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Dataset results
279 results for “model comparison”
Comparison of primary production between different models and measurements in the Indian Ocean
<p>This data set contains the various cruises data of Chlorophyll, Primary Production, photic zone integrated chlorophyll and normalized chlorophyll in the Indian Ocean.</p> <p> </p>
Dataset for Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations
<div> <div> <div> <div> <h4>Dataset Overview</h4> <p>This dataset accompanies the research paper titled " Investigation of Thermospheric Response to Geomagnetic Storms Using GITM-OVATION Prime and -FTA model With Comparison to GOLD and SABER Observations" and contains all the necessary data and scripts required to reproduce the results presented in the paper. The dataset is organized by figure numbers, corresponding directly to the figures in the paper, making it straightforward to locate and generate the specific results.</p> <h4>Structure of the Dataset</h4> <p>The dataset is divided into multiple folders, each named according to the figure numbers in the paper (e.g., Figure_1, Figure_2, etc.). Inside each of these folders, you will find:</p> <ul> <li><strong>Data Files</strong>: These files contain the raw and processed data used to generate the figures.</li> <li><strong>Scripts</strong>: MATLAB scripts (e.g., Figure_2*.m) that process the data and generate the respective figures.</li> <li><strong>Readme.txt</strong>: A text file providing detailed instructions on how to use the data and scripts, including any dependencies or specific steps required.</li> </ul> <h4>Instructions for Reproducing Figures</h4> <ol> <li> <p><strong>Download the Dataset</strong>:</p> <ul> <li>Download the entire dataset or specific figure folders as needed.</li> </ul> </li> <li> <p><strong>Prepare Your Environment</strong>:</p> <ul> <li>Ensure that MATLAB is installed on your local machine.</li> <li>Verify that all necessary MATLAB toolboxes and dependencies are installed, as specified in the Readme.txt files within each figure folder.</li> </ul> </li> <li> <p><strong>Generate Figures</strong>:</p> <ul> <li>Navigate to the directory where the dataset is saved.</li> <li>Open MATLAB and set the current directory to the folder containing the downloaded data and scripts.</li> <li>Run the script corresponding to the figure you wish to generate. For example, to generate Figure 1, navigate to the Figure_1 folder and run the <code>Figure_1*.m.</code></li> </ul> </li> <li> <p><strong>Refer to Readme.txt for Further Details</strong>:</p> <ul> <li>Each figure folder contains a Readme.txt file with additional details, including specific instructions, data descriptions, and any figure-specific requirements or notes.</li> </ul> </li> </ol> <p>By following these steps, you can successfully reproduce the figures and results presented in the paper "Paper 1 vs Paper 2" using the provided dataset and scripts. If you encounter any issues or have questions, refer to the Readme.txt files or contact the authors for further assistance.</p> </div> </div> </div> </div>
DYAMOND-II Model Outputs for "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations"
<p>This dataset contains outputs from the DYAMOND Phase-II simulations, focusing on Global Storm-Resolving Models (GSRMs) at various Atmospheric Radiation Measurement (ARM) sites. The dataset supports the analysis presented in the manuscript "Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons with the ARM Observations."</p> <p>Included are high-resolution model outputs from nine GSRMs, detailing simulations of atmospheric processes at six ARM sites, including variables of clouds, temperature, humidity, wind, and surface fluxes. This dataset allows for a direct comparison between GSRM simulations and ARM field observations, facilitating the evaluation of PBL-coupled and decoupled clouds. For further inquiries or assistance regarding the dataset, please contact the corresponding author at su10@llnl.gov.</p>
A digital twin model of urban utility tunnels and its application:One-dimensional comparison verification
<div> <div> <div> <div> <div> </div> 重点词汇</div> <div> <div>93<em>/</em>5000</div> </div> </div> </div> </div> <div> </div> <div> <div> <div> <div> <div> <div>通用场景</div> <div> </div> </div> </div> </div> </div> <div> <div> <p><span>论文《城市综合管廊数字孪生模型及其应用》中一维综合管廊中天然气浓度分布快速预测模型结果对比</span></p> </div> </div> </div>
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
Dataset for "Technical Note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models "
<p>This record contains data used in Peltola et al. (2018): Technical Note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models, Biogeosciences.</p> <p>The record contains model input data, as well as CH<sub>4</sub> flux data used to validate the modelling results.</p> <p>Please see the read me file for more information.</p> <p>For further information contact Olli Peltola (olli.peltola@helsinki.fi)</p>
Global riverine DOC flux by TRIPLEX-HYDRA model and a comparison with a published DOC database
<p>A database of global riverine DOC flux simulated by TRIPLEX-HYDRA model. And the temporal variation of riverine DOC flux in terms of different continents, oceans and latitudinal bands. </p>
The 2019 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Replication
<p>This archive contains detailed results from QComp 2019 as well as the necessary scripts and data to replicate them.</p> <p>Visit http://qcomp.org for more information for QComp.</p> <p>Overview of Contents</p> <p>- `qcomp.org/` contains the state of our website from the timepoint of the competition. This includes:<br> - All benchmark files, browsable at `qcomp.org/benchmarks/index.html`<br> - Detailed competition results in a human-readable format, browsable at `qcomp.org/competition/2019/results/index.html`<br> - `logs/` contains the raw logfiles and data gathered by our scripts<br> - `scripts/` contains scripts to replicate the whole competition<br> - `toolpackages/` contains a package for each participating tool which includes<br> - Instructions for obtaining and installing the tool<br> - a file `invocations.json` listing the commandlines used in QComp 2019<br> - a file `tool.py` providing functionalities to obtain the result from the tool output.</p>
Impacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)
<p>Here presented CMAQ outputs used for paper of "MImpacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)"</p>
Quasi-2D Finite Volume Modeling of Corona Discharges for Ionic Propulsion: Comparison of Reduced Reaction Schemes
<p>This work investigated the effects of different kinetic models on the results of corona discharge simulations of a wire-cylinder geometry. The considered dry air kinetic models are: a 6-species model from Parent et al. (<a title="https://doi.org/10.1016/j.jcp.2013.11.029" href="https://doi.org/10.1016/j.jcp.2013.11.029" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jcp.2013.11.029</a>), a Townsend-like model (<a title="http://dx.doi.org/10.1088/0022-3727/30/4/017" href="http://dx.doi.org/10.1088/0022-3727/30/4/017" target="_blank" rel="noreferrer noopener">http://dx.doi.org/10.1088/0022-3727/30/4/017</a>) and the simple model proposed by Mateo-Velez et al. (<a title="http://dx.doi.org/10.1088/0022-3727/41/3/035205" href="http://dx.doi.org/10.1088/0022-3727/41/3/035205" target="_blank" rel="noreferrer noopener">http://dx.doi.org/10.1088/0022-3727/41/3/035205</a>).</p>
The comparison of structured abstracts generated by the ChatGPT language model with the author's original abstracts derived from research papers
<p>The study utilized publications in the field of information science, both in Polish and English, which appeared in the journal <em>Zagadnienia Informacji Naukowej – Studia Informacyjne</em> during the 2022-2023 period. A total of 10 research papers were selected – 5 in Polish (PL1-PL5) and 5 in English (EN1-EN5).</p>
Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations
<p>This dataset is composed of two main folders.</p> <p><strong>clim_3D</strong>: contains 4 files in 3 directories:</p> <ul> <li>BGC-Argo/bbp700_nemo_clim.nc: global monthly climatology of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>BGC-Argo/bbp700_nemo_climseas.nc: seasonal climatology (JFM, ...OND) of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>biomes/nemobiomes.nc: biomes of Fay and McKinley 2014 (ESSD) reprojected onto the ORCA2_L31 NEMO grid.</li> <li>PISCES/PISCES_1m_19600101_19601231_ptrc_T.nc: PISCES tracers (living organisms and detrial organic carbon), monthly climatology based on pre-industrial simulation described by Aumont et al. 2017 (Biogeosciences).</li> </ul> <p><strong>CATS_1D:</strong> contains 66 folders. The folders are named as <fwmo>_<yyyy>_<orca1cell>, where</p> <ul> <li>fwmo = World Meteorological Organization (WMO) float number</li> <li>yyyy = year</li> <li>orca1cell = number of the NEMO model horizontal grid cell (ORCA1 grid) used to run the PISCES 1D offline simulation.</li> </ul> <p>Each folder contains:</p> <ul> <li>BGC-Argo observations for a given Argo float and year, bined onto the model vertical grid and 5-day peiods: 'Mprof*.nc'</li> <li>Dynamical fields used to run PISCES 1D offline simulations: 'dyna_grid_T.nc'</li> <li>Output of the simulations: PISCES tracers at 5-day resolution: 'PISCES_5d_*_ptrc_T.nc'</li> <li>List of horizontal model grid cells that were visited by the Argo float on a given year: 'xymatch_<fwmo>.csv'. If the list is queried for the grid cell number <orca1cell>, one can obtain the corresponding longitude (xmap), latitude (ymap), and the x and y indices of the grid (plus/minus 1). These indices correspond to the 3x3 horizontal grid of the dynamical fields and the PISCES 1D output.</li> </ul> <p> </p> <ul> </ul>
Atmospheric and Coupled Model inter-comparison Study
<p>This repository contains software tools, model output datasets and plotting scripts that used for evaluations and figures presented in the study.</p>
A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble
<p>Supporting information for manuscript titled "A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble"</p><p>Datasets S1. Early to Middle Miocene NetCDF files: E2MMIO280.nc, E2MMIO400.nc, E2MMIO560.nc, E2MMIO850.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S2 Middle to Late Miocene NetCDF files: M2LMIO280.nc, M2LMIO400.nc, M2LMIO560.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S3 Preindustrial NetCDF files: PI contains MioMIP1 climate variables used to make manuscript figures.</p><p>Dataset S4 CSV file MioMIP_MAP_compilation contains newly revised miocene reconstructed mean annual precipitation from proxies. </p>
Comparison of Two PPOS Models for Pituitary Suppression
ClinicalTrials.gov study NCT06304220. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Comparison Between Asynchronous Online Care Model With Usual In Office Care for the Management of Atopic Dermatitis
ClinicalTrials.gov study NCT00985894. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of In-Situ Simulation Training in Adult Non-Trauma Resuscitation: A Comparison of ISS and OSS Team Performance Using the A-C-L-S Model
ClinicalTrials.gov study NCT07358793. IPD Sharing: NO. Countries: 1. Publications: 21.
DElirium prediCtIon in the intenSIve Care Unit: Head to Head comparisON of Two Delirium Prediction Models
ClinicalTrials.gov study NCT02518646. IPD Sharing: NO. Countries: 1. Publications: 5.
Comparison of the Accuracy and Reliability of Measurements Made on CBCT and IOS Images With Their made-on Plaster Models.
ClinicalTrials.gov study NCT05711160. IPD Sharing: Not stated. Countries: 1. Publications: 5.
A Comparison of Different Community Models of ART Delivery Amongst Stable HIV+ Patients in Two Urban Settings in Zambia
ClinicalTrials.gov study NCT03025165. IPD Sharing: NO. Countries: 1. Publications: 2.
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.