pixel polygon intersections.</p> <p> The 3rd column and the 4th column can be either a pixel coordinate or a spatial point coordinate in lon/lat.</p> <p> The 5th column is either the cameraID of a pixel, or the order of a boundary point for a spatial polygon. The cameraID starts from 1.</p> <p> The 6th column is the type of the record, which can be</p> <p> pixel,</p> <p> or intersection1 (a polygon),</p> <p> or center1 (center of intersection1),</p> <p> or intersection2 (a polygon),</p> <p> or center2 (center of intersection2).</p> <p> Note that intersection2 and center2 appear rarely in the 6th column.</p>
Hydrography profiles from ship CTD captured during PolarFront cruise 2024-01
<p>See the Cruise Report (Daase 2024) for details.</p> <p>Daase, M. (2024). PolarFront January 2024 Cruise Report. Zenodo. <a href="https://doi.org/10.5281/zenodo.10623810" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10623810</a></p>
983_new_ship_images
<p>This dataset was created by capturing video clips of ships navigating through the Wuhan section of the Yangtze River, specifically the Zhuozhou Yangtze River Bridge, the Wuhan Yangtze River Bridge, and the Second Wuhan Yangtze River Bridge. From these video clips, one frame image was extracted every 50 frames, resulting in a total of 983 ship images after removing highly similar images. These images were added to the existing Seaships dataset, resulting in an expanded dataset called Seaships_enlarge.</p>
Data from: Travel with your kin ship! Insights from genetic sibship among settlers of a coral damselfish
<p>Coral reef fish larvae are tiny, exceedingly numerous, and hard to track. They are also highly capable, equipped with swimming and sensory abilities that may influence their dispersal trajectories. Despite the importance of larval input to the dynamics of a population, we remain reliant on indirect insights to the processes influencing larval behavior and transport. Here, we used genetic data (300 independent single nucleotide polymorphisms) derived from a light trap sample of a single recruitment event of Dascyllus abudafur in the Red Sea (N = 168 settlers). We analyzed the genetic composition of the larvae and assessed whether kinship among these was significantly different from random as evidence for cohesive dispersal during the larval phase. We used Monte Carlo simulations of similar‐sized recruitment cohorts to compare the expected kinship composition relative to our empirical data. The high number of siblings within the empirical cohort strongly suggests cohesive dispersal among larvae. This work highlights the utility of kinship analysis as a means of inferring dynamics during the pelagic larval phase.</p>
SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations
<p><strong>SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations</strong></p> <p>Availability of domain-specific datasets is a challenge for object detection. In particular, conducting onsite experiments is risky and time-consuming for the maritime domain. It is not always possible to capture challenging situations and incorporate variations in the environment. Therefore, we present a simulation-based dataset for maritime environments consisting of 9471 high-resolution (1920x1080) images resembling the real world with precise annotations.</p> <p>The dataset was acquired using a simulation tool, AILiveSim [17]. AILiveSim is a 3D simulation platform for targeted scalable development and integration of autonomous systems through digital twins. The tool provides a realistic 3D model of the route of the watercraft extended from the city of Turku to Ruisalo in South-West Finland.</p> <p>Our dataset incorporates diversity of objects, weather, illumination, visible proportion and scale in images that resemble the real world. </p> <p>Please cite the following publication when using the dataset:</p> <p>M. Raza, H. Prokopova, S. Huseynzade, S. Azimi and S. Lafond, "SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations," <em>OCEANS 2022, Hampton Roads</em>, Hampton Roads, VA, USA, 2022, pp. 1-5, doi: 10.1109/OCEANS47191.2022.9977182.</p> <p>The publication is available at: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977182">https://doi.org/10.1109/OCEANS47191.2022.9977182</a></p> <p>A preprint version of the publication is available at <a href="https://arxiv.org/abs/2211.05237">https://arxiv.org/abs/2211.05237</a></p> <p> </p> <p> </p>
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Maritime Transportation - Deep-Sea Shipping Instances
<p>Each instance has a filename "deep_n_i.dat", in which deep indicates that the trips are international (deep sea shipping), n is the number of ports (including the depot), and i is the index of the instance in the same group.</p> <p>Line 1 - [# of customer, vehicle/vessel capacity, speed lower limit, speed upper limit]<br> Line 2 - demand at each port<br> Line 3 - lower time window bound for each port<br> Line 4 - upper time window bound for each port<br> Line 5 - service time at each port<br> From line 6 - distance matrix</p>
Geobukseon (Turtle Ship)
One of my first models. I hope you like it Source: Objaverse 1.0 / Sketchfab
15th C. Ship Graffiti, Calatrava la Vieja, Esp
Medieval graffiti from Calatrava la Vieja, Castilla-La Mancha, Spain. Late medieval Christian period (3rd quarter 15th century CE). Located in the crypt of the Santa María La Blanca Church, inside the alcazar. The main motif is a "coca" from the later 15th C, a merchant ship etched with precision and detail, probably by a sailor who knew this type of ship well, and with this act requested the protection of the virgin, as a votive graffiti. It is an interesting drawing because it depicts the complete rigging, including the crow´s nests, the bow castle, the stern fortress, and the "codaste" rudder-an element of great importance in the history of navigation, given that its use after 1425 allowed navigation on the high seas, and gave way to the Age of Discovery. GDH Graffiti Panel 1. Hervás Herrera, Miguel Ángel (2016): Calatrava la Vieja. Conservación y Restauración (1975-2010), Universidad de Castilla-La Mancha, Repositorio Digital RUIDERA, Albacete, 2016, 958 págs. URI: http://hdl.handle.net/10578/8711. Source: Objaverse 1.0 / Sketchfab
Ships
Source: Objaverse 1.0 / Sketchfab
Tollesbury Light Ship Model by Mike Olley
Source: Objaverse 1.0 / Sketchfab
Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images
<p>Dataset for Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images from https://github.com/WindVChen/Push-and-Pull-Network?tab=readme-ov-file</p>
Modeling fluid flow in ship systems for controller tuning using an artificial neural network
<p>Dataset used to develop ANN NARX models</p>
Data supplementing article "Estuarine circulation in a shallow but stratified estuary: Different responses to river discharge between deep ship channel and shoals" submitted to Journal of Geophysical Research: Oceans
<p>The dataset uploaded includes the measured salinity and velocity at two monitoring stations (one at the lower Mobile Bay and the other at the eastern edge of ship channel in middle Mobile Bay) and from multiple ship cruises crossing the lower, middle, and upper Mobile Bay. </p> <p>Detail information on the measurement frequency, date, and location can be found in the mat files. Records with bad quality are filled with NaN values. </p> <p> </p> <p> </p>
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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.