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955 results for “Ocean data”
Sentinel-3B OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3A OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Aqua MODIS Regional Ocean Color (OC) - Near Real Time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
The Wave Acquisition Stereo System data in support of Directional Breaking Kinematics Observations from 3D Stereo Reconstruction of Ocean Waves
<p>The stereo-image data archived at https://data-dataref.ifremer.fr/stereo/AA_2015/2015-03-05_10-35-00_12Hz/ has been processed with the Wave Acquisition Stereo System (WASS) software and the output is resampled to a regular grid of 20 cm for the computation of Wave Spectrum.</p> <p>The data is in standard Netcdf4 format and include metadata. The python script used to generate this .nc file is available at https://github.com/akaawase-bernard/WASSnc</p> <p>There is a total of 21571 snapshots of the sea surface elevation information.</p>
Data from: Deep Sea Spy: an online citizen science annotation platform for science and ocean literacy
<p>Data sets of buccinid <em>Buccinum thermophilum </em>and crab <em>Segonzacia mesatlantica </em>after identifying unique groups (i.e. individuals) — using the updated version (v0.0.3) of the <a title="Deep Sea Spy - deeptools" href="https://github.com/DeepSeaSpy/deeptools">deeptools</a> package — among Deep Sea Spy citizen participants and expert.</p> <p>Data cleaning : annotated buccinids in background removed in <span><a href="https://zenodo.org/api/records/14203506/draft/files/CS_groups.csv/content" target="_blank" rel="noopener noreferrer">CS_groups.csv</a></span></p> <p>R script used to generate and analyse the dataset.</p> <p><strong>Please, use this version (v5).<br></strong></p>
Data for publication "CMIP6 trends of oceanic variables (2015-2100)"
<p>These netCDF files are made available as part of the paper: G. Ibarra-Berastegui, J. Sáenz, A. Ulazia, S. J. González-Rojí & G. Esnaola (2024) "CMIP6 trends of oceanic variables (2015-2100)", submitted to Ocean Engineering. The dataset holds the trends of several variables needed to reproduce the results included in the manuscript.</p> <p>The files are named following the same structure: <em>${Variable}</em>_trend_<em>${SSP}</em>_<em>${Season}</em>perz.nc</p> <ul> <li>Variable: This dataset includes the trends (in % with respect to current-day values) of four variables for the period 2015-2100: wave energy flux (WEF), wind speed (U), mean wave period (mwp; Tm in the paper) and significant wave height (swh; Hs in the paper). *</li> <li>SSP: The analysis is performed for two Shared Socioeconomic Pathways (SSP): the lowest and highest GHG emission climate scenarios, SSP 1-2.6 and SSP 5-8.5 respectively.</li> <li>Season: The trend analysis is performed for the entire year (YE) or for each season: spring (SP), summer (SU), autumn (AU) or winter (WI). </li> </ul> <p>* Note that, as explained in the manuscript, the current-day oceanic values have been obtained from ERA5 global averages corresponding to the 1985-2014 period. The ERA5 data can be downloaded from the official website of the ECMWF. </p>
Data Processing for a Small-Scale Long-Term Coastal Ocean Observing System Near Mobile Bay, Alabama: A Geoscience Papers of the Future (GPF) Workflow Diagram
<p>The Dauphin Island Sea Lab (DISL) has been operating a permanent moored oceanographic station at 30 05.410'N, 88 12.694'W, 25 km southwest of the entrance to Mobile Bay, Alabama, since 2004. It collects hydrographic and current velocity data.</p> <p>This diagram shows the processing steps for data from the instruments at this mooring, from initial download to initial scientific analysis.</p> <p>The file has been prepared as supplementary material for a Geoscience Paper of the Future (GPF) in prep for publication at Earth and Space Science, as part of the OntoSoft GPF Initiative.</p>
Data for prepare-ocean-param
Open the record for dataset details and reuse information.
Data used in paper: Characteristics of shallow low-frequency earthquakes off the Kii Peninsula, Japan, in 2004 revealed by ocean bottom seismometers
<p>The data archived here is a set of the OBS waveform data used in the paper 'Characteristics of shallow low-frequency earthquakes off the Kii Peninsula, Japan, in 2004 revealed by ocean bottom seismometers', by Koji Tamaribuchi, Akio Kobayashi, Takahito Nishimiya, Fuyuki Hirose, and Satoshi Annoura.<br> <br> For more information, please contact the first author.</p>
Ocean Bottom Seismometer data of line OBS2017-2 in the South China Sea
<p>Dataset line OBS2017-2 was collected in June 2017, using R/V “Shiyan 2” that belongs to the South China Sea Institute of Oceanology, CAS. The spacing of OBSs was ~9.6 km. The seismic source consisted of an array of four BOLT air-guns with a total volume of 6,000 cubic inches and was towed at ~10 m depth below sea level. 1,767 shots had been fired along OBS2017-2 from south to north, and the shooting intervals were set at 80-110 s with a nominal ship speed of ~4.5 knots, resulting in a shooting interval of ~200 m on average.</p> <p>The format of seismic data is SAC. Please send the purpose of the data requesting, as well as the name and affiliation of the applicant to <a href="mailto:go223@scsio.ac.cn">go223@scsio.ac.cn</a>. The updated SEGY format data can be accessed through <a href="http://www.doi.org/10.11922/sciencedb.01267">http://www.doi.org/10.11922/sciencedb.01267.</a></p>
Data used in paper "Crustal structure across the extinct mid-ocean ridge ..."
<p>Seismograms are used for generating receiver functions for 11 OBS in the Central sub-basin of the SCS. We note that seismograms are windowed to include only the P wave (40 s and 60 s before and after the predicted arrivals). </p> <p>Note: This dataset can only be downloaded to verify the RF results. it can not be used for other seismic applications since it has already been allocated to specific groups that are working on seismic tomography, anisotropy, noise analysis, microearthquake relocations, and waveform modeling. Please contact us at: tyang@sustech.edu.cn for more information. </p>
Data publication for the paper entitled Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM
<p>The files contain the source code of ICON-ESM-V1.0, primary data and scripts used in the analyses and for producing the figures for the paper "Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM" by Pohlmann, H., Brune, S., Fröhlich, K., Jungclaus, J. H., Sgoff, C., and Baehr, J. (2022, JAMES).</p>
Data supplementing article "Sediment exchange between channel and sand ridges in the southern Yellow Sea " under review at the Journal of Geophysical Research - Oceans
<p>Data for Plotting</p>
Weakly coupled atmospheric-ocean data assimilation in the Canadian global prediction system. (v1)
<p>Code and data for GMD reviewers</p>
Data from: Integrating diverse data for robust species distribution models in a dynamic ocean
<p><strong>Aim: </strong>Species distribution models (SDMs) are an important tool for marine conservation and management, yet guidance on leveraging diverse data to build robust models is limited. While various approaches can be used to integrate different datasets, studies comparing their performance, particularly for highly migratory and mobile species, are scarce. Here, we assess whether a model-based integrative framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types.</p> <p><strong>Location: </strong>North Atlantic Ocean</p> <p><strong>Time Period: </strong>1993 - 2019</p> <p><strong>Major Taxa Studied: </strong>Blue shark (<em>Prionace glauca</em>)</p> <p><strong>Methods: </strong>We trained traditional, correlative SDMs and integrated SDMs (iSDMs) with three distinct data types: fishery-dependent marker tags, fishery observer records, and fishery-independent electronic tag data. We evaluated data pooling and ensemble approaches in a correlative SDM framework and compared performance to an iSDM approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset.</p> <p><strong>Results: </strong>While each integration approach yielded robust models, model performance varied among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model's ability to explain the spatiotemporal dynamics of the training data. iSDMs that explicitly accounted for seasonal variability yielded the most accurate and ecologically realistic estimates. However, such approaches are computationally intensive and warrant identifying model purpose as an important step in the data-integration process.</p> <p><strong>Main Conclusions: </strong> Our findings reveal important trade-offs among the current techniques for integrating data in SDMs, including variability in accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, maximizing our ability to leverage available data with robust analytical approaches will be instrumental in enhancing conservation and management efforts and for understanding current and future species distributions in a dynamic ocean.</p>
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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.