Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “ocean eddy”
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Data and code for: Pelagic sharks target long-lived, retentive anticyclonic eddies in the Northwest Atlantic Ocean
Open the record for dataset details and reuse information.
Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.
<p>This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans.</p> <p>Specifically, this repository contains the following items:<br> (1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN).<br> (2) Codes for assessing the prediction skill of the machine learning and LR methods. <br> (3) Seasonal-mean and annual-mean input data to run these codes. <br> (4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .</p>
Data used in "Topographic Modulation of the Wind Stress Impact on Eddy Activity in the Southern Ocean"
<p>These are the data used in the creation of figures (Fig3,4,S2) in Cai et al. 2022: "Topographic Modulation of the Wind Stress Impact on Eddy Activity in the Southern Ocean", including 9 experiments. It is obtained from NEMO output.</p>
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</p>
Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'
<p>Additional Supporting Information to 'Quantifying the Contribution of Ocean Mesoscale Eddies to Low Oxygen Extreme Events.'. Submitted to Geophysical Research Letters for publication. 2022.</p> <p>NetCDF files and Python NumPy arrays of data used to create all figures in the manuscript main text and supporting information.</p>
Model data for " Topography Influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"
<p>This dataset is for the paper " Topography influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.
A multifaceted isoneutral eddy transport diagnostic framework and its application in the Southern Ocean -- datasets
<p>We propose a multifaceted isoneutral eddy transport diagnostic framework and apply it in the Southern Ocean to analyze eddy potential vorticity (PV) transport dynamics. We leverage a thickness-weighted spatiotemporal scale separation to define a generalized form of eddy flux. A local sampling method is employed and validated to estimate the transport tensor. We introduce Leonard’s decomposition and stationary-transient decomposition to diagnose the role of multiscale interactions. The non-Reynolds term under Leonard’s decomposition and the stationary effect perform prominently in determining the transport property. The Reynolds and transient parts have distinctions, that show oceanic mesoscale dynamics are not synergistic in time and spatial scales. We investigate the spatial scale dependence of each part and find that the transient and stationary components of the Reynolds (non-Reynolds) term have consistent (dissimilar) scale dependence. The scale dependence of the total eddy flux and the transport eigenvalues is controlled by the non-Reynolds term. We further investigate the eigenvalues in terms of the potential enstrophy budget and anisotropy. The potential enstrophy in the Southern Ocean holistically has a forward cascade on the researched scales. The Reynolds and transient effects efficiently promote the forward enstrophy cascade. The non-Reynolds and stationary effects stimulate drastically anisotropic eddy transport. The PV gradient barrier mechanism dominates the drastic anisotropic case. When the anisotropy is weak, a special anisotropic case with a locally balanced potential enstrophy budget overwhelms the case of real isotropy. The results urge future diagnostic works to examine the generalized eddy flux form in a spatiotemporal sense.</p>
Dataset for paper Pavel Perezhogin, Andrey Glazunov "Subgrid parameterizations of ocean mesoscale eddies based on Germano decomposition" submitted to JAMES.
<p>The data is updated due to the response to review in the third round.</p> <p>The directory structure is :</p> <pre><code>├── barotropic │ ├── data │ ├── figures │ └── code ├── pyqg │ ├── data │ ├── figures │ └── code ├── NEMO │ ├── data │ ├── figures │ └── code </code></pre> <p>Where <strong>barotropic</strong> - experiments in barotropic fluid, <strong>pyqg</strong> - experiments in QG model, <strong>NEMO</strong> - experiments in primitive equation model.</p> <p>Subfolder <strong>data</strong> contains numerical simulations, <strong>figures</strong> contains plotters of Figures for Publication, and <strong>code</strong> is source code with parameterized barotropic/QG/primitive equation ocean models. </p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Open the record for dataset details and reuse information.
Ocean Currents from eddy-resolving model simulations
<p>This data set includes the model data used in the paper by Barnier et al. (2020) <strong>"Modelling the impact of flow-driven turbine power plants on great wind-driven ocean currents and the assessment of their energy potential"</strong> to appear in Nature Energy in 2020. It contains the ocean currents produced by eddy-resolving model simulations in the regions of the Gulf Stream and Kuroshio. Two one year long simulations are available. A Control simulation, and a Turbine simulation. In the Turbine simulation, virtual large turbine power plants (TPPs) are implemented at given locations.The effects of the TPPs on the flow is performed in the study of Barnier et al. (2020) by comparing the Control and the Turbine simulations.</p>
Scale and Space Dependent Horizontal Eddy Diffusivity at the Global Surface Ocean
<p>Sea surface temperature (SST) and idealized temperature tracer based estimates of horizontal diffusivity at the ocean surface. See https://doi.org/10.5281/zenodo.4126315 for the code to reproduce these estimates.</p> <p>There are two files which include the following</p> <p>KxKy_SST_MITGCM_025.nc</p> <p>SST based estimates are based on 35 years of satellite SST data (1982-2017) and we have divided the data in 30 chuncks, both using a 5 year and 10 year sub-sampling. The 'ens' dimension marks these chuncks whereas 'lat' and 'lon' mark the two Cartesian directions (latitude and longitude). Note that 'ens' can be also viewed as time.</p> <p>There are two fields 'Kx' and 'Ky' for each dataset ('SST' for the OI-SST based estimates or 'MITGCM' for the tracer simulation based estimates) and length of sub-sampling. 'Kx' is the diffusivity in 'lon' direction whereas 'Ky' is the diffusivity in 'lat' direction. Data is given at 0.25° resolution, but the effective resolution is twice that i.e. 0.5°.</p> <p>K_SST_MITGCM_2deg.nc</p> <p>Scale dependent estimate of the diffusivity magnitude i.e. <span class="math-tex">\(K= \sqrt{K_xK_y}\)</span> . As for the other data, here as well the estimates are done using 5 year and 10 year sub-sampling, and all the data is interpolated to a common 2° by 2° global grid. The coordinate 'dg' defines the scale in degrees. Note that again the effective resolution of the estimates is twice the 'dg'.</p> <p> </p> <p> </p>
Model data for "Factors Modulating Variability of Eddy Kinetic Energy in the Southern Ocean from Idealized Simulations" "
<p>This dataset contains the all the idealized simulations with different topographic features.</p>
Code for paper 'Advances in Surface Water and Ocean Topography for Fine-Scale Eddy Identification from Altimeter Sea Surface Height Merging Maps'
Open the record for dataset details and reuse information.
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>
Eddy activity in the Arctic Ocean projected to surge in a warming world
<p>High resolution FESOM2 results used for Arctic Ocean eddy activity study, with Figure 1-5 in the article and Extended Data Figures included in the supplementary material.</p>
Post-processed output for paper "Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model"
<p>This dataset contains processed output of the ACCESS-OM2-01 global ocean-sea ice model.</p> <p>The processed output was used for analyses of the paper:</p> <p>Huneke W. G. C., Morrison A. K., Hogg A. McC. Spatial and interannual variability of the Antarctic Slope Current in an eddying ocean-sea ice model, 2022, Journal of Physical Oceanography, doi:10.1175/JPO-D-21-0143.1</p> <p>The Github repository github.com/wghuneke/ASC_SpatialTemporalVariability contains Jupyter notebooks and instructions for how to produce these files from the raw model output.</p> <p>Contact wilma.huneke@anu.edu.au if you want access to the ACCESS-OM2-01 raw model output itself.</p>
Dataset for "Mesoscale Eddy-Induced Sharpening of Oceanic Tracer Front and its Parameterization"
<p>Data of the tracer experiments and the associated diagnostics in the shallow water model for the ocean front study.</p><ul><li><strong>eforc.tar.gz</strong>: diagnosed eddy forcing fields for different tracers;</li><li><strong>exps_trs.tar.gz</strong>: solutions in offline tracer experiments on the coarse grid;</li><li><strong>forc_uvh.tar.gz</strong>: mass fluxes and layer thicknesses used to advect tracers;</li><li><strong>params.tar.gz</strong>: parameters (eddy-induced velocities, diffusivity) used for tracer experiments</li></ul><p>Please contact Yueyang Lu via <strong>yueyang.lu@miami.edu</strong> if there are any questions.</p>
ScienceDex guides
Understand access before you commit
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.