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955 results for “Ocean data”
Data and scripts (1) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p><br> ================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Mobile Ocean Bottom Seismometer Instrument (MOBSI) data from Arctic rivers, lakes, and seas
<p>We collected the data using the Mobile Ocean Bottom Seismic Instrument (MOBSI). The MOBSI is portable (9 kg) and consists of a watertight, heavy-duty pressure housing (diameter 30 cm, height 35 cm) containing a three-component, broadband seismic sensor. There are three channels (two horizontal and one vertical) recorded by the built-in data logger to monitor ambient seismic noise. Deployed by a steel cable from a small boat or zodiac, the device was lowered to the bottom of the water bodies or the top of the beach area (if present). The device recorded ambient seismic noise for a few minutes before being retrieved and moved to a new location. The steel cable was outfitted with a communications cable, which allowed for live data analysis and quality control via a shipboard monitor. This ensured that the tilt of the instrument was correct (less than 5 degrees) and that the deployment time was sufficient. We stopped data collection when the "real-time" H/V ratio was stable, i.e., not changing with time. For all soundings, the sampling frequency was set to 100 samples per second. The MOBSI data was recorded in an internal RAW format that was converted to MINISeed format. In this paper, we present MOBSI data for seven survey lines from the Russian and the Canadian Arctic. The Banja Lake and Lena River profiles are situated in the Lena Delta, northeastern Siberia, Russia. Ivashkina Lagoon is located along the southern coastline of the Bykovsky Peninsula, northeastern Siberia, Russia. The Tuktoyaktuk profiles are located north of Tuktoyaktuk Island (Northwest Territories, Canada) in the Canadian Beaufort Sea. The survey lines presented in this manuscript are the following:</p> <p>Profile A-A' (Banja Lake): 8 soundings</p> <p>Profile B-B' (Banja Lake): 9 soundings</p> <p>Profile C-C' (Banja Lake): 9 soundings</p> <p>Profile D-D' (Lena River-Samoylov Island): 9 soundings</p> <p>Profile E-E' (Lena River-Chay-Tumus): 16 soundings</p> <p>Profile F-F' (Tuktoyaktuk Island): 10 soundings</p> <p>Profile G-G' (Tuktoyaktuk Island): 9 soundings</p> <p>Profile H-H' (Ivashkina Lagoon): 20 soundings</p> <p>The Excel file "MOBSI_soundings.xlsx" provides an overview of all the soundings, whereby each tab is split by the survey line. The latitude, longitude, water depth, and recording time for each sounding is provided.</p>
Data and code supplementing article "Impact of estuarine exchange flow on multiple tracer budgets in the Salish Sea" submitted to the Journal of Geophysical Research: Oceans
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Data for Simulation of Particulate Organic Carbon transport in the Pearl River Estuarine-Coastal Ocean, South China Sea following Typhoon Hato and Pakhar (2017)
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TABLE 1 in Small island but great diversity: thirty six species of Parmotrema (Parmeliaceae, lichenized Ascomycota), including sixteen new species, on Réunion (Mascarenes), with additional data from the Western Indian Ocean
<p><b>TABLE 1.</b> List of the 36 species of the genus <i>Parmotrema</i> reported from Réunion Island with, for each species, its Mycobank number, the availability of an ITS barcode, and a summary of its currently known distribution.</p><table><tbody><tr><th></th><th>MycoBank no.</th><th>ITS barcode</th><th><b>Distribution as currently known</b></th></tr></tbody><tbody><tr><th><i>P. appendiculatum</i> (Fée) Hale</th><td>343010</td><td>No</td><td>Réunion</td></tr><tr><th><i>P. aurantioreagens</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853865</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. austrosinense</i> (Zahlbr.) Hale</th><td>343014</td><td>Yes</td><td>Pantropical, extending into temperate areas, incl. Madagascar, Mauritius and Réunion</td></tr><tr><th><i>P. brachyblepharum</i> D.M. Masson, Magain & Sérus., <i>sp. nov</i></th><td>853866</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. cetratum</i> (Ach.) Hale</th><td>343018</td><td>No</td><td>Pantropical, extending into temperate areas, incl. Madagascar and Réunion</td></tr><tr><th><i>P.</i> cf. <i>clavuliferum</i> (Räsänen) Streimann</th><td>129346</td><td>Yes</td><td>Maybe pantropical and pantemperate, incl. Madagascar and Réunion</td></tr><tr><th><i>P. cooperi</i> (J. Steiner & Zahlbr.) Sérus.</th><td>107091</td><td>No</td><td>Pantropical, incl. Madagascar and Réunion</td></tr><tr><th><i>P. crinitum</i> (Ach.) M. Choisy</th><td>368891</td><td>Yes</td><td>Widespread in temperate and tropical areas, incl. Madagascar, Mauritius and Réunion</td></tr><tr><th><i>P. cristiferum</i> (Taylor) Hale</th><td>343031</td><td>Yes</td><td>Pantropical, extending into temperate areas, incl. Comoros, Madagascar, Mauritius, Réunion and Seychelles</td></tr><tr><th><i>P. crossotum</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853867</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P.</i> cf. <i>deflectens</i> (Kurok.) Streimann</th><td>129347</td><td>No</td><td>Paleotropical, incl. Madagascar and Réunion</td></tr><tr><th><i>P. dilatatum</i> (Vainio) Hale</th><td>343038</td><td>Yes</td><td>Pantropical, incl. Mauritius and Réunion</td></tr><tr><th><i>P. eleonomum</i> D.M. Masson, Magain & Sérus., <i>sp. nov.</i></th><td>853868</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. intonsum</i> D.M. Masson, Magain & Sérus., <i>sp. nov.</i></th><td>853869</td><td>Yes</td><td>Madagascar and Réunion</td></tr><tr><th><i>P. mascarenense</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853870</td><td>Yes</td><td>Mauritius and Réunion</td></tr><tr><th><i>P. meiospermum</i> (Hue) D.M. Masson & Sérus. <i>comb. nov.</i></th><td>853882</td><td>Yes</td><td>Mauritius and Réunion</td></tr><tr><th><i>P. mellissii</i> (C.W. Dodge) Hale</th><td>343083</td><td>Yes</td><td>Pantropical, extending into temperate areas, incl. Madagascar and Réunion</td></tr><tr><th><i>P. mezierii</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853871</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. mirum</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853872</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P.</i> cf. <i>negrosorientale</i> Elix & Schumm</th><td>546186</td><td>Yes</td><td>Philippines, Mauritius and Réunion</td></tr><tr><th><i>P. nemorum</i> D.M. Masson, Magain & Sérus., <i>sp. nov.</i></th><td>853873</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. nephophilum</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853874</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. occultum</i> D.M. Masson & Sérus., <i>sp. nov.</i></th><td>853875</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. odontatum</i> (Hue) D.M. Masson & Sérus., <i>comb. nov.</i></th><td>853883</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. orarium</i> D.M. Masson, Magain & Sérus., <i>sp. nov.</i></th><td>853876</td><td>Yes</td><td>Réunion</td></tr><tr><th><i>P. paramascarenense</i> D.M. Masson, <i>sp. nov.</i></th><td>853877</td><td>No</td><td>Madagascar and Réunion</td></tr><tr><th><i>P. praesorediosum</i> (Nyl.) Hale</th><td>343106</td><td>No</td><td>Pantropical, extending into temperate areas, incl. Madagascar, Mauritius and Réunion</td></tr></tbody></table><p>......continued on the next page</p>
Data for "Dynamic changes of Southern Hemisphere Westerlies tie to thermal conditions of Southern Ocean"
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Data and MATLAB code for the publication entitled "Intensification of Submesoscale Frontogenesis and Forward Energy Cascade Driven by Upper-Ocean Convergent Flows"
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Planktonic foraminifera-bound d15N data for "Ocean iron fertilization by sea-level enhanced mid-ocean ridge volcanism "
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Data from: Climate impacts on the ocean are making the Sustainable Development Goals a moving target traveling away from us
1. Climate change is impacting marine ecosystems and their goods and services in diverse ways, which can directly hinder our ability to achieve the Sustainable Development Goals, set out under the 2030 Agenda for Sustainable Development. 2. Through expert elicitation and a literature review, we find that most climate change effects have a wide variety of negative consequences across marine ecosystem services, though most studies have highlighted impacts from warming and consequences to marine species. 3. Climate change is expected to negatively influence marine ecosystem services through global stressors – such as ocean warming and acidification – but also by amplifying local and regional stressors such as freshwater runoff and pollution load. 4. Experts indicated that all Sustainable Development Goals would be overwhelmingly negatively affected by these climate impacts to marine ecosystem services, with eliminating hunger being among the most directly negatively affected Sustainable Development Goal. 5. Despite these challenges, the Sustainable Development Goals aiming to transform our consumption and production practices and develop clean energy systems are found to be least affected by marine climate impacts. These findings represent a strategic point of entry for countries to achieve sustainable development, given that these two goals are relatively robust to climate impacts and that they are important pre-requisite for other Sustainable Development Goals. 6. Our results suggest that climate change impacts on marine ecosystems are set to make the Sustainable Development Goals a moving target traveling away from us. Effective and urgent action towards sustainable development, including mitigating and adapting to climate impacts on marine systems are important to achieve the Sustainable Development Goals, but the longer this action stalls the more distant these goals will become.
Ocean eddy kinetic energy at 1000 m depth estimated from Argo drift data from July 1997 to December 2015
<p><strong>Eddy kinetic energy at 1000 dbar depth estimated from Argo drift data from July 1997 to December 2015</strong><br><br>These are the data used to draw Fig.1 of Katsumata (2017b).<br>Argo drift data from YoMaHa (Lebedev et al. 2017) is averaged<br>in a quasi-circle with a radius of 300 km, deformed to follow<br>bathymetry, and deviation from the average is our eddy here.<br><br>Detail of the method is found in Katsumata (2017a).<br><br><a href="https://doi.org/10.1175/JPO-D-16-0150.1">Katsumata (2017a)</a><br><a href="https://doi.org/10.1175/JPO-D-16-0150.1">Katsumata (2017b)</a><br>Lebedev, K., H. Yoshinari, N. A. Maximenko, and P. W. Hacker,<br> (2007) YoMaHa’07: Velocity data assessed from trajectories of<br> Argo floats at parking level and at the sea surface. IPRC Tech.<br> Note 4, 16 pp.<br> <a href="https://doi.org/10.1175/JPO-D-16-0150.1">http://apdrc.soest.hawaii.edu/projects/yomaha/</a></p> <p> </p>
Data supplement to 'Astrochronology for the Miocene Climatic Optimum at Ocean Drilling Program Site 959 in the eastern Equatorial Atlantic'
<p>Data supplement to "Astrochronology for the Miocene Climatic Optimum at Ocean Drilling Program Site 959 in the eastern Equatorial Atlantic". </p> <p><strong>Table </strong><strong>T1</strong>: Depth conversion to rmbsf.</p> <p><strong>Table T2</strong>: List of all Early to Middle Miocene calcareous nannofossil and diatom bioevents at ODP Site 959.</p> <p><strong>Table T3</strong>: Diagnostic diatoms range chart</p> <p><strong>Table T4</strong>: Diagnostic calcareous nannofossils range chart</p> <p><strong>Table T5</strong>: Magnetic susceptibility data</p> <p><strong>Table T6</strong>: Bulk carbonate δ<sup>1</sup><sup>8</sup>O and δ<sup>13</sup>C data</p> <p><strong>Table T7</strong>: Wt% CaCO<sub>3 </sub>data</p> <p><strong>Table T8</strong>: DeCrack mean greyscale, redness, greenness and blueness data</p>
Data for Ocean warming drives rapid dynamic activation of marine-terminating glacier on the west Antarctic Peninsula
<p>Data made available to support "Ocean warming drives rapid dynamic activation of marine-terminating glacier on the west Antarctic Peninsula" Wallis et al. 2023 (accepted). For details, see publication.</p><p>Data included:</p><p>Altimetry data for Cadman, Funk and Lever Glaciers.</p><p>Bathymetric data for Beascochia Bay.</p><p>Calving fronts 1984-2022 for Cadman Glacier.</p><p>Co-registered DEMs.</p><p>All geometric definitions.</p><p>Ice velocity time-series for Cadman, Funk and Lever Glaciers.</p><p>Ice discharge time-series for Cadman Glacier.</p><p>Ocean reanalysis data, oceanographic station data.</p><p>Ice velocity Kalman filter code.</p>
Data from: Atypical panmixia in a European dolphin species (Delphinus delphis); implications for the evolution of diversity across oceanic boundaries.
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Data from: Resistance of corals and coralline algae to ocean acidification: physiological control of calcification under natural pH variability
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Data from: Ocean acidification boosts larval fish development but reduces the window of opportunity for successful settlement
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Data from: European sea bass show behavioural resilience to near-future ocean acidification
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Data from: Evolutionary responses of a coccolithophorid Gephyrocapsa oceanica to ocean acidification
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Data from: Nutrient starvation impairs the trophic plasticity of reef-building corals under ocean warming
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Data from: Reverse diel vertical movements of oceanic manta rays off the northern coast of Peru and implications for conservation
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Data from: Phylogenetic relationships and convergent evolution of ocean-shore ground beetles (Coleoptera: Carabidae: Trechinae: Bembidion and relatives)
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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.