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.
81
datasets available to search
ShareScore release 0.9.0
Dataset results
81 results for “ocean simulation”
Amplification of the divergent component from balanced motions and forward kinetic energy cascade in an ocean-atmosphere simulation
<p>This dataset contains animations of:</p> <ol> <li> <p>The temporal evolution of ocean currents partitioned into balanced motions (BMs) and non-BMs (including internal gravity waves and low-frequency wind-driven currents), displayed in the physical domain for COAS (top row) and Ocean-forced (bottom row). Both simulations illustrate kinetic energy (KE) together with velocity gradients—vorticity ($\zeta$) and horizontal divergence ($\delta$)—all normalized by the Coriolis frequency.</p> </li> <li> <p>The temporal evolution of low-frequency (lf) and high-frequency (hf) non-BM currents in the physical domain for COAS (top row) and Ocean-forced (bottom row).</p> </li> </ol>
Reference LES Simulations for Ocean Models Validation
<p>All Large-Eddy Simulations (LES) computations are conducted with the open-source PALM solver (<a href="https://palm.muk.uni-hannover.de/trac">https://palm.muk.uni-hannover.de/trac</a>) and version 5.0 of the Los Alamos National Laboratory (<a href="https://github.com/lanl/palm_lanl">https://github.com/lanl/palm_lanl</a>) branch. The simulations use a cubical domain with length L=128m, three grid resolutions (N=128<sup>3</sup>, 256<sup>3</sup>, or 512<sup>3</sup>), and mimic four single-column canonical oceanic regimes in which the surface flux and background stratification profiles are imposed. These simulations are named cooling (c), stratification (s), evaporation (e), and mixed (m):</p> <ul> <li><strong>cooling cases</strong> test different surface heat fluxes: Q<sub>h</sub>=-1.185x10<sup>-5</sup> (c<sub>01</sub>), -2.371x10<sup>-5</sup> (c<sub>02</sub>), -4.742x10<sup>-5</sup> (c<sub>04</sub>), and -18.966x10<sup>-5</sup> (c<sub>16</sub>) [K.m/s]. The background stratification is set through a vertical temperature gradient (T<sub>z</sub>) equal to 0.1 [K/m], and both the salinity surface flux (Q<sub>s</sub>) and the background stratification due to vertical salinity gradient (S<sub>z</sub>) are equal to zero.</li> <li><strong>stratification cases</strong> are similar to the cooling c<sub>02</sub> case, but Q<sub>h</sub>=-2.371x10<sup>-5</sup> [K.m/s], and T<sub>z</sub>=0.01 (s<sub>01</sub>), 0.1 (s<sub>10</sub>), or 0.2 (s<sub>20</sub>) [K/m].</li> <li><strong>evaporation cases</strong> define Q<sub>h</sub>=T<sub>z</sub>=0, S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub>=3.115x10<sup>-6</sup> (e<sub>01</sub>) or 1.225x10<sup>-5</sup> (e<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> <li><strong>mixed cases</strong> combine heat and salinity surface fluxes, and the background stratification is imposed by both vertical temperature and salinity gradients. There are four mixed cases in which Q<sub>h</sub>=-1.185x10<sup>-6</sup> [K.m/s], T<sub>z</sub>=0.05 [K/m], S<sub>z</sub>=-0.025 [PSU/m], and Q<sub>s</sub> is either 0 (m<sub>01</sub>), 3.115x10<sup>-6</sup> (m<sub>02</sub>), 9.10x10<sup>-6</sup> (m<sub>03</sub>), or 4.55x10<sup>-5</sup> (m<sub>04</sub>) [PSU/(m<sup>2</sup>.s)].</li> </ul> <p>The simulations run 96h at a latitude of 43.29<sup>o</sup>, and utilize a linear equation of state where the reference temperature and salinity are 293.15 K and 35 PSU. The subgrid turbulent scales are modeled through the Moeng and Wyngaard SGS closure (see PALM documentation), and the flow is perturbed during the first 150 s of simulation with normally distributed fluctuations with a maximum amplitude of 10<sup>-4</sup>. The dataset provides 1D and 3D statistics. The 1D profiles are time-averaged during 3600 s and written every 3600 s. The 3D fields are not time-averaged and written every 3600 s, except those obtained with the grid having 512<sup>3</sup> points. In this case, the data are written every 21,600 s. The 3D fields are not in the tar file but can be requested by emailing the authors (fmsoarespereira@lanl.gov). The PALM input decks of the simulations are included in the shared files.<br> <br> <strong>Note:</strong> the PALM version used in this work requires the 1D salinity flux profiles to be normalized by the product of N<sub>x </sub>and<sub> </sub>N<sub>y </sub>(number of grid points in x and y)</p> <p> </p> <p><strong>LA-UR-22-32996</strong></p>
Data for "Turbulent drag at the ice-ocean interface of Europa in simulations of rotating convection: Implications for nonsynchronous rotation of the ice shell"
<p>Data files and python Jupyter notebook to reproduce Figures 2 - 6 from the manuscript "Turbulent drag at the ice-ocean interface of Europa in simulations of rotating convection: Implications for nonsynchronous rotation of the ice shell".</p> <p>Contents:</p> <p>Package</p> <p>--EuropaOceanTorque.ipynb - Jupyter Notebook to load and analyse data. Reproduces figures 1-2 from the manuscript.<br> --cs510 - Grid info files<br> --mitgcm_vis - plotting and interpolation functions<br> --Results - directory for all data files<br> --Setup<br> ----runx - input files for running MITgcm<br> ----code - mods for compiling the MITgcm</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
A tightly coupled river-ocean model for simulating combined flood due to storm surge and river flow in coastal-urban areas
<p>Coastal flooding, resulting from storm surges or extreme river flows, causes significant causalities and damage to properties in low-lying areas. The simultaneous occurrence of river flows and storm surges, termed combined/compound events, exacerbates the flood risk compared to independent occurrences. Combined flood events are simulated with the help of hydraulic and hydrodynamic models using a loosely or tightly coupled approach. In the loosely coupled approach, a hydrodynamic model simulates storm surges first, and a hydraulic model then simulates inland flood due to river overflow considering surge as the boundary condition at the river mouth/estuary. Conversely, the tightly coupled approach involves simultaneous simulation of both river flow and storm surge by coding the mathematical representation of river and ocean flow dynamics in the same numerical model. This allows the interaction between river and ocean flows to be simulated anywhere in the combined river-ocean computational domain, making it highly relevant for simulating combined floods. However, existing models based on this approach encounter numerical instability, especially in inland regions where topography variation is steep and highly uneven. Also, such combined models are highly limited for large scale applications. Therefore, this research focuses on developing a tightly coupled 2D finite volume river-ocean model called IROMS-C2D. The developed model intends to address the limitations of the previous models and provide a stable solution framework for the simulation of combined flooding resulting from the interaction of storm surges and river flows in coastal urban areas. Further, it enhances our understanding of flood risks in coastal areas, particularly in urban settings, and facilitates the formulation of effective measures for flood control and adaptation of coastal infrastructure.</p>
Data from: Plant diversity in oceanic archipelagos: realistic patterns emulated by an agent‐based computer simulation
Open the record for dataset details and reuse information.
Data from: Energy expenditure of adult green turtles (Chelonia mydas) at their foraging grounds and during simulated oceanic migration
Open the record for dataset details and reuse information.
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
Open the record for dataset details and reuse information.
Data from: Network analysis by simulated annealing of taxa and islands of Macaronesia (North Atlantic Ocean)
Open the record for dataset details and reuse information.
Monthly mean temperature and salinity outputs from the Southern Ocean high-resolution (SOhi) simulation
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>
CESM coupled simulation dataset with new (Mod_cp) and original (F09) dynamical coupling scheme from atmosphere, ocean and ice.
<p>1. Coupled models simulate air-sea interaction by coupling components models together. However, in state-of-the-art models such as CESM, the atmospheric model contains dynamic core and physic parameterization that are not differentiated during air-sea interaction. The dynamics-physics (D-P) coupling in CAM causes the prognostic winds of the dynamic core be interpolated onto non-staggered locations. We propose a new dynamical coupling scheme (denoted as Mod_cp) that eliminates the extra interpolation during D-P coupling for the atmosphere-ocean interaction, reducing the numerical diffusion during the dynamics-physics coupling for air-sea interactions.</p> <p>2. Using new and original dynamical coupling scheme, two coupled simulations (100 years) were conducted. Climatology of atmosphere (cam), ocean (ocn) and sea ice (ice) model simulations are archived here (last 50 years). The resolution is around 1 latitude x 1 longitude.</p> <p>3. B1850_mod_cp denotes the simulation with new dynamical coupling scheme, B1850_f09_g16 denotes the simulation with original scheme.</p>
Idealised ocean model sector simulations
<p>This repository contains model output for a series of `MOM6` ocean model simulations.<br> These simulations use a northern hemisphere sector domain to test buoyancy wind-driven gyres.<br> Wind forcing is achieved through a zonal wind stress that has maximum value ($\tau_0$) at mid-latitudes.<br> Thermal forcing is achieved by restoring towards a prescribed SST profile, at a rate proportional to `FLUXCONST`.<br> The southern boundary and upper several hundred meters have elevated diffusivity.</p> <p>See README files (.md or .pdf) for more information.</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>
Pathways, form drag, and turbulence in a simulated ocean flowing through through an ice melange: datasets
<p>Data associated with Hughes (2022). <em>J. Geophys. Res. </em><a href="https://doi.org/10.1029/2021JC018228">doi:10.1029/2021JC018228</a></p> <p>This archive contains</p> <ol> <li>Output from all model simulations together with a summary csv file. Full details in <strong>melange_flow_simulations_readme.pdf</strong></li> <li>A directory (<strong>icebergs.zip</strong>) containing the scripts necessary to reproduce model outputs described in the paper.</li> </ol> <p> </p>
Single Basin Simulations for "A Framework for Constraining Ocean Mixing Rates and Overturning Circulation from Age Tracers"
Open the record for dataset details and reuse information.
Data for Simulation of Particulate Organic Carbon transport in the Pearl River Estuarine-Coastal Ocean, South China Sea following Typhoon Hato and Pakhar (2017)
Open the record for dataset details and reuse information.
Simulations of Typhoon In-Fa (2106) and air-sea interactions using a coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system
<p>The Observation data supporting the result of our manucript submitted to JGR-Ocean.</p> <p> </p>
CORE inter-annual forced ocean ice simulation using E3SMv0-HiLAT-tx0.3v2 (HiLAT03)
<p>This data set supports the analysis presented in the manuscript: “Labrador Sea freshening linked to Beaufort Gyre freshwater release” (LA-UR-20-20936), which is under review for publication by Nature Communications. The model output is from a 186-year long simulation with the E3SMv0-HiLAT code that was described in the technical report: “An eddy-permitting ocean-sea ice general circulation model (E3SMv0-HiLAT03): Description and evaluation” (LA-UR-19-25177; doi: 10.2172/1542803), by Zhang et al.. The ocean and sea ice components are active, and their grid is configured with a nominal 0.3 degree horizontal resolution. The model is forced by an atmospheric data set that represents the atmospheric state from 1948 through 2009, and is repeated for 3 cycles.</p> <p>#----------------------------------------------------------------------------------------------------------<br> # Data description<br> # Fri Jul 24 16:55:12 MDT 2020<br> # Contacts: Jiaxu Zhang (jiaxuzh@uw.edu) and Wilbert Weijer (wilbert@lanl.gov)<br> #----------------------------------------------------------------------------------------------------------<br> Simulation length: 186 years<br> Simulation machine: LANL HPC facility, on Grizzly<br> Case name: t32_GIAF_woa13rest_deepenNares_Griz<br> Time range: 012101-018612 (corresponding to Jan 1948 to Dec 2009)<br> Grid file: gridFile/tx0.3v2_grid.nc<br> In the post-processed files:<br> pop = ocean output from POP2<br> mon = monthly<br> ann = annual mean<br> clim = monthly climatology<br> ltm = long-term annual mean<br> dec = decadal mean<br> YYYYMM-YYYYMM = time range<br> FastRel = Fast release case, corresponding to the 016001-017212 period<br> FastAcc = Fast accumulation case, corresponding to the 017301-018512 period<br> NH = data only contain the Northern Hemisphere</p> <p>Similarly, there are files for "BG" (Beaufort Gyre), "Davis_Strait", "Fram_Strait", "Labrador_Sea", "Labrador_Sea_Outflow", "Lancaster_Sound", and "Nares_Strait". These regions are illustrated in Figure 1 of the manuscript.</p> <p>Variable names:<br> SALT = salinity<br> UES = East flux of SALT<br> UVEL = Zonal Velocity<br> VNS = North flux of SALT<br> VVEL = Meridional velocity<br> DYE01 = dye tracer that tags the Beaufort Gyre freshwater (from surface to the reference salinity of 34.6)<br> DSALT01 = salt tracer that tags the Beaufort Gyre salinity (from surface to the reference salinity of 34.6)<br> UE_DSALT01 = East flux of DSALT01<br> VN_DSALT01 = North flux of DSALT01</p> <p>In the transport folder:<br> File names: transport.[StraitName].012101-018612.txt<br> Content: Diagnosed transport at a specific strait.<br> Structure: 4 columns are<br> 1. Model time (days since 0001-01)<br> 2. Volume transport (Sv)<br> 3. Heat transport (PW)<br> 4. Liquid freshwater transport (mSv)<br> For all the transports, positive is poleward, negative is equatorward.</p> <p><br> File names: [CaseName].transport.BG_portion.[StraitName].txt<br> Content: Diagnosed BG-sourced transport at a specific strait for either FastRel or FastAcc case.<br> Structure: 8 columes are<br> 1. Model time (days since 0001-01)<br> 2. Volume transport (Sv)<br> 3. Liquid freshwater transport (mSv)<br> 4. Validation data, please ignore<br> 5. Validation data, please ignore<br> 6. Volume transport sourced from BG alone (Sv)<br> 7. Liquid freshwater transport sourced from BG alone (mSv)<br> 8. Validation data, please ignore</p> <p> </p> <p> </p> <p> </p> <p> </p>
(Copernicus - WEkEO Hackathon 22 – 23 JUNE 2023 ) Digital Cartography Simulation of Essential ocean variables (EOV) (educational support resource in space oceanography)
<p>Description of idea : The multidimensional view of the Earth and its immediate environment that is provided by space borne sensors, operating at many wavelengths and directed at many different phenomena, has revolutionized man's understanding of his planet and the surrounding space environment.<strong>(John H. McElroy.,1985),</strong></p> <p>Earth observation satellites measuring in the visible and infrared spectral domain provide a global perspective for many required to determine the role of the ocean in the global climate system, as well as the effects on the ocean of a changing climate <strong>(James A. Yoder and all.,2014).</strong></p> <p>Data visualization by video graphics technology is a digital modeling technique also a description or analogy used to help visualize something that cannot be observed directly which exploits the bases of scientific knowledge in a data processing system by the use of mathematical and statistical tools and analysis and forecasting methods to visualize what is hidden behind the data. This work is inspired by the general principle of numerical modeling and data processing, which takes into consideration (the observation of natural phenomena, and the statistical processing of scientific data, which are at the base of the functioning of natural variation).</p> <p>We see on the same simulation scale several other spatial and temporal scale, for example the simulation of SST of several years with a large gap between the years, also the simulation of the displacement of surface currents with the variations of the SST, in other words the document can be used in pedagogy.</p> <p> </p> <p>Bibliographic reference:<br> -Monitoring Earth's Ocean, Land, and Atmosphere from Space-Sensors, Systems, and Applications, edited by Abraham Schnapf, American Institute of Aeronautics and Astronautics, 1985<br> -Optical Radiometry for Ocean Climate Measurements, Elsevier Science & Technology, 2014</p> <p> </p> <p> </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.