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864 results for “numerative”
Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling, Supporting Data
<p>Experimental data and numerical codes used in the manuscript "Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling" by V. Pinel, S. Furst, F. Maccaferri and D. Smittarello.</p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.
<p>The publications and research associated to cite is in:</p><p><a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a></p><p>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a></p><p>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:</p><p>Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements </p><p>Dune1</p><p> </p><p>Multi-grid solver</p><p> </p><p>11</p><p> </p><p>3</p><p> </p><p>2,304</p><p> </p><p>Complex..</p><p> </p><p>2,304</p><p> </p><p>HSMGP1</p><p> </p><p>Stencil-grid solver</p><p> </p><p>14</p><p> </p><p>3</p><p> </p><p>3,456</p><p> </p><p>..equation..</p><p> </p><p>3,456</p><p> </p><p>HiPAcc1</p><p> </p><p>Image processing framework</p><p> </p><p>33</p><p> </p><p>2</p><p> </p><p>13,485</p><p> </p><p>..solving..</p><p> </p><p>13,485</p><p> </p><p>Trimesh2</p><p> </p><p>Triangle mesh library</p><p> </p><p>13</p><p> </p><p>4</p><p> </p><p>239,360</p><p> </p><p>..time</p><p> </p><p>239,360</p><p> </p><p>GEC</p><p> </p><p>Generic edge computing</p><p> </p><p>552</p><p> </p><p>2</p><p> </p><p>~5.3*108</p><p> </p><p>Energy Consumption</p><p> </p><p>132500</p><p> </p><p>The dataset zip file contains:</p><ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul><p>References:</p><p>[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.</p><p>[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).</p>
Data from: Combined experimental-numerical analysis of the temperature evolution and distribution during friction surfacing
<p>This dataset contains the data for the publication "Combined experimental-numerical analysis of the temperature evolution and distribution during friction surfacing".</p>
NEMO: A Tool to Support Feature Models with Numerical Features and Arithmetic Constraints
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://youtu.be/V-ONW8PftwM</a></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://doi.org/10.1007/978-3-031-08129-3_4</a></p> <p>Real-world <em>Software Product Lines</em> (SPLs) need <em>Numerical Feature Models</em> (NFMs) whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting.</p> <p>Nemo (<strong>N</strong>umbers, f<strong>e</strong>atures, <strong>mo</strong>dels) supports NFMs by <em>bit-blasting</em>, the technique that encodes arithmetic as boolean clauses. Nemo translates NFMs to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate Nemo with a diverse set of real-world NFMs, complex arithmetic constraints, and counting experiments in this paper.</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
Numerical data for "Black Hole Metamorphosis and Stabilization by Memory Burden [arXiv:2006.00011]"
<p>This is the numerical data that belongs to the paper</p> <p>G. Dvali, L. Eisemann, M. Michel, S. Zell, <em>Black Hole Metamorphosis and Stabilization by Memory Burden</em>, <a href="https://doi.org/10.1103/PhysRevD.102.103523">Phys. Rev. D <strong>102</strong> (2020) 103523</a>, <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>.</p> <p>The numerical data is generated using the computer program <em>TimeEvolver</em>, which was presented in</p> <p>M. Michel, S. Zell, <em>TimeEvolver: A Program for Time Evolution With Improved Error Bound, </em><a href="https://doi.org/10.1016/j.cpc.2022.108374">Comput. Phys. Commun. <strong>277</strong> (2022) 108374</a>, <a href="https://arxiv.org/abs/2205.15346">arXiv:2205.15346</a>.</p> <p>In the following, equation numbers refer to the latest arXiv-version of <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>, where also all relevant definitions can be found. In this paper, the procedure for generating the data, which we summarize in the following, is also described in more detail.</p> <p>First, among the five parameters N<sub>c</sub>; <sup><span class="math-tex">\(\epsilon\)</span></sup><sub>m</sub>; C<sub>0</sub>; ∆N<sub>c</sub>; K all but one are fixed (according to eq. (36)). Then for different values of the remaining unfixed parameter - subsequently called X - the following 2-step process is performed.</p> <ol> <li>Time evolution is computed (with the initial state shown in eq. (35)) for many different values of the parameter C<sub>m</sub> in the interval [0;1] (sampling step 10<sup>-3</sup>). The results for X are stored in five folders called "X", the subfolders of which contain data for different values of X. For example, the subfolder "01" of the folder "C0" consists of data for C<sub>0</sub>=0.01. Please note that the folders "Cgap", "N0" and "Q" correspond to X=<span class="math-tex">\(\epsilon\)</span><sub>m</sub>, X=N<sub>c</sub> and X=K, respectively. Subsequently, "rewriting values" of C<sub>m</sub> are selected as those for which the amplitude of n<sub>0</sub> is sufficiently large (1.2 times than in the case C<sub>m</sub>=0). This is done in Mathematica-notebooks "_New.nb". Finally, rewriting values for different values of X are collected using Mathematica-notebooks with names that start on "_Meta". These notebooks generate the 5 plots shown in figures 4(a), 5(a), 6(a), 7(a) and 8(a).</li> <li>Next finer scans are performed around some of the rewriting values determined in step 1 (new sampling step 5 10<sup>-5</sup>). The results are stored in five folders called "XRates", where again subfolders correspond to different values of X. For example, the subfolder "01" of the folder "C0Rates" consists of finer scans in C<sub>m</sub> around rewriting value of C<sub>m</sub> for C<sub>0</sub>=0.01. Next, Mathematica-notebooks with the names "_New.nb" or "_NewRates.nb" are used to select around each rewriting value the C<sub>m</sub> that leads to the largest rate (see definition in <a href="https://arxiv.org/abs/2006.00011">arXiv:2006.00011</a>). Finally, these maximal rates for different values of X are collected using Mathematica-notebooks with names that start on "_Meta" and end on "Rates.nb". These notebooks generate the 5 plots shown in figures 4(b), 5(b), 6(b), 7(b) and 8(b).</li> </ol>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Numerical data for "Nonlocal correlations transmitted between quantum dots via short topological superconductor"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Nonlocal correlations transmitted between quantum dots </em><em>via short topological superconductor</em> (arXive preprint <a href="http://arxiv.org/abs/2405.06630">http://arxiv.org/abs/2405.06630</a>), and other data obtained within the same project. See file headers for details concerning the content.</p>
Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"
<p>This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors. <br>The two models considered are:<br>- a modified Ornstein-Uhlenbeck model with path-dependency;<br>- a model of neural masses with intrinsic potentiation leading to path-dependent dynamics. </p>
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
<p>This Zenodo repository contains the runs data for the paper <strong>RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models </strong>[<a href="https://arxiv.org/abs/2408.16118" target="_blank" rel="noopener">https://arxiv.org/abs/2408.16118</a>] presented at the NeurIPS 2024 workshop on Tackling Climate Change with Machine Learning, as well as the Master of Research (MRes) report <strong>Towards improving weather and climate models using reinforcement learning</strong> at the University of Cambridge<strong>.</strong> For questions, please contact Pritthijit Nath, <a href="mailto:pn341@cam.ac.uk" target="_blank" rel="noopener">pn341@cam.ac.uk</a>. Full documentation is available in the README.md file of the associated <a href="https://github.com/nathzi1505/climate-rl" target="_blank" rel="noopener">GitHub repo</a>.</p>
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
On the order of demonstrative, numeral, adjective, and noun
<p>Cite the source of the dataset as:</p> <blockquote> <p>Dryer, M.S. (2018). On the order of demonstrative, numeral, adjective, and noun. Language 94(4), 798-833. doi:10.1353/lan.2018.0054.</p> </blockquote>
Austronesian and Papuan numeral systems
<p>Cite the CLDF dataset as</p> <p>Cite the source dataset as</p> <blockquote> <p>Barlow, Russell. 2023. Papuan-Austronesian contact and the spread of numeral systems in Melanesia. Diachronica 40(3). 287–340. (doi:10.1075/dia.22005.bar)</p> </blockquote>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
H2020 ENODISE: ONERA Numerical Aeroacoustic Database Configuration A2-Ducted
<p>This database contains the numerical results obtained by ONERA on the configuration A2-ducted of the H2020 ENODISE project. In this configuration, a ducted propeller is placed above an S-plate to ingest an adverse pressure gradient boundary layer in a partially buried configuration. The present results can be compared to the measurements carried out by the University of Bristol.</p> <p>All following parameters are kept constant in the present simulations:</p> <ul> <li>Free-stream velocity <em>u_inf</em> = 32 m/s,</li> <li>Propeller tip clearance <em>d/D</em> = 0.002,</li> <li>Propeller position inside the duct <em>x/D</em> = -0.0627.</li> </ul> <p>Five simulations have been realized by varying the installation (with or without the S-plate) and the propeller rotation speed:</p> <ul> <li>Isolated propeller (without the S-plate), <em>N</em> = 6000 rpm, 8000 rpm, 11000 rpm,</li> <li>Installed propeller (with the S-plate), <em>N</em> = 6000 rpm, 8000 rpm.</li> </ul> <p>The predictions were obtained using the ProLB solver which is based on the Lattice Boltzmann method (<a href="http://www.prolb-cfd.com/">http://www.prolb-cfd.com/</a>). Aerodynamic and acoustic results are provided for each simulation. One-dimensional results are saved into ASCII column files (<em>.dat</em> extension) while geometry and raw flow extractions are saved into HDF5 CGNS files (<em>.cgns</em> extension). <em>README.txt</em> files are included for detailed description of the data.</p> <p> </p>
Precessing binary-black-hole numerical relativity catalogue (minimal data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Dataset for Numerical modeling of air-vented parallel plate ionization chambers for ultra-high dose rate applications
<p>Dataset for paper: Jose Paz-Martín et al., <a href="https://www.sciencedirect.com/journal/physica-medica">Physica Medica</a> <a href="https://www.sciencedirect.com/journal/physica-medica/vol/103/suppl/C">Volume 103</a>, November 2022, Pages 147-156</p> <p><a href="https://doi.org/10.1016/j.ejmp.2022.10.006">https://doi.org/10.1016/j.ejmp.2022.10.006</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.