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195 results for “Coupled models”

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zenodo32/100

Dataset for scaling of the 2d orders coupled to the Ising Model

This is the data and some example analysis scripts used for the article "Phase transitions in $2$d orders coupled to the Ising model"

opencc-by-4.0Nov 2020View details →

dryad32/100

Data from: Phylogeography in continuous space: coupling species distribution models and circuit theory to assess the effect of contiguous migration at different climatic periods on genetic differentiation in Busseola fusca (Lepidoptera: Noctuidae)

Current population genetic models fail to cope with genetic differentiation for species with large, contiguous and heterogeneous distribution. We show that in such a case, genetic differentiation can be predicted at equilibrium by circuit theory, where conductance corresponds to abundance in species distribution models (SDM). Circuit-SDM approach was used for the phylogeographic study of the lepidopteran cereal stemborer Busseola fusca Füller (Noctuidae) across sub-Saharan Africa. Species abundance was surveyed across its distribution range. SDM models were optimized and selected by cross validation. Relationship between observed matrices of genetic differentiation between individuals, and matrices of resistance distance was assessed through Mantel tests and redundancy discriminant analyses (RDA). A total of 628 individuals from 130 localities in 17 countries were genotyped at 7 microsatellite loci. Six population clusters were found based on a Bayesian analysis. The eastern margin of Dahomey Gap between East and West Africa was the main factor of genetic differentiation. The SDM projections at present, last interglacial and last glacial maximum periods were used for estimation of circuit resistance between locations of genotyped individuals. For all periods of time, when using either all individuals or only East-African individuals, partial Mantel r and RDA analyses conditioning on geographic distance were found significant. Under future projections (year 2080), partial r and RDA significance were different. From this study, it is concluded that analytical solutions provided by circuit theory are useful for the evolutionary management of populations and for phylogeographic analysis when coalescence times are not accessible by approximate Bayesian simulations.

opencc-zeroDec 2013View details →

zenodo32/100

Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions

This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.

opencc-by-4.0Jun 2021View details →

zenodo32/100

ROM data and code for Dakar Niño variability under global warming investigated by a high-resolution regionally coupled model

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2023View details →

zenodo32/100

Dataset of 'Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids'

As a dataset of the paper "Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids" , this dataset includes all configuration files of the idealized case and realistic test on the Arctic Ocean and the source code.

opencc-by-4.0Dec 2023View details →

zenodo32/100

Dataset for "Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric Coupling in the SKRIPS Model"

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →

zenodo32/100

Coupling deep learning and physically-based hydrological models for monthly streamflow predictions

Revision in journal Water Resources Research, Manuscript number: 2023WR035618R Abstract: This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically-based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model-simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) as the DL model, is applied to predict 1-, 3-, and 6-month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN-GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN-GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision-makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited. Keywords: monthly streamflow prediction; deep learning; physically-based distributed hydrological model; VIC model; soil moisture; hybrid model

opencc-by-4.0Dec 2023View details →

zenodo32/100

The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.

The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.

opencc-by-4.0Jan 2024View details →

zenodo32/100

input files for CROCO-WRF-OASIS3-MCT coupled model with a nest in CROCO

This tar file contains all the input files needed to run the coupled model CROCO-WRF with OASIS3-MCT coupler with one 2-way nested domain in CROCO. This example configuration is based on the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentationa nd tutorials for more details)

opencc-by-4.0Nov 2024View details →

zenodo32/100

input files for CROCO-WW3-WRF-OASIS3-MCT coupled model

<div>This tar file contains all the input files needed to run the coupled model CROCO-WW3-WRF with OASIS3-MCT coupler over an example configuration of the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentation and tutorials for more details)</div>

opencc-by-4.0Nov 2024View details →

zenodo32/100

Model data for: Upper-lower layer coupling of recurrent circulation patterns in the Gulf of Mexico

Post processed model output data for "Upper-lower layer coupling of recurrent circulation patterns in the Gulf of Mexico" submitted to Journal of Physical Oceanography. There are two datasets, one for the upper layer (H1) and one for the lower layer (H2). Each one contains the demeaned, detrended, and filtered (Lanczos low-pass) daily fields of layer thickness anomaly to which the authors computed the Hilbert EOFs. File list: H1_GoM_day_ssk15_st30dl.mat - this file contains the layer thickness anomaly fields for the upper layer (<250m) H2_GoM_day_ssk15_st30dl.mat - this file contains the layer thickness anomaly fields for the lower layer (>1000m) Scripts for plotting and processing the data into the model domain are available at: https://github.com/erickolvera/Olvera_et_al_21

opencc-by-4.0Oct 2021View details →

zenodo32/100

Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System

While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB. Licenses Different licenses apply to the various components of this data bundle (mostly attribution). For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a> Changelog 0.3.1 <ul> <li>Fix IRENASTAT encoding</li> </ul> Changelog 0.3.0 <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> Changelog 0.2.0 <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul>

openother-atApr 2022View details →

zenodo32/100

Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"

those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU).

opencc-by-4.0May 2022View details →

zenodo32/100

Dataset of "A hierarchy of global ocean models coupled to CESM1"

Data associated with the following publication: Hsu, T. Y., Primeau, F. W., & Magnusdottir, G. (2022). A Hierarchy of Global Ocean Models Coupled to CESM1. Paper Abstract: We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.   Methods Description of methods used for generation of data:  The data is generated with EMOM, a hierarchy of ocean models that are applied in CESM1. The detailed description of the model is in the paper the dataset is presented in (i.e. A Hierarchy of Global Ocean Models Coupled to CESM1). Methods for processing the data: This dataset consists of a set of atmospheric and oceanic fields produced by the NCAR CESM1 climate model. The data is in NETCDF format and has been post-processed and formatted using the NCO command language (see http://nco.sourceforge.net/ for more details). Software-specific information needed to interpret the data: The data is in NetCDF format. Usage Notes This README file was generated on 20200416 by Tien-Yiao Hsu Dataset of the paper A Hierarchy of Global Ocean Models Coupled to CESM1                      Email: tienyiah@uci.edu       OrcID: 0000-0002-8121-1525     Associate Contact Information       Name: Francois Primeau       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: fprimeau@uci.edu     Associate Contact Information       Name: Gudrun Magnusdottir       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: gudrun@uci.edu   3. Date of data organized : 20220201   4. Information about funding sources that supported the collection of the data:     Funder name: Department of Energy     Funder uri: https://www.energy.gov/   5. Contextual description of the data:          The data used to produce the figures in the paper.   -------------------------- SHARING/ACCESS INFORMATION --------------------------      Licenses/restrictions placed on the data:        CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL CC-BY   --------------------- DATA & FILE OVERVIEW ---------------------   We separate sets of data in terms of folders.    1. AMOC      This directory contains the AMOC streamfunction output from     simulations OGCM_CTL and OGCM_EXP.   2. hierarchy_statistics          This directory contains the statistics (mean, variability, ...)    and diagnosed quantities (ex: EOF, heat transport) of the hierarchy    output.      The output of CTL run of year 21 to 120 is in CTL_21-120.    The output of EXP run of year 81 to 180 is in EXP_81-180.   3. hierarchy_average      This directory is similar to is similar to hierarchy_statistics,     containing CTL and EXP. The difference is that it is the raw, unprocessed    mean data that contains the complete output variables.     -------------------------- METHODOLOGICAL INFORMATION --------------------------     1. Description of methods used for generation of data:       The data is generated with EMOM, a hierarchy of ocean models that is    applied in CESM1. The detail description of the model is in the paper    the dataset is preseted in (i.e. A Hierarchy of Global Ocean Models     Coupled to CESM1).   2. Methods for processing the data:      The output data is mostly the mean and variance of the climate variables.   3. Software-specific information needed to interpret the data:      The data is in NetCDF format.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: AMOC ---------------------------------------------   # Filename: MOC_[CTL|EXP].nc   Variable list:     1. MOC        Unit: Sv        The monthly mean value of streamfunction of the meridional overturning      circulation in ocean basins.     # Filename MOC_[CTL|EXP]_timeseries.nc   Variable list:     1. AMOC_max       Unit: Sv       The annual maximum value of the Atlantic Meridional Overturning Circulation.     2. AMOC_max_lat       Unit: degree north       The latitude of the location where AMOC_max occurs.     3. AMOC_max_z       Unit: m       The depth of the location where AMOC_max occurs.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_average ---------------------------------------------   In this directory, each sub-directory is of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. A sub-directory has three files: atm.nc, ocn.nc and ocn_regrid.nc.    atm.nc is the averaged data of atmosphere model output of year 21-121 of each model run on f09 grid. ocn.nc is the averaged data of ocean model output of year 21-121 of each model run on g16 grid. ocn_regrid.nc is the regrided version ocn.nc from grid g16 onto f09.   Details of the atm.nc variables can be found in CAM4 documentation https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html   Details of the ocn.nc variables can be found in POP2 documentation https://ncar.github.io/POP/doc/build/html/users_guide/model-diagnostics-and-output.html   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_statistics ---------------------------------------------   This directory conatins CTL and EXP runs folder where the statistics time is 21-121 for CTL and 81-180 for EXP.   Each experiment folder contains sub-directories of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. Each of these directories has the same analysis listed below.   # Filename: atm_analysis_[AAO|AO|ENSO|NAO|PDO].nc     Description: This file contains the derived climate variability patterns (i.e.                 AAO, AO, ENSO, NAO, PDO).      Variable list:         1. PCAs(modes, Ny, Nx)            Unit: None            The normalized PCAs. Different modes of the PCAs are separated according          to the first dimension.         2. PCAs_ts(time, modes)            Unit: None            The timeseries of projected PCAs onto the anomalies (i.e. the inner product of PCAs and anomalous fields).   # Filename: atm_analysis_mean_anomaly_[VARNAME].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [ICEFRAC|TAUX|TAUY|SST]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The mean values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.                    2. [VARNAME]_[TIMESCALE]A            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The anomalous values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.            3. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.          4. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = (N / m^2)^2                TAUY    = (N / m^2)^2                SST     = K^2            The variance of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.    # Filename: atm_analysis_mean_var_[T|U].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [T|U]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: T       = K                U       = m / s            The mean values of each grid point. TIMESCALE = [M|A] where M stands for monthly, A for annnual.          2. [VARNAME]_[TIMESCALE]ASTD            Unit: T       = K                U       = m / s            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.          3. [VARNAME]_[TIMESCALE]AVAR            Unit: T       = K                U       = m / s            The variance of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.    # Filename: atm_analysis_SST_CORR.nc     Description: This file contains the year-to-year correlation of monthly anomalous SST.     Variable list:         1. CORR(months, Ny, Nx)            Unit: None            The year-to-year correlation of monthly anomalous SST.   # Filename: ice_analysis_mean_anomaly_[aice|vice].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [aice|vice].                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for aice = None                The unit for vice = m   # Filename: ocn_analysis_mean_anomaly_STRAT.nc     Description: This file contains the mean, standard deviation of the denoted field.                STRAT is the difference of mean ocean temperatures T_top - T_bot.                T_top is the mean temperature of the top 50m of the ocean where as                T_bot is the mean temperature of the ocean between depth 50m to 503.7m.                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for STRAT = K   # Filename: atm_analysis_AHT_OHT.nc     Description: This file contains the indirectly derived atmosphere heat transport.      Variable list:         1. AHT(time, lat_bnd)            Unit: W            The monthly atmospheric heat transport.         2. AHT_AM(year, lat_bnd)                     Unit: W                    The annual atmospheric heat transport.         3. AHT_MEAN(lat_bnd)                     Unit: W                    The time-averaged atmospheric heat transport.         4. AHT_TFLX_CONV(time, lat)                     Unit: W / m                    The monthly-zonally-averaged atmospheric heat convergence.         5. AHT_TFLX_CONV_MEAN(lat)                               Unit: W                    The time-zonally-averaged atmospheric heat convergence.   # Filename: ocn_analysis_OHT.nc     Description: This file contains the derived ocean heat transport.      Variable list:         1. ADVT(time, lat)            Unit: K / s / m^3            The monthly vertically-integrated temperature tendency due to advection and horizontal diffusion.         2. ADVT_MEAN(lat)            Unit: K / s / m^3            The time-averaged ADVT.         3. OHT(time, lat_bnd)            Unit: W            The total monthly ocean heat transport.         4. OHT_MEAN(lat_bnd)            Unit: W            The time-average of OHT.         5. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         6. OHT_ADVT_MEAN(time, lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.         7. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         8. OHT_ADVT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.           9. OHT_WKRSTT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to weak-restoring.         10. OHT_WKRSTT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_WKRSTT.         11. SHF(time, lat)            Unit: W            The monthly surface heat flux.         12. SHF_MEAN(lat)            Unit: W            The time-average of SHF.         13. WKRSTT(time, lat)            Unit: K / s / m^2            The vertically integrated monthly weak-restoring.         14. WKRSTT_MEAN(lat)            Unit: W            The time-average of WKRSTT.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/importance_of_KH ---------------------------------------------   This directory conatins the average of CAM4 and EMOM output of field during year 21-30.   The meaning of the variable can be found in official website https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_mean_temp ---------------------------------------------   This directory conatins the annual average of ocean mean temperature of the top 33 layers (507.33m) in the EXP run (sea-ice loss run)   # Filename: paper2021_[MODEL_NAME]_EXP.ocn_mean_T.nc     Description: This file contains the annual average of ocean mean temperature of the top 33 layers (507.33m).        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.           2. SALT(time, Nz)            Unit: kg / m^3 (PSU)                    Ocean salinity.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_heat_content_trend ---------------------------------------------   This directory conatins the difference of the ocean state between the year 181 and year 81 of the EXP run.   # Filename: OHC_diff_[MODEL_NAME].nc     Description: The difference of the ocean state between the year 181 and year 81 of the EXP run.        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/vice_target_file ---------------------------------------------   This directory conatins the sea-ice forcing used to derive Q-flux (CTL) and the forcing applied in EXP run.    # Filename: forcing.vice.[GRID].paper2021_[RUN]_POP2.nc     Description: This the sea-ice forcing used in the [RUN] in the grid of [GRID].                GRID = [f09|gx1v6]                [RUN] = [CTL|EXP]        Variable list:         1. vice_target(time, nlat, nlon)            Unit: m^3 / m^2 (volume density)                    Total ice volume.

opencc-by-4.0Jun 2022View details →

zenodo32/100

Improved boundary conditions for coupled geospace models: an application in modeling spacecraft surface charging environment

These are simulation results from running the RAM-SCB model coupled with the BATS-R-US model via the Space Weather Modeling Framework (SWMF). Two simulations are conducted. One named as "Simulation I" contains simulation outputs from using the traditional outer boundary conditions for the RAM-SCB (i.e., assuming a Kappa distribution with MHD parameters obtained from the BATS-R-US model). The other one named as "Simulation II" contains simulation output from using the new outer boundary conditions for the RAM-SCB (i.e., combining the Denton's empirical electron flux distribution (E<40 keV) with the MHD-parameterized Kappa distribution).  In "simulation_data.zip", three types of simulation results are included:  <ul> <li>"BC_simulation_?.zip": the outer boundary conditions of the electron flux at 6.5 Re at five different times (hour=6, 7, 10, 14, 19)</li> <li>"ram_e_d20130317_flux_simulation_?.zip": the differential electron flux in the equatorial plane within 6.5 Re during the March 17, 2013 event</li> <li>"RBSPB20130317.nc": the differential electron flux, and other parameters (ion flux, magnetic fields) along the RBSP-B trajectory.</li> </ul> In "simulation20180410_alongRBSPa.zip", model results during the storm of 2018/04/10 are saved, including the differential electron flux and other parameters (ion flux, magnetic fields) along the RBSP-A trajectory. Both types of simulations are conducted. In "simulation20180826_alongRBSPa.zip", model results during the storm of 2018/08/25-26 are saved, including the differential electron flux, and other parameters (ion flux, magnetic fields) along the RBSP-A trajectory. Both type of simulations are conducted.

opencc-by-4.0Oct 2021View details →

zenodo32/100

Wave-current Coupling Effects on the Variation Modes of Pore Pressure Response in a Sandy Seabed: Physical Modeling and Explicit Approximations

This is experimental data of combined wave-current induced pore pressure. The corresponding test condition is given in the title of each excel. The channels 4, 2, 1 represent the wave height data measured by the WHGs just above PPTs, in the upstream, and in the downstream, respectively. The channels 6, 8, 3, 7 are pore pressure data monitored by PPTs installed at 0, 6, 9, and 15 cm below the seabed surface, respectively.

opencc-by-4.0Aug 2022View details →

zenodo32/100

Dataset for "Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model" by Kim et al., 2022 in Geophysical Research Letters

Kiel Climate Model pre-industrial simulation data used in the Geophysical Research Letters publication titled “Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model” by Kim et al., 2022

opencc-by-4.0Sep 2022View details →

zenodo32/100

Golgi cell gap junction coupling in cerebellum cortex model WT and KO conditions

Golgi cell gap junction coupling in cerebellum cortex model WT and KO conditions

opencc-by-4.0Oct 2022View details →

zenodo32/100

Gap junction coupling of Golgi cells in cerebellar cortex model

Gap junction coupling of Golgi cells in cerebellar cortex model

opencc-by-4.0Oct 2022View details →

zenodo32/100

Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"

<div> This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"   </div>

opencc-by-4.0May 2024View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record