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.
226
datasets available to search
ShareScore release 0.7.1
Dataset results
226 results for “intercomparison”
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw
<p>Dataset (.csv files) and software (python scripts) for generating figures, including data analysis, in our manuscript "A two-year intercomparison of CW focusing wind lidar and tall mast wind measurements at Cabauw", submitted to Atmos. Meas. Tech.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing
<p>Simulation output data from COSMO and IFS used to produce the figures in the model intercomparison paper (https://doi.org/10.5194/gmd-2021-31), as well as the data used for initializing the soil in COSMO.</p> <p>The output data is partitioned into different parts:</p> <ol> <li>cosmo_hordiff.tar:<br> Model output from COSMO for the horizontal diffusion experiment.</li> <li>cosmo_standard.tar:<br> Model output from COSMO for the standard experiment.</li> <li>ifs_standard.tar:<br> Model output from IFS for the standard experiment.</li> <li>soil_cosmo_ini_vergara2021_avg_mayjune_12km.nc:<br> Initial conditions for the soil model used in COSMO.</li> </ol>
Intercomparison data for ARM near-surface turbulent fluxes at Fermilab and SGP
<p>FERMI_ECORSF.csv provides the ARM ECORSF prototype data while deployed at US-IB2</p> <p>Ref-EC-b1-[SITE #].xlsm provide the roving portable eddy covariance system data deployed at [SITE #]</p> <p> </p>
GABLS4, snow model intercomparison.
<p>Content of the archive:</p> <p><strong>FORCING</strong>: The near-surface variable forcing dataset used to drive the offline simulations. Time step of 30 minutes, start date 1/12/2009, 15 days.</p> <p><strong>SIMULATIONS</strong>: NetCDF output files from participating models.</p> <p><strong>OBSERVATIONS</strong>: Surface temperature observation time series and observations of snow temperature in the snowpack at different depths used for the validation.</p> <p><strong>FIGURES</strong>: The datasets and the python scripts used to prepare the figures.</p>
Iron model intercomparison project muti model dissolved iron data
<p>Dataset here includes the dissolved iron multi model mean and variance from the models used in :</p> <p>Tagliabue, A., et al. (2016), How well do global ocean biogeochemistry models simulate dissolved iron distributions?, Global Biogeochemical Cycles, doi:10.1002/2015gb005289.</p> <p>Average and variance calculated using ferret @ave and @var transforms in ferret (https://ferret.pmel.noaa.gov/Ferret)</p> <p>netcdf format and information on the grid is found in the above manuscript</p> <p>Prepared as part of SCOR Workig Group 151 FeMIP</p>
Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models
<p>This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".</p>
Dataset for "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements"
<p>The final dataset used in manuscript "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements" by Kohonen et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as GPP estimates derived from COS and CO2 flux measurements as described in the manuscript from January 2013 to December 2017. Raw data are available upon request from the author.</p>
ISIMIP 3b, Land Use Models outputs intercomparison
<p>These data sets contain the information used to compare the land use models' outputs submitted as human forcings for ISIMIP3b and utilized for different impact analyses and teams under various scenarios (SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5). </p>
Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic"
<p>Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic", accepted for publication in Atmospheric Chemistry and Physics, 2018, same authors. The intercomparison includes output from three large-eddy simulation models (UCLALES-SALSA, MIMICA, and COSMO-LES) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM) for a case study of high-Arctic tenuous cloud based on observations from the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign. See publication for details. The discussion preprint for peer review can be found at https://doi.org/10.5194/acp-2017-1128.</p>
Model intercomparison of medicane Ianos using ten mesoscale numerical frameworks
<p>The dataset provides numerical simulations of the high-impact medicane Ianos of September 2020. It is based on a collective effort with five mesoscale models to look for a robust response among ten numerical frameworks used in the community involved in the networking activity of the EU COST Action "MedCyclones" <a href="https://medcyclones.eu/">https://medcyclones.eu/</a></p> <p>The five mesoscale models are:</p> <ul> <li>The BOLAM hydrostatic model and the MOLOCH non-hydrostatic, fully compressible model developed at CNR-ISAC available upon request to <a href="mailto:dinamica@isac.cnr.it">dinamica@isac.cnr.it</a></li> <li>The Met Office Unified Model (MetUM) available for use under a closed licence agreement, further information at <a href="http://www.metoffice.gov.uk/research/modelling-systems/unified-model">http://www.metoffice.gov.uk/research/modelling-systems/unified-model</a></li> <li>The Meso-NH mesoscale non-hydrostatic model of the French research community freely available under CeCILL-C license agreement on <a href="http://mesonh.aero.obs-mip.fr">http://mesonh.aero.obs-mip.fr</a> with two variants included: <ul> <li>one run at Centre National de Recherches Météorologiques (MESONH-CNRM)</li> <li>one run at Laboratoire d’Aérologie (MESONH-LAERO)</li> </ul> </li> <li>The WRF (Weather Research and Forecasting) non-hydrostatic, fully compressible model freely available at <a href="https://github.com/wrf-model/WRF/releases">https://github.com/wrf-model/WRF/releases</a> with five variants included: <ul> <li>one run at the Aristotle University of Thessaloniki (WRF-AUTH)</li> <li>two run at CNR-ISAC (WRF-ISAC and WRF-ISAC-2)</li> <li>one run at the National Observatory of Athens (WRF-NOA)</li> <li>one run at the University of the Balearic Islands (WRF-UIB)</li> </ul> </li> </ul> <p>Four sets of simulations are provided:</p> <ul> <li>Control simulations obtained by initialising the models at 00 UTC on 15 September 2020 and using 6-h operational analyses from the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) as initial and lateral boundary conditions. The horizontal grid spacing is set to 10 km, which approximately matches the resolution of IFS analyses and requires parameterization of deep convection.</li> <li>A first sensitivity test obtained by initialising the models 12 h earlier at 12 UTC on 14 September 2020.</li> <li>A second sensitivity test obtained by using ECMWF Reanalysis v5 (ERA5), which provides higher frequency (hourly) but lower spatial resolution (about 30 km), as initial and lateral boundary conditions.</li> <li>A third sensitivity test obtained by setting the horizontal grid spacing to 2 km, which allows explicit representation of deep convection.</li> </ul> <p>The model output is stored every 3 h until 00 UTC 20 September 2020 and interpolated onto the same regular 0.1°×0.1° horizontal grid and pressure levels. The data files are formatted in Network Common Data Form (NetCDF) and named <strong>runs_ILBC_DDHH_RES.nc</strong> where</p> <ul> <li><strong>ILBC</strong> describes the initial and lateral boundary conditions (IFS or ERA5) </li> <li><strong>DDHH</strong> describes the initial day and hour (1500 or 1412) </li> <li><strong>RES</strong> describes the horizontal grid spacing (10 or 2 km)</li> <li>simulated infrared brightness temperatures are provided in extra files with <strong>RTTOV</strong> suffix for five of the models and variants</li> </ul>
Fossil Fuel CO₂ Emissions for the OCO2 Model Intercomparison Project (MIP)
<p>These are fossil CO<sub>2</sub> fluxes updated through August 2024 for atmospheric CO<sub>2</sub> modeling. They were constructed primarily to be used for the OCO2 Model Intercomparison Project (MIP).</p> <ul> <li>For 2000-2022, they're based on <a href="https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2023.html">ODIAC 2023</a>, which in turn uses BP's energy use statistics for 2021 and 2022.</li> <li>ODIAC monthly emissions have been disaggregated to hourly using the TIMES emission factors for day of week and time of day (<a href="https://urldefense.us/v3/__https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196__;!!PvBDto6Hs4WbVuu7!YsQP_T-Vf3Fv83toql-90HY0NO5e92fR0D9kAi10tTzUd0Ugum9d3CTUMBp22qA0M-vYoU_fvd4$">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196</a>).</li> <li>For 2023 onwards, ODIAC's 2022 emissions have been scaled by the ratio of that month to 2022 emissions reported by <a href="https://www.nature.com/articles/s41597-020-00708-7">Carbon Monitor</a>, downloaded on October 15, 2024 from <a href="https://carbonmonitor.org">https://carbonmonitor.org/</a>. <ul> <li>ODIAC does not have sectoral decomposition to the degree provided by Carbon Monitor, so total ODIAC emissions for each region have been scaled by the total emission change between 2022 and each extended year reported by Carbon Monitor, i.e., power, ground transport, etc. have <strong>not</strong> been separately scaled.</li> <li>Carbon Monitor data are daily, but ODIAC emissions are monthly. So Carbon Monitor data have been aggregated to monthly totals before deriving scaling factors between 2022 and the extended years.</li> <li>Carbon Monitor reports international aviation emissions by country of origin, while ODIAC reports aviation emissions on a grid. Since there is no way to derive the points of emission for Carbon Monitor aviation emissions , all Carbon Monitor international aviation was aggregated to create a single number for each month, then that number was used to scale ODIAC's bunker fuel for each month in 2023-2024.</li> <li>CarbonMonitor data used for deriving 2023 and later emissions are now included in this dataset for convenience as netcdf files (converted from original CSV files).</li> </ul> </li> <li>Hourly global totals are given in the files as a check, in case you want to verify your units and file reading.</li> </ul> <p>These files can be downloaded from the browser, or from the command line following guides such as <a href="https://ict.ipbes.net/ipbes-ict-guide/data-and-knowledge-management/technical-guidelines/zenodo#b.-programmatically-using-r" target="_blank" rel="noopener">this</a>.</p>
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data
<p>This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures.</p> <p>Abstract of the manuscript:</p> <p>The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.</p>
Supplementary data for 'Results of the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)'
<p>Datasets and model datasheets provided to the third Marine Ice Sheet Model Intercomparison Project (MISMIP+)</p>
Data and scripts for: Intercomparison of atmospheric datasets and PBL schemes for precipitation downscaling over a coastal mountain valley of northern British Columbia, Canada
<p>anl_6MYJdiv and anl_6MYNNdiv contains pairwise normalizations of dataset outputs (NAM/ERA5, NAM/NARR and ERA5/NARR) of total rainfall in 2017 for simulations with the MYJ and MYNN3 PBL schemes, that can be plotted by fig3_4.ncl. anl_MYJMYNN_div contains MYJ/MYNN3 spatial contours for each of ERA5, NAM -ANL and NARR outputs. anl_snow_MYJ contains MYJ output for total snow in 2017 by the ERA5, NAM-ANL and NARR datasets, for which values at discrete locations can be retrieved with yr2017snow.ncl, daily_ppt.ncl is script to extract modeled daily precipitation (dly_MYJ and dly_MYNN) from the various locations. Fig_ppt_monthly.R is the plotting script for observed and modeled precipitation time series from hydro31pt1pk.txt. nullwrf is array holder for plotting with ncl scripts. rivs_coasts.shp is shape file that is used in the spatial plots. </p>
Code and data for RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project
<p>Model configuration code and post-processed data for simulations with SAM6.11.2 (Khairoutdinov and Randall, 2003) and CAM6 (https://github.com/ESCOMP/CESM/releases/tag/release-cesm2.1.3) needed to reproduce figures in the protocol paper for RCEMIP-II (Wing et al., 2023):</p> <p>Wing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-235, in review, 2023.</p> <p>SAM6.11.2 data (SAM6.11.2-lambda6000.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>crh_avg_$SST_$dT.mat: column relative humidity averaged over the short (y) dimension, as a function of x and time.</li> <li>mockwalker2048x128x74_3km_12s_$SST_$dT.nc: domain-averaged 0D (function of t) and 1D (function of z and t) data <ul> <li>The "long" simulations, which have a domain that is twice as long as normal, instead have files with names mockwalker4096x128x74_3km_12s_$SST_$dT.nc</li> <li>The "wide" simulations, which have a domain that is twice as wide as normal, instead have files with names mockwalker2048x256x74_3km_12s_$SST_$dT.nc</li> <li>The "longwide" simulations, which have a domain that is twice as long and twice as wide as normal, instead have files with names mockwalker4096x256x74_3km_12s_$SST_$dT.nc</li> </ul> </li> <li>SAM_CRM_MW_$SST_$dT_1D_cldfrac_avg.nc: domain cloud fraction profile (function of z and t) following cfv2 definition of Stauffer and Wing (2022)</li> </ul> </li> </ul> <p>SAM6.11.2 configuration files (SAM6.11.2-lambda6000-config.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST and delta SST has the following configuration files <ul> <li>snd: Initial sounding</li> <li>prm: Namelist parameters</li> <li>grd: Vertical grid</li> <li>domain.f90: Domain size and number of subdomains</li> <li>simpleocean.f90: SST specification</li> </ul> </li> </ul> <p>CAM6 data (CAM6.zip):</p> <ul> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>MockWalk54_humidity_HCF_$dT_$SST.nc: column relative humidity averaged over 4 longitude points, as a function of latitude and time.</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rlut_avg.nc: domain-averaged longwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsut_avg.nc: domain-averaged upwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsdt_avg.nc: domain-averaged downwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_1D_cldfrac_avg.nc: domain-averaged cloud fraction profile (function of z and t)</li> </ul> </li> </ul> <p>CAM6 configuration files (CAM6-MW295dT1p25-config.tar, CAM6-MW300dT1p25-config.tar, CAM6-MW305dT1p25-config.tar): Contains model initialization and configuration files for simulations with delta SST = 1.25 K. Simulations with other delta SST values need only change the delta SST parameter. </p>
Data for "The Green's Function Model Intercomparison Project (GFMIP) Protocol"
<p>Data used in the analysis for the GFMIP Protocol paper, in the form of .jld2 files, to be used in conjunction with <a href="../doi/10.5281/zenodo.7697344">https://zenodo.org/doi/10.5281/zenodo.7697344</a></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.