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1,246 results for “vessels”
Physiological responses of narwhals to anthropogenic noise: a case study with seismic airguns and vessel traffic in the Arctic
<p>Limited polar geographical range, narrowly defined migratory routes, and deep-diving behaviors make narwhals exceptionally vulnerable to anthropogenic disturbances including oceanic noise. Although behavioral studies indicate marked responses of cetaceans to disturbance, the link between fear reactions and possible injury from noise exposure is limited for most species.</p> <p>To address this, we deployed custom-made heart rate-accelerometer-depth recorders on 13 adult narwhals in Scoresby Sound, East Greenland across a five-year period (2014-2018). Physiological responses of the cetaceans were monitored in the absence (n = 13 animals) or presence (n = 2 animals across 3 acoustic events) of experimentally directed, seismic airgun pulses and associated vessels (full volume source level = 241 dB re 1 μPa-m).</p> <p>We found that anthropogenic noise resulted in marked cardiovascular, respiratory and locomotor reactions by two narwhals exposed to seismic pulses across three acoustic events. The general behavioral response to seismic and vessel noise included an 80% reduction in the duration of gliding during dive descents by seismic-exposed narwhals compared to controls, and the prolongation of high-intensity activity (ODBA > 0.20 g) with elevated stroke frequencies exceeding 40 strokes per minute. Noise exposure also resulted intense (< 10 bpm) bradycardia that was decoupled from stroking frequency. This decoupling instigated increased variability in heart rate, with the heart switching rapidly between bradycardia and exercise tachycardia during noise exposure. Maximum respiratory frequency following seismic exposure, 12 breaths.min<sup>-1</sup>, was 1.5 times control levels.</p> <p>Overall, the effect of seismic/ship noise exposure on wild narwhals was a 2.0 – 2.2-fold increase in the energetic cost of diving, which paradoxically occurred during suppression of the cardiac exercise response. This unusual relationship between diving heart rate and exercise intensity represents a new metric for characterizing the level of fear reactions of wild marine mammals exposed to different environmental stressors. Together, the multi-level reactions to anthropogenic noise by this deep-diving cetacean demonstrated how a cascade of effects along the entire oxygen pathway could challenge physiological homeostasis especially if disturbance is prolonged.</p>
Breccia vessel with lug handles (3300-2900 BCE)
ID: 2009.0223.000.000 A cross section of the model is also available: https://sketchfab.com/models/8996430ed746496fa7febfdfe56146e6 288 images, Hasselblad H5D60, 120mm macro lens Produced as part of a pilot project for the Object Based Learning lab in Arts West (http://arts.unimelb.edu.au/about/about-arts/arts-west) Source: Objaverse 1.0 / Sketchfab
Iron Age Pottery Vessel, Sarajevo Museum
Iron Age Pottery Vessel, Sarajevo Museum Data were collected as part of a joint research project between GDH and the Association for the Digitization and Informatisation of Cultural Heritage (DIGI.BA) in Sarajevo, in full collaboration with The Sarajevo Museum. Scanned artifacts were on display. Model was created from 539 images from a Canon 5D mk IV. All data were processed in Reality Capture. Project ID PrehistoricPottery3. Source: Objaverse 1.0 / Sketchfab
NMB&H, Iron Age metal vessel
"Early Iron Age Imported bronze vessel with annular base, ribbed belly and short neck. Height 8 cm. From the site of Čitluci on Glasinac (eastern Bosnia) Tumulus I, Grave 5 (princely grave). Late 6th c. BCE. Artifact in the National Museum of Bosnia and Herzegovina. Processed in Reality Capture from 594 images. GDH ID No. 2851 This project is a collaboration between Global Digital Heritage and the National Museum of Bosnia and Herzegovina in Sarajevo." Source: Objaverse 1.0 / Sketchfab
Vessel Lekeitio, Bizkaia, Spain
CerámicaMuseo de Arqueología de Bilbao Source: Objaverse 1.0 / Sketchfab
Moving Large Vessels, Mastaba of Kagemni
Scene of workmen moving large ceramic vessels from the 6th Dynasty mastaba tomb of the vizier Kagemni, Teti Cemetery area, Saqqara, Egypt. Created from 19 photographs (Canon EOS Rebel T7i) using Metashape 1.6.1. Photographed in January 2020. Source: Objaverse 1.0 / Sketchfab
Soft stone or steatite vessel, Kalba, Sharjah
Soft stone or steatite vessel from the 2nd millennium BCE. Excavated from the site of Kalba. Dated to 1800 BC. Sharjah, UAE. Catalog No. SIH-SF-KK-08. Kalba - Sharjah. # 285 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Predynastic Decorated Ware Vessel
A Predynastic Decorated Ware (D-ware) vessel from the late Naqada II period. In the teaching collecitons of the Department of Archaeology and Anthropology, University of Wisconsin-La Crosse. From the Curtis Collection, formally at the Michael C. Carlos Museum, Emory University. Created from 320 photographs (Canon EOS Rebel T7i) using Metashape 1.5.1 Source: Objaverse 1.0 / Sketchfab
Naqada I Ceramic Vessel with Dancing figures
Predynastic Polished Redware pottery vessel from Tomb U502, Cemetery U, Abydos Egypt discovered by the Detsches Archaologisches Institut, Cairo. Dating to the Naqada Ic period, the rim is surrounded by eight dancing female figures wearing white skirts. Photographed in the Egyptian Museum, Cairo (exc.inv. no. U-502/1: reg. no. Abydos R378). Model created using a Canon EOS Rebel T5i and Agisoft Photoscan 1.4.1 For a discussion of the vessel see: Patch, Diana Craig 2011 *Dawn of Egyptian Art*. Metropolitan Museum of Art, New York. pages 114-115. Source: Objaverse 1.0 / Sketchfab
Softstone vessel, Dibba, Sharjah, UAE.
Softstone vessel, Dibba, Sharjah, UAE. Incised decorations and flat base. 1st millenium BCE. 604 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. Catalogue No. EXS 253 D81 Source: Objaverse 1.0 / Sketchfab
Glass Vessel, Dibba Al Hisn, Sharjah, 1st C. CE
Opaque Glass Vessel, Dibba Al Hisn, Sharjah, 1st C. CE Glass was an important export product of the Mediterranean Roman world from the middle of the first century BC onwards. Large quantities reached the Oman peninsula. This specimen was discovered in a communal tomb at Dibba al-Hisn (Sharjah Emirate) on the East coast of the Oman peninsula, together with glass unguentaria, Indian ivory combs, a Roman intaglio-- luxury products that illustrate the importance of Dibba as a trading port (Jasim 2006). Sabah A. Jasim. 2006. Trade centres and commercial routes in the Arabian Gulf: Post-Hellenistic discoveries at Dibba, Sharjah, United Arab Emirates. *Arabian Archaeology and Epigraphy* 2006: 17: 214–237. Source: Objaverse 1.0 / Sketchfab
Evaluation benchmark for natural robustness evaluation of retinal vessel segmentation models
<p>A dataset contains benchmark images for natural robustness evaluation of deep learning models for retinal vessel segmentation. The dataset consists of three mainstream retinal vessel segmentation datasets: DRIVE, STARE, and CHASE_DB1.</p> <p>For each dataset are provided:</p> <ul> <li><em>images </em>- directory containing fundus images augmented using <a href="https://github.com/goranagojic/AugOOD">AugOOD</a> tool for fast image augmentation for OOD robustness evaluation.</li> <li><em>labels</em> - directory with labels that correspond to the images.</li> <li><em>masks</em> - directory with FoV masks that correspond to the images.</li> </ul> <p>The benchmark is used in the paper <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6809">Robustness of deep learning methods for ocular fundus segmentation: Evaluation of blur sensitivity</a> to evaluate natural robustness of a portfolio of deep learning models for retinal vessel segmentation from fundus images.</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2011 & 2014 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2011 & 2014 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2011 & 2014 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2009 & 2010 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2009 & 2010 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2009 & 2010 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13328931 - all 2011 & 2014 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</p>
Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels
<p>This folder contains datasets, code, and analysis scripts for reproducing the results in the paper "Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels". Specifically, it contains the following folders: </p> <ol> <li>Datasets <ol> <li>Dataset for each Vessel model in different maneuvers and conditions, i.e., waypoint, zigzag, ocean current (including IND and OOD).</li> <li>Configurations (Configurations used to train the DTM for each vessel in different maneuvers).</li> </ol> </li> <li>Results <ol> <li>Raw results for RQ1 and RQ2 for each vessel in different maneuvers, including the results from DTM-R and DTM-E.</li> <li>Results for RQ3 (statistical tests) derived from the above results. </li> </ol> </li> <li>Scripts and Code <ol> <li>Scripts used for statistical tests.</li> <li>DTC implementation (shown with one vessel as example).</li> <li>Code used for hyperparameter optimization.</li> <li>Inference class used for integration of DTM and DTC.</li> </ol> </li> </ol>
Data from: Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterised by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector, creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real time.
Negative Vessel Remodeling in Stargardt Disease Quantified with Volume-Rendered Optical Coherence Tomography Angiography
<p>Data underlying the figures in the publication “Negative Vessel Remodeling in Stargardt Disease Quantified with Volume-Rendered Optical Coherence Tomography Angiography”, published in Retina, <strong>2021</strong>. Doi: 10.1097/IAE.0000000000003110</p> <p>Table of contents:</p> <p><strong>1. Dataset 1</strong>; Excel sheet containing the source data of the publication.</p>
Broad-Scale Responses of Harbor Porpoises to Pile-Driving and Vessel Activities During Offshore Windfarm Construction
<p>Offshore windfarm developments are expanding, requiring assessment and mitigation of impacts on protected species. Typically, assessments of impacts on marine mammals have focussed on pile-driving, as intense impulsive noise elicits adverse behavioural responses. However, other construction activities such as jacket and turbine installation also change acoustic habitats through increased vessel activity. To date, the contribution of construction-related vessel activity in shaping marine mammal behavioural responses at windfarm construction sites has been overlooked and no guidelines or mitigation measures have been implemented.</p> <p>We compared broad-scale spatio-temporal variation in harbour porpoise occurrence and foraging activity between baseline periods and different construction phases at two Scottish offshore windfarms. Following a Before-After Control-Impact design, arrays of echolocation click detectors (CPODs) were deployed in 25 km by 25 km impact and reference blocks throughout the 2017-2019 construction. Echolocation clicks and buzzes were used to investigate porpoise occurrence and foraging activity respectively. In parallel, we characterised broadband noise levels using calibrated noise recorders (SoundTraps and SM2Ms) and vessel activities using AIS data integrated with engineering records. Following an impact gradient design, we then quantified the magnitude of porpoise responses in relation to changes in the acoustic environment and vessel activity.</p> <p>Compared to baseline, an 8-17% decline in porpoise occurrence was observed in the impact block during pile-driving and other construction activities. The probability of detecting porpoises and buzzing activity was positively related to the distance from vessel and construction activities, and negatively related to levels of vessel intensity and background noise. Porpoise displacement was observed at up to 12 km from pile-driving activities and up to 4 km from construction vessels. This evidence of broad-scale behavioural responses of harbour porpoises to these different construction activities highlights the importance of assessing and managing all vessel activities at offshore windfarm sites to minimise potential impacts of anthropogenic noise.</p>
Figure 3 in Rhynchocoel vessel in Cephalotrichidae (Nemertea: Palaeonemertea)
Figure 3. Schematic diagram of sagittal sections of the cephalic region of cephalotrichids, showing three types of rhynchocoel vessel morphology. (A) Type A, found in Cephalothrix adriatica, Cephalothrix hongkongiensis, Cephalothrix kefersteini, Cephalothrix oestrymnica, Cephalothrix orientalis, Cephalothrix rufifrons, Cephalothrix cf. fasciculus and Cephalothrix cf. simula; (B) Type B, found in Cephalothrix filiformis sensu Iwata (1954); and (C) Type C, found in Cephalothrix filiformis s.str.
Figure 2 in Rhynchocoel vessel in Cephalotrichidae (Nemertea: Palaeonemertea)
Figure 2. Transverse sections to show the rhynchocoel vessel (indicated by an arrowhead) in cephalotrichids. (A) Cephalothrix cf. simula (Iwata, 1952) (ZIHU 3517); (B) Cephalothrix cf. fasciculus (Iwata, 1952) (ZIHU 3515); (C) Cephalothrix filiformis sensu Iwata (1954) (ZIHU 3177); (D) Cephalothrix filiformis (Johnston, 1828) (ZIHU 3511). Scale bars: A = 100 µm; B = 30 µm; C, D = 50 µm.
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.