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.
364
datasets available to search
ShareScore release 0.9.0
Dataset results
364 results for “Convection”
Dataset for paper "Investigation of convective transport in the so-called 'gas diffusion layer' used in polymer electrolyte fuel cell"
<p>Dataset containing all data for figures and supplemental material for the paper:<br /> Investigation of convective transport in the porous media of a fuel cell-like system</p> <p>O. Beruski, T. Lopes, A. R. Kucernak and J. Perez.</p>
Data for GRL Article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model"
This repository contains mandatory material to reproduce the results of the GRL article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model" [Paper #2021GL093265RR].
Aquaplanet simulations using CAM5.4-MPAS4 with two different deep convection schemes
This dataset includes model output from aquaplanet simulations using the Communitiy Atmosphere Model (CAM) version 5.4 with the nonhydrostatic Model for Prediction Across Scales (MPAS) version 4 dynamical core. The aquaplanet simulations has been performed with a global quasi-uniform resolution mesh with ~120 km grid spacing and a variable resolution mesh employing a circular refined region with ~30 km grid spacing centering at the equator and ~120 km elsewhere. Specifically, these simulations are produced for evaluating the Grell-Freitas deep convection scheme (GF), which has been implemented in the CAM5.4 recently, for the quasi-uniform resolution and the variable resolution meshes, compared with the Zhang-McFarlane scheme deep convection scheme (ZM) in CAM. The four simulations are run for three years, but the datasets are only for the last 30 months because the first six months are regarded as spinup. The model output was interpolated to a 1-degree by 1-degree latitude-longitude grid. Details of the simulations will be documented in a manuscript submitted to the Journal of Advances in Modeling Earth Systems (JAMES). The dataset will be shared with readers without restrictions by the journal's data policy.
Input files and movie visualizations for convection models discussed in Becker and Fuchs, "Generation of evolving plate boundaries and toroidal flow from visco-plastic damage-rheology mantle convection and continents", manuscript revised for G-Cubed
<p>These input files are for the CitcomS software as available on github.com/geodynamics/citcoms and used in the version under commit 2bda530. They can be used to recreate the models discussed in Becker and Fuchs (revised manuscript submitted to G-Cubed, 11/2023), with model codes discussed and listed in Table 1 of the preprint as provided here. We also provide selected animations of the time dependence of model output, referenced to the same model names.</p>
Inlist and Source Code Files for "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses"
<p>MESA (r12778) and GYRE (version 6.0) inlist files used in the work described in "Fossil Signatures of Main-sequence Convective Core Overshoot Estimated through Asteroseismic Analyses".</p><p>Two subdirectories are provided in the archive:</p><p>1) The directory called "inlists" contains different MESA inlist files for different evolutionary period (pms=pre main sequence, ms=main sequence, and rgb=red giant branch) of the stellar model. The file named "inlist_0all" is applied to all evolutionary periods. The different inlist files for the different evolutionary states are called by putting their names in the "inlist" file, for example the include file named "inlist" evolves a MESA model from the pre main sequence until ZAMS (using the stop_near_zams = .true. option in the "inlist_1pms" file). An example gyre (version 6.0) inlist is also included in the "inlists" subdirectory. The Python scripts used to evaluate the matrix elements (which are used to determine the dipolar mixed-mode frequencies) discussed in this work are available at https://gitlab.com/darthoctopus/mesatricks. </p><p>2) Custom stopping conditions (used for stopping a model before the red giant branch) as well as a custom diffusion cutoff (see Viani et al. 2018, ApJ, 858, 28) are included in the run_star_extras.f file in the "src" subdirectory. </p>
Data-driven parametrisation for Rayleigh-Bénard convection [DATASET 3/3]
<p>This is dataset 3 of 3 associated with an Honours thesis submitted by Thomas Schanzer at the University of New South Wales in November 2023.</p>
Data-driven parametrisation for Rayleigh-Bénard convection [DATASET 1/3]
<p>This is dataset 1 of 3 associated with an Honours thesis submitted by Thomas Schanzer at the University of New South Wales in November 2023.</p>
Characterizing the complexity of subduction zone flow with an ensemble of multiscale global convection models
<p>Parameter files and model input .txt files for ASPECT mantle convection simulations.</p>
Open Research Datasets – Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America
<p>Data supporting the findings of "Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America" by Victoria M. Fernandes, Gareth G. Roberts and Fred D. Richards. Further details about these data can be found in the main manuscript and accompanying Supporting Information. This research was funded by NERC Large Grant MC2: Mantle Convection Constrained (NE/T012595/1). <br><br>The datasets contained in this repository:<br>1) Uplift constraints from youngest outcropping marine to terrestrial stratigraphic transitions, as described in Fernandes et al. (2019) JGR Earth Surface (Figure 1; MarineTerrestrialTransition_Fernandes_etal2019.txt)<br>2) Digitised sediment isopachs from Roberts & Kirschbaum (1995), USGS Professional Paper 1561 (Supplementary Figure S3; Roberts_Kirschbaum_1995_isopachs.zip)<br>3) Air-loaded subsidence grids (Supplementary Figure S4; Subsidence.zip)<br>4) Temperature grids at 25 km depth intervals, from 50–400 km depth, generated from the SLNAAFSA hybrid Vs model of Hoggard et al. (2020), Nature Geoscience (Supplementary Figure S6; Temperature_grids_txt.zip)</p>
Data and figures of SuperDARN convection maps
<p>This dataset includes SuperDARN convection maps for the analysis of the 2016-03-11 event. The convection maps are generated through the following steps:</p> <p>In the first step, we process SuperDARN RAWACF files from all available radars in the Northern Hemisphere using the make_fit routine. The resulting fitacf files contain key physical properties of SuperDARN backscatter, including power, velocity, spectral width, elevation, and associated errors. To enhance the quality of the fitacf files, we employ the fit_speck_removal routine, which effectively eliminates noise and despecks the files. </p> <p>In the second step, we generate grid files from the fitacf files. These grid files comprise geo-magnetically located line-of-sight velocity vectors. The make_grid routine is employed with default options to create grid files for all available radars in the northern hemisphere during the specified event intervals. Subsequently, we merge these individual grid files into a consolidated file using the combine_grid routine. </p> <p>In the third step, we utilize the MAP POTENTIAL packet to generate the convection map from the grid file. The map_grd routine is used to reformat the grid file into a cnvmap format file, using the default option of AACGM-v2. The map_addimf routine is used to add solar wind OMIN data to the map file. Additionally, the map_addmodel routine calculates the TS18 statistical model (Thomas and Shepherd, 2018), incorporating it into the convection map file with default options. Finally, the map_fit routine, with default options, performs spherical harmonic fitting to generate the final convection map file.</p> <p>Reference:<br>Thomas, E. G., and S. G. Shepherd (2018), Statistical patterns of ionospheric convection derived from mid-latitude, high-latitude, and polar SuperDARN HF radar observations, J. Geophys. Res. Space Physics, 123, 3196-3216, doi:10.1002/2018JA025280. </p>
Data used in the study titled "Significant Tornado Environments In Canada Using ERA5-Derived Convective Parameters"
<p>These data include: (1) Observation-derived convective parameters at four Canadian sounding stations based on 1990-2020 data, and ERA5-derived convective parameters at grid points nearest to the same four Canadian sounding stations on 1990-2020 data - file called "ERA5 and Observation convective parameters.zip". (2) ERA5 vertical profile data, derived convective parameters, and skew-t images for the 166 Canadian F/EF2+ tornado events - file called "ERA5 profiles-parameters-skewt 166 cases.zip"</p>
Data of two-cell convection
<p>This archive contains input and output data extracted from the PPMLR-MHD simulations of the March 11, 2016 event. The files are:</p> <p>(a) Solar wind data files and the input file of the PPMLR-MHD code in the ‘solar_wind’ folder, which contains 5 files:</p> <p>(1) ‘B_clip.txt’: the magnetic field data;</p> <p>(2) ‘Ni_clip.txt’: the solar wind density data;</p> <p>(3) ‘Vi_clip.txt’: the solar wind speed data;</p> <p>(4) ‘Vthi_clip.txt’: the thermal velocity data;</p> <p>(5) ‘160311T1055_140m_wm’: the input file of the PPMLR-MHD code.</p> <p>(b) Data extracted from the output files of PPMLR-MHD simulations on March 11, 2016. The files are:</p> <p>(b1) The 'Tbhd_data' folder contains 51 sub-folders, with each folder representing one minute. The folder with the suffix '0000' represents the timestamp ‘12:20 on March 11, 2016’. Each 'Tbhdxxxx' sub-folder contains 7 files: </p> <p>(1) ‘key_parameter.mat’: key parameters in the magnetosphere;</p> <p>(2) ‘magnetopause_z0.mat’: the position of the magnetopause determined by open and closed magnetic field lines at the equatorial plane;</p> <p>(3) ‘magpause_p_z0.mat’: the position of the magnetopause determined by the pressure gradient at the equatorial plane;</p> <p>(4) ‘magpause_rho_z0.mat’: the position of the magnetopause determined by the density gradient at the equatorial plane;</p> <p>(5) ‘MI_coord_z0.mat’: the grid points on the equatorial plane and their corresponding magnetic latitude values;</p> <p>(6) ‘vazm.mat’: azimuthal velocity;</p> <p>(7) ‘xyz.mat’: the xyz coordinates of grid points in the magnetosphere. </p> <p>(b2) The 'Tbhp_data' folder contains 51 sub-folders, with each folder representing one minute. The folder with the suffix '0000' represents the timestamp ‘12:20 on March 11, 2016’. Each 'Tbhpxxxx' sub-folder contains 4 files:</p> <p>(1) ‘facn.mat’: field-aligned current in the northern hemisphere;</p> <p>(2) ‘jt.mat’: current on ionosphere and the corresponding location; </p> <p>(3) ‘key_parameter.mat’: key parameters in the ionosphere;</p> <p>(4) ‘xyz.mat’: the grid points in the ionosphere.</p>
Animation, MESA inlists, and post-processing package for "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants"
<p>Videos, post-processing package for mock ALMA observations, MESA (r23.05.1 with mesasdk-x86_64-linux-23.7.3) inlists and history files, Jupyter notebook and data to reproduce figures of the paper "Is Betelgeuse really rotating? Synthetic ALMA observations of large-scale convection in 3D simulations of Red Supergiants".</p>
Mantle convection promotes abnormal acceleration of the "Tethys Train"
<p>Table S1 Zircon Hf isotopes of the Tethyan Realm</p> <p>Table S2 Whole-rock Nd isotopes of the Tethyan Realm</p> <p>Table S3 Zircon O isotopes of the Tethyan Realm</p> <p>Table S4 Whole-rock analysis of basalts from the Tethyan Realm</p> <p>Table S5 Key paleomagnetic data of Indochina, North Qiangtang, South China</p> <p>Table S6 Major and Trace elements of samples</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
Software file and numerical results of Modelling heat transfer for assessing the convection length in ventilated caves
<p>The Comsol file corresponding to the reference case as shown in Figures 4-6 as well as all the numerical results for the rest of the figures are available here.</p>
Supporting data for "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades" (previously for ch. 5 of "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer")
<p>This contains both the data and scripts required to produce the figures in the preprint "Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades". The scripts labeled 1-5 produce the main figures; the other scripts produce supporting data or figures.</p> <p>Earlier versions of this dataset contained the scripts and data supporting Ch. 5 of the PhD thesis "Mesoscale Cloud Patterns in the Trade-Wind Boundary Layer". The scripts labeled 1-5 produce the main figures; the other scripts produce either the underlying data, or supporting figures (prefix S). </p>
Multi-LES code output of Flower-type organized convection on 02 February, 2020 - 0-1D Files
Open the record for dataset details and reuse information.
Supporting data for: "Weakening of the AMOC and Strengthening of Labrador Sea Deep Convection in Response to External Freshwater Forcing"
<p>This repository contains the key supporting data (in the netcdf format) for the following paper:<br>Wei, X., Zhang, R. Weakening of the AMOC and strengthening of Labrador Sea deep convection in response to external freshwater forcing. <em>Nat Commun</em> <strong>15</strong>, 10357 (2024). https://doi.org/10.1038/s41467-024-54756-3</p> <p>In this study, control and water hosing ensembles are conducted using a coupled climate model (GFDL CM4) with an eddy-permitting ocean component. The anomaly is defined as the difference between the water hosing and control ensembles (anomaly = water hosing - control). This paper investigates mechanisms of the AMOC weakening and its subsequent impact on the Labrador Sea open-ocean deep convection in response to external freshwater forcing.</p> <p><strong>Descriptions of data files in this repository:</strong></p> <p><strong>Main figures:</strong></p> <p>1. The anomalies of the OSNAP AMOC and the Labrador Sea March mixed layer depth (MLD), as shown in Fig. 1 in the paper. </p> <p>The anomalies of the maximum AMOC and the AMOC at a relatively dense level around sigma0=27.84 kg/m3 across the entire OSNAP section in density space:</p> <p>Anomaly_AMOC_max_OSNAP.nc</p> <p>Anomaly_AMOC_denser_OSNAP.nc</p> <p>The anomalies of the maximum AMOC across OSNAP West and OSANP East in density space:</p> <p>Anomaly_AMOC_max_OSNAP_WEST.nc</p> <p>Anomaly_AMOC_max_OSNAP_EAST.nc</p> <p>The March mixed layer depth (MLD) in the Labrador Sea:</p> <p>Anomaly_MLD_003_March_Labrador.nc</p> <p>The spatial map of March MLD climatology and anomaly:</p> <p>Control_MLD_003_March_spatial.nc</p> <p>Anomaly_MLD_003_March_spatial.nc</p> <p>2. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction across the OSNAP section, in density-space and depth-space, as shown in Fig. 2 in the paper.</p> <p>OSNAP West:</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_z_OSNAP_WEST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_WEST.nc</p> <p>WaterHosing_moc_z_OSNAP_WEST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_WEST.nc</p> <p>Anomaly_moc_z_OSNAP_WEST.nc</p> <p>OSNAP East:</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_z_OSNAP_EAST.nc</p> <p>WaterHosing_moc_sigma0_OSNAP_EAST.nc</p> <p>WaterHosing_moc_z_OSNAP_EAST.nc</p> <p>Anomaly_moc_sigma0_OSNAP_EAST.nc</p> <p>Anomaly_moc_z_OSNAP_EAST.nc</p> <p>Entire OSNAP section:</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p>Control_moc_z_OSNAP.nc</p> <p>WaterHosing_moc_sigma0_OSNAP.nc</p> <p>WaterHosing_moc_z_OSNAP.nc</p> <p>Anomaly_moc_sigma0_OSNAP.nc</p> <p>Anomaly_moc_z_OSNAP.nc</p> <p>3. The climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, and potential density across the OSNAP section, as shown in Fig. 3 in the paper.</p> <p>Control_salinity_OSNAP.nc</p> <p>Control_temperature_OSNAP.nc</p> <p>Control_sigma0_OSNAP.nc</p> <p>Anomaly_salinity_OSNAP.nc</p> <p>Anomaly_temperature_OSNAP.nc</p> <p>Anomaly_sigma0_OSNAP.nc</p> <p>4. The sigma-z diagram of climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC transport across OSNAP East, i.e. integrated volume transport across OSNAP East over each potential density bin and depth bin, as shown in Fig. 4 in the paper.</p> <p>Control_SigmaZ_OSNAP_EAST.nc</p> <p>WaterHosing_SigmaZ_OSNAP_EAST.nc</p> <p>Anomaly_SigmaZ_OSNAP_EAST.nc</p> <p>5.The deep ocean potential density anomalies along with its thermal and haline components over the west boundary and eastern regions of the OSNAP East subsection, and the AMOC anomalies at a relatively dense level around sigma0=27.84 kg/m3 across OSNAP East in density space, as shown in Fig. 5 in the paper.</p> <p>Anomaly_sigma_west.nc</p> <p>Anomaly_sigmaS_west.nc</p> <p>Anomaly_sigmaT_west.nc</p> <p>Anomaly_sigma_east.nc</p> <p>Anomaly_sigmaS_east.nc</p> <p>Anomaly_sigmaT_east.nc</p> <p>Anomaly_sigma_diff.nc</p> <p>Anomaly_sigmaS_diff.nc</p> <p>Anomaly_sigmaT_diff.nc</p> <p>Anomaly_AMOC_denser_OSANP_EAST.nc</p> <p>6.The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at the upper ocean (413m), as shown in Fig. 6 in the paper, and at the deep ocean (2250m), as shown in Fig. 7 in the paper.</p> <p>Anomaly_FWF_salt_413m_10yr.nc</p> <p>Anomaly_FWF_dye_413m_10yr.nc</p> <p>Anomaly_FWF_diff_413m_10yr.nc</p> <p>Anomaly_FWF_salt_413m_20yr.nc</p> <p>Anomaly_FWF_dye_413m_20yr.nc</p> <p>Anomaly_FWF_diff_413m_20yr.nc</p> <p>Anomaly_FWF_salt_413m_30yr.nc</p> <p>Anomaly_FWF_dye_413m_30yr.nc</p> <p>Anomaly_FWF_diff_413m_30yr.nc</p> <p>Anomaly_FWF_salt_413m_40yr.nc</p> <p>Anomaly_FWF_dye_413m_40yr.nc</p> <p>Anomaly_FWF_diff_413m_40yr.nc</p> <p>Anomaly_FWF_salt_413m_50yr.nc</p> <p>Anomaly_FWF_dye_413m_50yr.nc</p> <p>Anomaly_FWF_diff_413m_50yr.nc</p> <p>Anomaly_FWF_salt_2250m_10yr.nc</p> <p>Anomaly_FWF_dye_2250m_10yr.nc</p> <p>Anomaly_FWF_diff_2250m_10yr.nc</p> <p>Anomaly_FWF_salt_2250m_20yr.nc</p> <p>Anomaly_FWF_dye_2250m_20yr.nc</p> <p>Anomaly_FWF_diff_2250m_20yr.nc</p> <p>Anomaly_FWF_salt_2250m_30yr.nc</p> <p>Anomaly_FWF_dye_2250m_30yr.nc</p> <p>Anomaly_FWF_diff_2250m_30yr.nc</p> <p>Anomaly_FWF_salt_2250m_40yr.nc</p> <p>Anomaly_FWF_dye_2250m_40yr.nc</p> <p>Anomaly_FWF_diff_2250m_40yr.nc</p> <p>Anomaly_FWF_salt_2250m_50yr.nc</p> <p>Anomaly_FWF_dye_2250m_50yr.nc</p> <p>Anomaly_FWF_diff_2250m_50yr.nc</p> <p>7.Climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, potential density and zonal velocity along the Iceland-Scotland Overflow pathway, as shown in Fig. 8 in the paper.</p> <p>Control_salinity_ISOW.nc</p> <p>Control_temperature_ISOW.nc</p> <p>Control_sigma_ISOW.nc</p> <p>Control_u_ISOW.nc</p> <p>Anomaly_salinity_ISOW.nc</p> <p>Anomaly_temperature_ISOW.nc</p> <p>Anomaly_sigma_ISOW.nc</p> <p>Anomaly_u_ISOW.nc</p> <p>8.The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the Iceland-Scotland Overflow section, as shown in Fig. 9 in the paper.</p> <p>Anomaly_FWF_salt_ISOW.nc</p> <p>Anomaly_FWF_dye_ISOW.nc</p> <p>Anomaly_FWF_diff_ISOW.nc</p> <p><strong>Supplementary figures:</strong></p> <p>S1. The anomalies of the density-space AMOC streamfunction as shown in Supplementary Fig. 1 in the paper.</p> <p>SuppFig1.nc</p> <p>S2. The extra-tropical North Atlantic subsurface (413m) temperature anomalies as shown in Supplementary Fig. 2 in the paper.</p> <p>SuppFig2.nc</p> <p>S3. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction, surface forced water mass transformation (WMTS) and interior mixing forced water mass transformation (WMTM), as shown in Supplementary Fig. 3 in the paper.</p> <p>SuppFig3_streamfunction.nc</p> <p>SuppFig3_surfaceWMT.nc</p> <p>SuppFig3_interiorWMT.nc</p> <p>SuppFig3_mask_section.nc</p> <p>S4. The climatological mean of the velocity across the OSNAP section in the control ensemble, as shown in Supplementary Fig. 4 in the paper.</p> <p>SuppFig4.nc</p> <p>S5. The transient salinity anomalies, potential temperature anomalies and potential density anomalies across the OSNAP section, as shown in Supplementary Fig. 5 in the paper.</p> <p>SuppFig5_11_20yr.nc</p> <p>SuppFig5_21_30yr.nc</p> <p>SuppFig5_31_40yr.nc</p> <p>SuppFig5_41_50yr.nc</p> <p>S6. The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at 625m, as shown in Supplementary Fig. 6 in the paper.</p> <p>SuppFig6_10yr.nc</p> <p>SuppFig6_20yr.nc</p> <p>SuppFig6_30yr.nc</p> <p>SuppFig6_40yr.nc</p> <p>SuppFig6_50yr.nc</p> <p>S7. The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the OSNAP section, as shown in Supplementary Fig. 7 in the paper.</p> <p>SuppFig7.nc</p> <p>S8. The anomalies of March Labrador Sea mixed layer depth (MLD), vertical potential density difference, surface and deep ocean potential density, as shown in Supplementary Fig. 8 in the paper.</p> <p>SuppFig8.nc</p> <p>S9. The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer, and the inverse horizontal grid resolution of GFDL CM4 (this study) and CMIP6 models as shown in Supplementary Fig. 9 in the paper. The CMIP6 model data were downloaded from https://aims2.llnl.gov/search/cmip6/.</p> <p>The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer in WOA18 as shown in Supplementary Fig. 9 in the paper. The WOA18 data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/.</p> <p>SuppFig9.nc</p> <p>S10. The supplementary Fig. 10 shares the same data of climatological mean of the AMOC streamfunction across the OSNAP section in density-space in the control ensemble with Figure 2 in the paper. The OSNAP observation data were downloaded from www.o-snap.org.</p> <p>Control_moc_sigma0_OSNAP_WEST.nc</p> <p>Control_moc_sigma0_OSNAP_EAST.nc</p> <p>Control_moc_sigma0_OSNAP.nc</p> <p><strong>Acknowledgments</strong></p> <p>We acknowledge the use of the following datasets and model code in this study: The World Ocean Atlas 2018 (WOA18) data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/. The Data from the OSNAP (Overturning in the Subpolar North Atlantic Program) array were downloaded from https://www.o-snap.org/. OSNAP data were collected and made freely available by the OSNAP project and all the national programs that contribute to it (www.o-snap.org). The CMIP6 (Coupled Model Intercomparison Project Phase 6) model data were downloaded from https://aims2.llnl.gov/search/cmip6/. The source code of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model version 4 (CM4) is publicly available at https://doi.org/10.5281/zenodo.3339397. The surface forced water mass transformation (WMTS) is calculated using the source code developed by Drake et al. 2024 at https://github.com/hdrake/xwmt. The relevant citations for the above datasets and model code are listed in Wei and Zhang 2024.</p>
Simulation data for: 'Convective shutdown in the atmospheres of lava worlds'
<p>PROTEUS model outputs for HD 63433 d and TRAPPIST-1 c simulations, for MNRAS article.</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.