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102
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Dataset results
102 results for “Data Model comparison”
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the fiducial simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the HR simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the 1024Mpc simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the fiducial simulations as well as the primordial phases used for all simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV HR simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
MJO-QBO Model Inter-comparison Data
<p>Data in support of the paper "The Lack of a QBO-MJO Connection in Climate Models with a Nudged Stratosphere" by Zane K. Martin, Isla R. Simpson, Pu Lin, Clara Orbe, Qi Tang, Julie M. Caron, Chih-Chieh Chen, Hyemi Kim, L. Ruby Leung, Jadwiga H. Richter, and Shaocheng Xie, currently in preparation for submission.</p> <p>Data is organized by model, then ensemble member, then the temporal data resolution.</p> <p>Daily data are daily model OLR (olr/) and precipitation (precip/) in lat/lon/time format, over at least the tropical region spanning all longitudes and 20N to 20S. Daily data also include the Real-time Multivariate MJO index (RMM; RMM_index/) value from each model and ensemble members. OLR and precip files are provided on a 2.5 x 2.5 degree similar grid, rather than the models' native grid.</p> <p>Monthly data are temperature (temp/, at all vertical levels and all longitudes, from at least 20N to 20S, and the 100 hPa temperature file, as described more in the paper) zonal wind (at all vertical levels, and the 50 hPa wind file; wind/), and TEM vertical velocity (wtem/).</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-figure)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>data needed for generating all figures</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
The data of "A comparison of citation-based clustering and topic modeling for science mapping"
<p>These files consist of the data used in "A comparison of citation-based clustering and topic modeling for science mapping". </p> <p> </p>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
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Nucula specimen data used for: Testing the ‘Plus ça Change’ model: A comparison of Nuculid bivalve evolution across contrasting broad-scale climatic regimes
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Data from: Cardiac dysfunction in C57Bl/6J mice with chronic kidney disease shows sex-specific effects: Comparison of dietary adenine and 5/6 nephrectomy models
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Data for: Diversification models conflate likelihood and prior, and cannot be compared using conventional model-comparison tools
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Data from a model inter-comparison study to examine limiting factors in modelling Australian tropical savannas
<p>The modelling results of Whitley et al. (2016), consisting of the models BESS, BIOS2, CABLE, LPJ-GUESS, MAESPA and SPA, for five sites along the North-Australian Tropical Transect.</p> <p><strong>References</strong><br> Whitley, R., Beringer, J., Hutley, L.B., Abramowitz, G., De Kauwe, M.G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., Yu, Q., 2016. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245–3265. https://doi.org/10.5194/bg-13-3245-2016</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.