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.
50
datasets available to search
ShareScore release 0.9.0
Dataset results
50 results for “numerical experiments”
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Results of the numerical experiments of the HyPaD algorithm
<p>This dataset is supplementary material for [1]. It provides the data obtained for the numerical experiments of the Hybrid Patch Decomposition (HyPaD) algorithm [2] that are presented in that paper. The following data is contained in the different zip-archives:</p> <ul> <li><strong>SNIA - full enumeration:</strong> A collection of .mat files with the output data for each of the 35 test instances using the full enumeration approach to realize the SNIA procedure.</li> <li><strong>SNIA - dynamic boxes:</strong> A collection of .mat files with the output data for each of the 35 test instances using the dynamic boxes approach to realize the SNIA procedure.</li> <li><strong>SNIA - fixed boxes [4]:</strong> A collection of .mat files with the output data for each of the 35 test instances using the fixed boxes approach with b=4 branching steps (i.e., 16 boxes in total) to realize the SNIA procedure.</li> <li><strong>MOMIX:</strong> A collection of folders that contain the .mat files with the results for the MOMIX (and MOMIX light) algorithm [3] which have been used as a reference point in the publication.</li> <li><strong>Figures:</strong> The .fig files used in [1].</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Gabriele Eichfelder and Leo Warnow, <strong>On implementation details and numerical experiments for the HyPaD algorithm to solve multi-objective mixed-integer convex optimization problems</strong>, <a href="http://www.optimization-online.org/DB_HTML/2021/08/8538.html">Optimization Online</a>, 2021.</li> <li>Gabriele Eichfelder and Leo Warnow, <strong>A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems</strong>, <a href="https://link.springer.com/article/10.1007/s00186-023-00828-x">Mathematical Methods of Operations Research</a>, 2021.</li> <li>Marianna De Santis, Gabriele Eichfelder, Julia Niebling and Stefan Rocktäschel, <strong>Solving Multiobjective Mixed Integer Convex Optimization Problems</strong>, <a href="https://epubs.siam.org/doi/10.1137/19M1264709">SIAM Journal on Optimization</a>, 2020.</li> </ol>
Numerical experiments for PDO
<p>The data named "JGR-CTRL-omega-50yr.nc", "JGR-CTRL-TS-50yr.nc", "JGR-CTRL-uwnd-50yr.nc" and "JGR-CTRL-vwnd-50yr.nc" are the output of the specific variables for the control experiment, which runs for 50 model years.The data named "JGR-SENS-omega-50yr.nc", "JGR-SENS-TS-50yr.nc", "JGR-SENS-uwnd-50yr.nc" and "JGR-SENS-vwnd-50yr.nc" are the corresponding output for the sensitive experiment, which also runs for 50 model years.</p>
Numerical experiments of ENSO and Arctic sea ice
<p>The data named "CTRL_40yrs_output.nc", “EXP_ALL_40yrs_R.nc”, “EXP_SIC_40yrs.nc”, “EXP_SST_40yrs_R.nc” are the output of last 40 model years for CTRL, EXP_ALL, EXP_SIC, and EXP_SST, which are run for totally 60 model years based on CAM5.</p>
Numerical experiments for IOBM
<p>The data named "1-GRL-CAM-EXP1-40yr.nc" and "1-GRL-CAM-EXP2-40yr.nc" are the output of last 40 model years for EXP1 and EXP2, respectively, which are run for totally 60 model years. The data named "1-GRL-CAM-CRTL-40yr.nc" is the corresponding 40-yr data of CTRL run, which is used to compute the differences beween EXP1/EXP2 and CTRL.</p> <p>The data named "2-GRL-CAM-EXP3-30yr.nc" and "2-GRL-CAM-EXP4-30yr.nc" are the output of last 30 model years for EXP3 and EXP4, respectively, which are run for totally 45 model years. The data named "2-GRL-CAM-CRTL-30yr.nc" is the corresponding 30-yr data of CTRL run, which is used to compute the differences beween EXP3/EXP4 and CTRL.</p> <p> </p>
Urbanization numerical experiment in Shanghai: WRF model outpts
<div>A numerical experiment was conducted using the WRF model to explore the impacts of urbanization and urban growth on convective rainfall in the subtropical city of Shanghai (China). Details on the experiment and results are summarized in the paper by Qi et al. (2024) entitled: "Contradictory influences of urbanization on intense convective rainfall in a subtropical city".</div> <div> </div> <div>The data is stored in NetCDF format and contains rainfall, moisture, temperature, and omega variables for the three scenarios explored in the paper.</div>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,</p> <p>Here you can access all the output of the PlaSim simulations in netCDF format.</p> <p>The form of the file names is as follows:</p> <p>TS_History#_m0##.nc</p> <p>, where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,</p> <p>Here you can access all the output of the PlaSim simulations in netCDF format.</p> <p>The form of the file names is as follows:</p> <p>TS_History#_m0##.nc</p> <p>, where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40</p>
PlaSim output data from TSI-related numerical experiments
<p>Dear Visitor,<br> <br> Here you can access all the output of the PlaSim simulations in netCDF format.<br> <br> The form of the file names is as follows:<br> <br> TS_History#_m0##.nc<br> <br> , where TS abbreviates surface temperature, # refers to the particular<br> forcing history (C, B, Ta, Tb, N) and ## denotes numbers from 1 to 40.</p>
Implementation of an adaptive bias-aware extended Kalman filter for sea-ice data assimilation in the HARMONIE-AROME numerical weather prediction system: numerical experiments
<p>This data set provides the post-processed output of the numerical experiments performed to assess the possible effects of applying sea ice data assimilation within the surface analysis procedure of the HARMONIE-AROME NWP system. Results of five numerical experiments are provided:</p> <ul> <li>HA-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>HA-EKF – sensitivity experiment with sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>3DVAR-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF – sensitivity experiment with sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF-TS – sensitivity experiment with sea ice data assimilation, and with 3DVAR for the upper-air analysis using coupled surface and atmospheric data assimilation procedures</li> </ul> <p>For the HA-REF and HA-EKF experiments a subset of the gridded model output is provided; for the 3DVAR-REF, 3DVAR-EKF and 3DVAR-EKF-TS experiments a subset of the gridded model output and model data extracted at the positions of the SYNOP and TEMP stations within the model domain are provided. Additionally, in-situ observations, covering the same time period as the 3DVAR-REF, 3DVAR-EKF, 3DVAR-EKF-TS experiments, are provided.</p>
XLEMOO numerical experiment data
<p>This data is related to the article by Misitano, G. (2023) titled Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method, to be published in ACM Transactions on Evolutionary Learning and Optimization. It includes data for two problems: the vehicle crash worthiness problem and the car-side impact problem. Refer to the original publication and included README file for additional details.</p>
Numerical experiments for La Niña, north Atlantic, and the LA_NA
<p>The data named "FCAM_cntrl*" are the outputs of the control run.</p> <p>The data named "FCAM_nina_2022*" are the outputs of for the La Niña run.</p> <p>The data named "FCAM_NA_2022_Feb*" are the outputs of for the north Atlantic run.</p> <p>The data named "FCAM_nina_NA*" are the outputs of for the LA_NA run.</p> <p> </p> <p> </p>
Supporting dataset for "Numerical insights into the effects of model geometric distortion in laboratory experiments of urban flooding"
<p>The supporting datasets includes:</p> <p>(1) All the Figures in the manuscript in .fig format </p> <p> - Figures in main text</p> <p> - Figures in SI</p> <p>(2) Original matrice data for the results of each run</p> <p> - A file that explains detailed data content in tree structure;</p> <p> - 4 sub-repositories which contain data for models in different scales</p>
Full results of numerical experiments described in research paper "A conservative index heuristic for routing problems with multiple heterogeneous service facilities"
<p>These files include the complete results of the numerical experiments described in our research paper, "A conservative index heuristic for routing problems with multiple heterogeneous service facilities".</p>
Raw results of the numerical experiments performed to evaluate different MAC schemes for LoRaWAN networks
<p>This zip file contains the scripts, gnuplot and data files needed to generate the figures showing the numerical results presented in [1].</p> <p>[1] S. Herrería-Alonso, A. Suárez-González, M. Rodríguez-Pérez and C. López-García, "Enhancing LoRaWAN scalability with Longest First Slotted CSMA," in <em>Computer Networks</em>, vol. 216, article number 109252, Oct. 2022, doi: 10.1016/j.comnet.2022.109252.</p>
Data for: Experimental and numerical methods to ensure comprehensible and replicable electrical stimulation experiments
<p>Replication data for "Experimental and numerical methods to ensure comprehensible and replicable electrical stimulation experiments".</p>
Outputs of Numerical experiments verifying the diverse interannual variability of Asian summer monsoon onset process
<p><strong>Introduction</strong></p> <p>We designed two sets of numerical experiments using the Community Earth System Model (CESM 1.2.2) released by NCAR to verify the SSTAs' effect on the interannual modes of ASMOP in April and May. In the atmospheric general circulation model (AGCM) experiments, we solely forced the Community Atmosphere Model version 5.3 (CAM5.3), the atmospheric component of the CESM 1.2.2, by the specific SSTAs in April–May. The AGCM control run included a 40-yr integration forced by the climatological sea surface temperature (SST) with the annual cycle. We attached the specific SSTAs to the climatological SST in each sensitivity experiment and integrated them for 40 years. The ensemble results in the last 30 years were used for analysis. By contrast, in the PACEMAKER experiments, we first integrated the CESM 1.2.2 for 200 years as a control run to exclude the evident climate shift. Afterward, the sensitivity experiments were conducted on the last 100-yr outputs of the control run, in which we nudged the April–May SSTAs in the specific domain but left the rest of the model's coupled climate system free to evolve. The last 30 members were ensembled for analysis in each PACEMAKER experiment. </p> <p><strong>Numerical Experiment Design</strong></p> <p>In the AGCM category, we forced CAM 5.3 by warm SSTAs in the southwestern Indian Ocean (20°S-0°, 40°-80°E) in the ASMOP-PC2 experiment but by cold SSTAs in the western North Pacific (0°-20°N, 120°-160°E) in the ASMOP-PC3 experiment from April to May, respectively. Similar SSTAs were nudged in the two oceanic domains from April to May in the PACEMAKER category.</p> <p><strong>File Description</strong></p> <ol> <li>Output files were saved in the NetCDF format.</li> <li>The "AGCM.tar.gz" and "PACEMAKER.tar.gz" was the compressed files of the atmospheric outputs in May in the AGCM and PACEMAKER experiments, respectively.</li> <li>In each compressed file, the results of the control run (in the subfolder named "Control_Run"), ASMOP-PC2 (in the subfolder named "ASMOP-PC2"), and ASMOP-PC3 (in the subfolder named "ASMOP-PC3") sensitivity experiments were included for comparison.</li> </ol>
Effective anisotropic and viscoelastic representation of gas-hydrate bearing sediments from harmonic numerical experiments
<p>The data are the binary patchy gas-hydrate contents and Lamé shear coefficient.</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.