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2,991 results for “indian ocean”
Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition (2016-2017)
<p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017.</p> <p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (>6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20).</p>
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints
<p>This dataset contains an initial set of curated and processed oceanographic observations collected as part of a joint effort between the EU SO-CHIC project and a UKRI Future Leaders Fellowship. It is partly intended to be used as a set of observational constraints for a Weddell Gyre region state estimate, although it can be used for more general analysis purposes as well. It has been used as part of an unsupervised clustering analysis [see Jones (2022) for software and Jones and Zhou (2022) for labelled dataset, see references]. </p> <p><strong>Overall spatial and temporal coverage</strong></p> <ul> <li>Latitude: 85°S-30°S</li> <li>Longitude: 65°W-80°E</li> <li>Time: 1974-2020</li> </ul> <p><strong>Contents</strong></p> <ul> <li>CPOM_SSH: sea-ice corrected sea surface height </li> <li>CTD: temperature and salinity profiles from ship-based CTD casts </li> <li>FLOATS: temperature and salinity profiles from Argo floats </li> <li>SEALS: temperature and salinity profiles from seal-mounted profilers</li> <li>Stress_and_EKE: sea-ice corrected surface stress and EKE </li> <li>XBT: temperature and salinity profiles from expendable bathythermographs (XBTs)</li> </ul> <p><strong>Profile quality control</strong></p> <p>We only consider profiles with good position and time flags, as well as good temperature, salinity, and pressure measurements with good flags. Duplicated profiles are identified when multiple profiles are found within 24 hours over the same 2 km x 2 km grid cell, and only one profile within the spatio-temporal window is used. We then used the MITprof toolbox (Forget, G., 2017) to pre-process the selected profiles, re-gridding them onto 72 standard pressure levels; the vertical interval varies from 20 dbar at the surface to 100 dbar in the deep ocean. </p> <p><strong>SSH processing</strong></p> <p>SSH data is sea-ice corrected version provided by the Centre for Polar Observation and Modelling (CPOM) in the UK. It is composed by two satellite missions, Envisat (2004/05-2012/03) and Cryosat-2 (2010/07-2020/04). The data is available in montly along-track format. A gaussian 300km filter, ±3 std outliner removal and 0.5x0.25 deg interpolation is applied to grid the data. Intersatellite offset is removed using the overlapped period between two missions using the mean difference map. SSH is referenced to EIGEN6C4 geoid to obtain the dynamic ocean topography feild for the computation of geostrophic velocity. See the README in the Stress_and_EKE directory for more information. </p> <p><strong>Sources</strong></p> <ul> <li>Argo floats: <a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a></li> <li>World Ocean Database: <a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a></li> <li>MEOP-CTD Database (seal profilers): <a href="https://www.meop.net/">https://www.meop.net/</a></li> <li>CDRv4 available via NSIDC: <a href="https://nsidc.org/data/G02202">https://nsidc.org/data/G02202</a></li> <li>Polar Pathfinder sea ice drift data via NSIDC: <a href="https://nsidc.org/data/nsidc-0116">https://nsidc.org/data/nsidc-0116</a></li> </ul> <p><strong>Version</strong></p> <p>This is a pre-production version, in that it has not yet been used with a state estimate. </p>
Exploring the Pocillopora cryptic diversity: a new genetic lineage in the western Indian Ocean or remnants from an ancient one?
<p>Cryptic species and lineages have been widely reported during the last decades, particularly in the marine realm. Misidentifications and ignoring species complexes imply many consequences, notably biasing biodiversity and connectivity assessments, which in turn mislead our understanding of ecosystems and impact the effective design and management of conservation plans. Focusing on the Indo-Pacific coral genus <em>Pocillopora</em>, playing key roles in reef ecosystems as one of the main bio-constructors, we report the first <em>Pocillopora</em> PSH16 (ORF53; <em>sensu</em> Gélin et al. 2017, Mol Phylogenet Evol 109:430–446) colonies (<em>N</em> = 19) in the western Indian Ocean (Nosy Tanikely, Madagascar), 6,000 km further from its current distribution. Colonies were identified according to their mitochondrial Open Reading Frame (ORF) haplotype and Bayesian assignment tests based on 13-microsatellite genotypes. Additionally, we performed genetic structure and diversity analyses with sympatric colonies from other <em>Pocillopora</em> species and <em>Pocillopora</em> PSH16 colonies from the tropical southwestern Pacific, revealing (1) a weak clonal richness, (2) a weak genetic diversity and (3) a relative isolation for the newly reported PSH16 colonies. These colonies thus represent either a new, distinct and uncommon, genetic lineage, or isolated remnants of a wider one. In any case, unless specific management measures are implemented, their long-term maintenance seems compromised due to restricted gene flow within a restricted pool of genes.</p> <p> </p> <p>This dataset contains the microsatellite genotypes analysed (98 <em>Pocillopora</em> colonies × 13 loci + ORF). Missing data are encoded as "?". The sampling marine province and the population are indicated for each individual.</p>
Influence of the tropical Indian Ocean tripole on summertime cold extremes over central Siberia
<p>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, zonal and meridional winds.</p>
Evolution of Indian Ocean Paleoceanography and South-East Asian Climate during the Miocene in response to change in regional topography
<p>This directory contain outputs of 9 paleo-climate simulations performed with the IPSL-CM5A2 and PISCES-v2 models. The simulations have used in a paper to be published in Nature Geoscience (2022) entitled "Divergent South Asian Monsoon Rainfall and Wind Histories due to topography effects" (Sarr et al.) that investigates the co-evolution of Arabian Sea upwelling and South Asian Monsoon rainfall and winds over the Miocene. It includes simulations with both early Miocene and late Miocene paleogeography.</p> <p>SimulationsOutputs.tar directory contains NetCDF files with ocean, ocean biogeochemistry and atmosphere variables. Data are monthly average over the last 100 years of each simulation.</p> <p>TopoMiocene.tar contains the paleogeographies used for the simulations.</p> <p> More informations on output contents can be find in README_detailsOutput.md document as well as within the Methods section of the publication.</p> <p>PISCES_update.tar contains updated routines for the PISCES-offline model (Aumont et al., 2015) that have been used for the publication. It contains a REAME.md file that explain how to include those updates within the reference code.</p> <p> </p>
Satellite tracking data of white sharks in the southwest Indian Ocean (2012-2014)
<p>These data comprise locations and individual metadata from 34 white sharks (<em>Carcharodon carcharias</em>) instrumented March-May 2012 with telemetry devices along the coast of South Africa. These devices were SPOT5 transmitters (SPOT-257, SPOT-258; Wildlife Computers) which transmit locations via ARGOS CLS. All research methods were approved and conducted under the South African Department of Environmental Affairs: Oceans and Coasts permitting authority.</p> <p>This dataset is linked to the manuscript Kock et al. 2021 "Sex and size influence the spatiotemporal distribution of white sharks, with implications for interactions with fisheries and spatial management in the southwest Indian Ocean".</p> <p>The data are structured in long format, so that each row in the dataset represents an observation. The columns in the data are as follows.</p> <p>DeployID: This a factor variable identifying each individual shark. It has 34 levels.</p> <p>SPOT: This is a numeric variable identifying the tag number unique to each shark.</p> <p>Date: This is a date variable (POSIXct) that gives the date and time of a geographic location record in UTC time.</p> <p>Type: This is a character variable identifying the type of location record.</p> <p>Quality: This is a character variable made up of numbers and letters giving the location error associated with each location as provided by ARGOS.</p> <p>Latitude: This is a numeric variable and gives the latitude of the shark at the time of each record.</p> <p>Longitude: This is a numeric variable and gives the longitude of the shark at the time of each record.</p> <p>Area_tagged: This is a character variable that gives the area where the shark was tagged.</p> <p>Sex: This is a character variable identifying the sex of the shark, either "F" or "M" for female and male.</p> <p>TL: This is a numeric variable giving the total length of the shark in centimetres.</p> <p>Maturity: This is a character variable giving the maturity of the shark based on its total length following Malcolm et al. 2001: juveniles (male and female: 175-300 cm TL), sub-adults (male: >300-360 cm TL; females: >300-480 cm TL) and adults (male: >360 cm TL; female: >480 cm TL).</p> <p> </p>
Temperature measurements from the SMS Gazelle, Valdivia, and SMS Planet in the Indian Ocean
<p>This dataset contains digitized temperature records from the SMS Gazelle (1874–1876), Valdivia (1898–1899), and SMS Planet (1906–1907) observations in the Indian Ocean. The data is described in:</p> <p>Wenegrat, J.O., E. Bonanno, U. Rack, and G. Gebbie, 2022: A century of observed temperature change in the Indian Ocean. <em>Geophys. Res. Letters.</em> doi:10.1029/2022GL098217.</p> <p>Data was digitized from the original cruise reports using independent double-entry, and checked for consistency. A number of observations were discarded due to data problems, as described in Wenegrat et al. 2022 (see also associated code repository doi:10.5281/zenodo.6646645).</p>
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
Homisland-IO: a homogeneous land cover over the small islands of the southwest Indian Ocean
<p>This dataset is a landcover product, called Homisland-IO<strong>,</strong> based on the analysis of high spatial resolution images acquired by the SPOT 5 satellite between December 2012 and July 2014 and produced at the SEAS-OI Station. We used an object-based image analysis method to identify the 11 major classes of land cover / land use of these tropical islands. This methodology together with a good knowledge of the field has enabled us to achieve an overall accuracy of 86%, making it an operational product. Homisland-IO is<strong> </strong>freely accessible through a web portal and thus available for future uses.</p>
Water Body Checklists 2019: Indian Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists: Indian Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Marine heatwave datasheet for Northern Indian Ocean
<p>The datasheet gives a detailed information on the marine heatwave intensity from 1981 to 2020 at the three coral reef regions (Andaman and Nicobar, Gulf of Mannar and Lakshadweep archipelago) in the Northern Indian Ocean. This dataset was used to study various regional ecosystem changes from the variability in MHW over the period of time.</p>
Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method
<p>Supporting data for Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method</p> <p>Matlab Codes to reconstruct the subsurface structures (Codes without Figure_*.m) and plot the figures (Figure_*.m) in the manuscript. The file in Netcdf format is our reconstructed 3D density and currents.</p> <p> </p>
FIG. 8 in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 8. — Monitilora Iredale, 1930, Palaeogene fossils (A-D) and Monitilora sepes (Barnard, 1964) Inhaca, Mozambique (E-Q): A, B, Monitilora duponti (Cossmann, 1908) Paleocene, Danian, Calcaire de Mons, Mons Puits Coppée, Belgium (RBINS I.G. 6544), L 17.5 mm; C, D, Monitilora obliqua baudoni (Deshayes, 1857) Eocene, Lutetian, Amblainville, Oise, France, Chavan collection (RBINS I.G. 21.735), L 18 mm; E, F, Monililora sepes exterior and interior of left valve, Inhaca stn MD11, L 15 mm; G, H, exterior and interior of right valve, Inhaca stn MD15, L 12.1 mm; I, J, exterior and interior of right valve, Inhaca stn MD15, L 10.2 mm; K, L, exterior and interior of right valve, Inhaca stn MD15, L 10.1 mm; M, interior of left valve, Inhaca stn MD15, L 8.4 mm; N, O, detail of hinge teeth of left and right valves of H, I; P, detail of external sculpture of K; Q, protoconch of H. Scale bars: N, O, 1.0 mm; P, 500 µm; Q, 100 µm.
FIG. 4 in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 4. — Barbierella louisensis (Viader, 1951): A-D, Lucina (Bellucina) louisensis Viader, 1951 syntypes (AMS C.305545), off Port Louis, Mauritius, L (A, B) 6.2 mm, (C) 5.5 mm, H (D) 5.4 mm. Images by A. C. Miller, Copyright: Australian Museum; E-G, Barbierella scitula Oliver & Abou-Zeid, 1986, holotype (NMW.Z.1982.68.1) exterior of right and interior of right and left valves (gold coated for SEM), off Ras Budran, Gulf of Suez, Red Sea, 30 m, L 8.2 mm, Images copyright NMW; H-K, Barbierella louisensis, Banc de la Zélée, Mozambique Channel, BENTHEDI stn 110, 24 m; H, I, exterior and interior of left valve, L 7.8 mm; J, K, interior and exterior of right valve, L 7.8 mm; L-W, Barbierella louisensis Inhaca, Mozambique, INHACA stn MD13, 50-53 m (MNHN); L, M, interior and exterior of right valve, L 6.0 mm; N, O, exterior and interior of left valve, L 5.9 mm; P, Q, interior and exterior of right valve, L 5.9 mm; R, exterior of right valve coated SEM im- age, L 6.5 mm; S, T, detail of hinge area of right and left valves; U, detail of lunule and dentition of right valve; V, detail of sculpture of R,; W, protoconch. Scale bars, S, T, 1 mm; U, V, 500 µm; W, 100 µm.
FIG. 6. — A-H, Retrolucina voorhoevei n in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 6. — A-H, Retrolucina voorhoevei n. comb. (Deshayes, 1857), Recent; and I-N, R. defrancei (Deshayes, 1857), Eocene; A-C, exterior of right and interiors of right and left valves, Mozambique (ANSP 234103), L 70 mm; D, E, exterior and interior of left valve, Mozambique (USNM 628930), L 78 mm; F, dorsal view (NHMUK 20170373), L 78 mm; G, H, details of hinge of right and left valves (NHMUK 20170373), scale bar, 10 mm; I, J, Retrolucina defrancei (Deshayes, 1857) exterior and interior of left valve Eocene, Lutetian, Chaumont-en-Vexin, Oise,France (MNHN.F.J07396), L 35 mm; K, L, Retrolucina defrancei (Deshayes,1857),Eocene, Lutetian, Chaussy, Seine et Oise, France, (RBINS IG10591), L 71 mm; M, N, Retrolucina defrancei (Deshayes, 1857), Eocene, Lutetian, Parnes, France, Deshayes collection (NHMUK 33283a), L 50.4 mm.
FIG. 2. — Gibbolucina zelee n in Hanging on - lucinid bivalve survivors from the Paleocene and Eocene in the western Indian Ocean (Bivalvia: Lucinidae)
FIG. 2. — Gibbolucina zelee n. sp., Banc de la Zélée, Mozambique Channel, BENTHEDI stn R110, 24 m: A-D, holotype (MNHN-IM-2000-33710) exterior and interior of left and right valves, L 15.3 mm; E, F, paratype (MNHN-IM-2000-33711) exterior and interior of left valve, L 12.7 mm; G, paratype (as E, F) interior of right valve with reconstituted body, L 12.7 mm; H-J, paratype (MNHN-IM-2000-33711) exterior of right valve and interior of right and left valves, L 17.7 mm; K, L, paratype (MNHN-IM-2000-33711) exterior of right valve and dorsal view, L 10.5 mm; M, N, paratype (MNHN-IM-2000-33711) exterior of left valve and dorsal view, L 9.2 mm; O, P, paratype (MNHN-IM-2000-33711) interior of right and left valves, L 8.9 mm; Q, R, paratype (MNHN-IM-2000-33711) detail of hinge teeth of right and left valves of O & P; S, protoconch of Q. Abbreviations: am, anterior adductor muscle; f, foot; ld, left demibranch; pa, posterior apertures; pm, posterior adductor muscle. Scale bars: Q, R, 1.0 mm; S, 100 µm.
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.
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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.