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816 results for “deep ocean”
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Effect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output data
<ul> <li>This dataset contains the output of model simulations used in the paper:<br> Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", Earth System Dynamics Discussions, https://doi.org/10.5194/esd-11-925-2020, 2020 </li> <li>All figures can be reproduced when combining this dataset with the published analysis code. <br> </li> <li>In addition this dataset contains the data behind Fig. 2 of the paper:<br> Gebbie, G. and Huybers, P. : "The Little Ice Age and 20th-century deep Pacific cooling", Science, 363, 70-74, https://doi.org/10.1126/science.aar8413, 2019<br> </li> <li>Download either the small (unzipped 5 Gb) or large (unzipped 22 Gb) version of the dataset. <strong>Warning: this needs to be loaded into memory when running the notebook.</strong> You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.</li> </ul> <p>Overview of doi's:</p> <ul> <li>paper: <a href="https://doi.org/10.5194/esd-11-925-2020">https://doi.org/10.5194/esd-11-925-2020</a></li> <li>code (analysis and figures): <a href="https://doi.org/10.5281/zenodo.4022947">https://doi.org/10.5281/zenodo.4022947</a></li> <li>data (simulation output): <a href="https://doi.org/10.5281/zenodo.4022927">https://doi.org/10.5281/zenodo.4022927</a></li> </ul>
FIG. 4 in First record of the rare Wide-mouth flounder Kamoharaia megastoma (Kamohara, 1936) (Pleuronectiformes, Bothidae) from the western Indian Ocean collected during the ATIMO VATAE expedition to Madagascar "Deep South"
FIG. 4. — Kamoharaia megastoma (Kamohara, 1936), SAIAB 189603, 108.7 mm SL, Madagascar: A, lateral line scale ocular side; B, body scale ocular side; C, body scale blind side. Scale bars: 200 µm.
FIG. 3 in First record of the rare Wide-mouth flounder Kamoharaia megastoma (Kamohara, 1936) (Pleuronectiformes, Bothidae) from the western Indian Ocean collected during the ATIMO VATAE expedition to Madagascar "Deep South"
FIG. 3. — Kamoharaia megastoma (Kamohara,1936), SAIAB 189603, 108.7 mm SL, Madagascar: A, close-up of ocular side of head; B, close-up of blind side of head; C, close-up of ocular side of mouth. Scale bars: A, B, 20 mm; C, not to scale.
FIG. 2 in First record of the rare Wide-mouth flounder Kamoharaia megastoma (Kamohara, 1936) (Pleuronectiformes, Bothidae) from the western Indian Ocean collected during the ATIMO VATAE expedition to Madagascar "Deep South"
FIG. 2. — Kamoharaia megastoma (Kamohara, 1936), SAIAB 189603, 108.7 mm SL, Sud-Ouest Pointe Barrow, Madagascar: A, ocular side fresh specimen (photograph by S. Ribes); B, ocular side preserved specimen; C, blind side preserved specimen. Scale bar: 20 mm.
Fig. 18 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 18. Kenyanema monorchis Muthumbi et al., 1997. A. Anterior region of ♀; B: lateral surface view of Ƌ head. C. Lateral view of Ƌ head (cross-section). D. Posterior region of Ƌ showing copulatory apparatus. E. ♀ gut, posterior body region. F. ♀ tail. Scale bar = 25 μm.
Fig. 17 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 17. Hopperia beaglense Chen and Vincx, 1998 light micrographs. A. Anterior region of Ƌ. B. Ƌ gut, showing clusters of small round inclusions with smaller orange-brown granules. C. Spinneret. Scale bar: A = 30 μm; B = 12 μm; C = 24 μm.
Fig. 15 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 15. Hopperia beaglense Chen and Vincx, 1998. A. Anterior region of Ƌ. B-C. Lateral view of Ƌ head. D. Lateral surface view of ♀ head. E. ♀ tail. F. Ƌ copulatory apparatus and vas deferens. Arrow shows approximate position of supplement-like structure observed in type specimens from Chile. Scale bar: A = 30 μm; B-D = 22 μm; E-F = 32 μm.
Fig. 11 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 11. Dorylaimopsis nodderi sp. nov. light micrographs. A. ♀ cuticle in pharyngeal region showing the cortical, median, and basal layers. B. Ventral view of ♀ head showing posterior portion of buccal cavity. C. ♀ mid-body region showing vulva and mature eggs. D. Sperm cells with nuclei. Scale bar: A = 8 μm; B = 12 μm; C = 50 μm; D = 30 μm.
Fig. 14 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 14. Hopperia ancora sp. nov. light micrographs. A. Ƌ head, showing everted tooth. B. Intestine wall of Ƌ, with arrows pointing to oval cells with larger, more closely packed granules than surrounding cells. C. Everted spicule. D. Vulva. Scale bar: A, D = 15 μm; B = 22 μm; C = 20 μm.
Fig. 10 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 10. Dorylaimopsis nodderi sp. nov. A. ♀ reproductive system. B. Ƌ posterior region, showing copulatory apparatus. C. ♀ tail. D. Ƌ posterior body region, showing copulatory apparatus. Scale bar: A = 90 μm; B = 40 μm; C and D = 55 μm.
Fig. 8 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 8. Cervonema proberti sp. nov. A. ♀ head, showing glands. B. Entire Ƌ. C. ♀ reproductive system. Arrows point to clear globular inclusions in intestine wall. Scale bar: A =20 μm; B and C = 65 μm.
Fig. 7 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 7. Cervonema proberti sp. nov. A. Ƌ anterior body region. B. ♀ anterior region. C. ♀ tail. D. Sperm cells. E. Ƌ posterior body region, showing copulatory apparatus. Scale bar: A, B, and C = 25 μm; D = 20 μm; E = 35 μm.
Fig. 9 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 9. Dorylaimopsis nodderi sp. nov. A. Anterior region of Ƌ. B. Lateral surface view of Ƌ head. C. Ventral view of ♀ head (cross-section). D. Entire Ƌ. E. Ƌ gut, mid-body. Scale bar: A = 50 μm; B and C = 25 μm; D = 70 μm; E = 30 μm.
Fig. 16 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 16. Hopperia beaglense Chen and Vincx, 1998. A. Entire ♀. B. Ƌ posterior region, showing testes and copulatory apparatus. Scale bar = 100 μm.
Fig. 12 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 12. Hopperia ancora sp. nov. A. Anterior region of Ƌ. B. Lateral surface view of Ƌ head. C. Lateral view of ♀ head. D. Ƌ posterior body region, showing copulatory apparatus. E. ♀ tail. Scale bar: A = 45 μm; B and C = 20 μm; D = 35 μm; E = 50 μm.
Fig. 6 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 6. Cervonema multispira sp. nov. A. Entire Ƌ. B. ♀ reproductive system. Scale bar: A =100 μm; B = 70 μm.
Fig. 4 in Deep-sea nematodes (Comesomatidae) from the Southwest Pacific Ocean: five new species and three new species records
Fig. 4. Cervonema kaikouraensis sp. nov. light micrographs. A. Anterior ovary, showing bent germinal portion. B. Ƌ head, showing jointed outer labial seta. C. Ƌ spinneret. D. Granules in the lateral chords of Ƌ. Scale bar: A = 40 μm; B = 18 μm; C = 12 μm; D = 24 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.