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Dataset results
279 results for “model comparison”
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-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>simulation snapshot data for the 0.0eV 1024Mpc 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-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>
Comparison between ozone column depths and methane lifetimes computed by 1-D and 3-D models at different atmospheric O2 Levels
<p>Recently, Cooke et al. (2022) used a 3-D coupled chemistry-climate model (WACCM6) to calculate ozone column depths at varied atmospheric O<sub>2</sub> levels. They argued that previous 1-D photochemical model studies, e.g., Segura et al. (2003), may have overestimated the ozone column depth at low pO<sub>2</sub>, and hence also overestimated the lifetime of methane. We have compared new simulations from an updated version of the Segura et al. model with those from WACCM6, together with some results from another 1-D and 3-D model. The discrepancy in ozone column depths is likely due to multiple interacting parameters, including lower boundary conditions, vertical and meridional transport rates, and different chemical mechanisms, especially the treatment of O<sub>2</sub> photolysis in the Schumann-Runge (SR) bands (175-205 nm). The discrepancy in tropospheric OH concentrations and methane lifetime between WACCM6 and the 1-D model at low pO<sub>2</sub> is reduced when absorption from CO<sub>2</sub> and H<sub>2</sub>O in this wavelength region is included in WACCM6. Including scattering in the SR bands may further reduce this difference. Resolving these issues can be accomplished by developing an accurate parameterization for O<sub>2</sub> photolysis in the SR bands and then repeating these calculations in the various models. Work is already underway to this end.</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>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </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>
Theoretical and numerical comparison of quantum- and classical embedding models for optical spectra
<p>This repository contains the files for the computational study on "Theoretical and numerical comparison of quantum- and classical embedding models for optical spectra".<br> The repository is organized into different folders as described below:</p> <p>====================================================================================================<br> 1_pna<br> This folder contains the configuration structures for p-nitroaniline extracted from the Molecular Dynamics (MD) simulations in *.xyz format that were used for any further calculations.<br> ====================================================================================================<br> 2_pftaa<br> This folder contains the configuration structures for pentameric formyl thiophene acetic acid extracted from the Molecular Dynamics (MD) simulations in *.xyz format that were used for any further calculations.<br> ====================================================================================================</p> <p><br> We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), The Villum Foundation, Young Investigator Program (EDH, grant no. 29412), the Swedish Research Council (EDH, grant no. 2019-04205), and Independent Research Fund Denmark (EDH, grant no. 0252-00002B and grant no. 2064-00002B) for support.<br> </p>
Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models
<p>The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr".</p> <p><strong>"3CCF_supermodes"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"3CCF_modelingJLTPaper" </strong>in order to plot them to get Fig. 3 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"3CCF_modelingJLTPaper"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p> <p> </p> <p> </p> <p><strong>! </strong><em>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</em>A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<em>]:</em></p> <p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p> <p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p> <p> </p> <p><strong>P.s. </strong>In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.</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>
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.