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864 results for “numerative”
Optimal Displacement Increment for Numerical Frequencies (Dataset)
<p>1<em>H</em>-pyrrolo[3,2-<em>h</em>]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.</p> <p>An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.</p> <p>For the initial OPT and ANFREQ, the following files are provided:<br /> PQ_OPT_AFQ.engrad -- Gradient data<br /> PQ_OPT_AFQ.gbw -- Wavefunction <br /> PQ_OPT_AFQ.hess -- Hessian data<br /> PQ_OPT_AFQ.out -- Computation output<br /> PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ)<br /> PQ_OPT_AFQ.txt -- ORCA input file<br /> PQ_OPT_AFQ.xyz -- Optimized geometry</p> <p>For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs:<br /> PQ_NFQ_0.####.hess -- Hessian data<br /> PQ_NFQ_0.####.out -- Computation output<br /> PQ_NFQ_0.####.txt -- ORCA input file</p> <p>Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)</p> <p>The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.</p> <p>For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.</p> <p>A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.</p>
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Dataset for "Numerical methods for the detection of phase defect structures in excitable media"
<p>This archive contains the numerical methods presented in the publication "Numerical methods for the detection of phase defect structures in excitable media" as well as the data sets these methods have been applied on. The Python module for Ithildin (py_ithildin.zip) contains the actual Python source code of those methods. Additional Python scripts have been used to generate the figures in the paper (scripts-pdl-detection.zip). The optical voltage mapping data (optical_*) has been slightly pre-processed (noise reduction, re-scaling, etc). The second variable for the optical data (optical_20200204114234_v.npy) is a delayed version of the first variable. The other files contain simulation results from several finite differences simulations of the mono-domain model. For details, see our paper.</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271351"><strong>Numerical methods for the detection of phase defect structures in excitable media</strong></a><br> Kabus D, Arno L, Leenknegt L, Panfilov AV, Dierckx H (2022) Numerical methods for the detection of phase defect structures in excitable media. PLOS ONE 17(7): e0271351. <a href="https://doi.org/10.1371/journal.pone.0271351">https://doi.org/10.1371/journal.pone.0271351</a></p>
Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)
<p>This dataset gathers synthetic T2-weighted magnetic resonance (MR) images generated using FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation.<br> This dataset is associated with the following paper:<br> Lajous H. et al. (2022) A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports. https://doi.org/10.1038/s41598-022-10335-4</p> <p>This dataset provides images simulated by FaBiAN based on the specific implementation of FSE sequences by two MR vendors (Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE), Siemens Healthcare, and Single-Shot Fast Spin Echo (SS-FSE), GE Healthcare) at 1.5 T or 3 T.<br> Automated brain tissue annotations of the low-resolution series and super-resolution (SR) reconstructions are also included.</p> <p>Works using any of these data should cite the following references:<br> - Lajous, H. et al. A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Scientific Reports (2022). https://doi.org/10.1038/s41598-022-10335-4<br> - Lajous, H., Roy, C. W., Yerly, J. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v1.2 (1.2). Zenodo (2022). https://doi.org/10.5281/zenodo.5471094<br> - Lajous, H. et al. Dataset A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Zenodo (2022). https://doi.org/10.5281/zenodo.6477946</p> <p><br> Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2022.</p>
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>
Blind Prediction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Masonry vaults play a much relevant role in the seismic response of heritage masonry buildings, ranging from housing to the greatest cathedrals. Acting as both a ceiling and a structural horizontal diaphragm with significant mass, their mechanical behaviour affects the overall seismic response of buildings, in terms of strength, stiffness, and ductility. Moreover, local damage and collapse of vaults may produce significant losses in terms of cultural assets and casualties. In spite of the importance of this topic, the evaluation of the complex three-dimensional behaviour of vaults is still an important challenge for researchers. The main objectives of the present research project are:<br> 1) to better understand the seismic behaviour of masonry cross vaults by means of shaking table tests on both full-scale and small-scale models;<br> 2) to assess the capability of different modelling/analysis approaches to predict the seismic response of these masonry structures.</p> <p>In particular, three sets of shaking table tests are planned:<br> a. Tests on a 1:1 scale model of a brick unreinforced masonry cross vault: to investigate the behaviour of brick masonry cross vaults under different seismic inputs, in terms of damage, displacement capacity and peak acceleration.<br> b. Tests on a 1:1 scale model of a brick reinforced masonry cross vault: to evaluate the effectiveness of reinforcing techniques to repair the vaults tested in a).</p> <p>In addition to the experimental tests, a blind prediction competition is performed to assess the efficacy of different modelling strategies and analysis techniques. The final aims are to improve the safety assessment procedures proposed for historic masonry buildings in Eurocode 8.3 and to provide better seismic assessment techniques and strengthening measures.</p>
Drop count and size of 38 numerical simulation of a flat fan spray
<p>Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions"</p> <p>by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049)</p> <p></p> <p></p> <p>Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle.</p> <p>Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as</p> <p>- alpha## (## is the spray angle)</p> <p>- Re## (## is the Reynolds number)</p> <p>- We## (## is the Weber number)</p> <p>Each file contains as many raws as the number of drops.</p> <p>In the i-th raw,</p> <p>1) the first element is the Volume of the i-th drop;</p> <p>2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1];</p> <p>3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume)</p> <p>The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time.</p> <p> </p>
Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona
This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.
Daily ocean bottom pressure anomalies 2007-2009 from global numerical models
<p>Data supplement to: Schindelegger, M., Harker, A. A., Ponte, R. M., Dobslaw, H., & Salstein, D. A. (2021). Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability. <em>Journal of Geophysical Research: Oceans</em>, 126, e2020JC017031. <a href="https://doi.org/10.1029/2020JC017031">https://doi.org/10.1029/2020JC017031</a></p> <p><strong>Contents:</strong></p> <p>Daily ocean bottom pressure anomalies over 2007-2009 obtained from two global forward simulations:</p> <ol> <li><strong>DEBOT</strong> (<em>David Einspigel Barotropic Ocean Tide Model</em>): 1/3° horizontal grid spacing, single-layer model</li> <li><strong>MITgcm LLC270</strong> (<em>Massachusetts Institute of Technology general circulation model, Lat-Lon-Cap 270</em>): nominal 1/3° horizontal grid spacing, 50 vertical layers</li> </ol> <p>Common specifications:</p> <ul> <li>Yearly files (<em>yyyy</em>) for each model run (<em>DEBOT_OBP_n180_yyyy.nc</em>, <em>LLC270_OBP_n180_yyyy.nc</em>)</li> <li>Temporal mean 2007-2009 reduced</li> <li>Daily fields centered at 12 UTC</li> <li>Units: cm of equivalent water height</li> <li>Data given on regular 1° grid</li> <li>Synthesized from spherical harmonic expansion truncated at degree 180 (<em>n180</em>)</li> <li>Degree 1 terms: included</li> <li>Static atmospheric contribution to bottom pressure: included</li> <li>Atmospheric forcing: ERA-Interim (6-hourly)</li> </ul> <p> </p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p>
Numerical simulations of AZO/ZnGeO/Cu2O solar cells: Impact of the germanium composition of the buffer layer and the use of low cost fabrication on the photovoltaic performances
<p>The dataset contains the results of the numerical simulations of AZO/ZnGeO/Cu2O solar cell models.</p> <p>The physical parameters of the model are chosen with special care to match literature experimental measurements or are interpolated using the values from binary metal oxides in the case of the new ZnGeO compound. The solar cell structure includes an interface and a defective layer at the ZnGeO/Cu2O heterojunction.</p> <p>The AZO/ZnGeO/Cu2O model results reproduce the photovoltaic characteristics of experimental devices presented by Minami et al. (Applied Physics Express 9, 052301 (2016) DOI:10.7567/APEX.9.052301)</p> <p>The dataset also includes results using models with different germanium compositions for the ZnGeO buffer layer.</p> <p>Other solar cell simulation results are presented to model the impact of low cost fabrication processes, such as spray pyrolysis, by varying the thickness, doping concentration, carrier mobilities and defect concentration of the Cu2O absorber layer as well as the germanium composition of the buffer layer.</p>
CLDF dataset derived from Mamta's "South Asian Numerals Database" from 2024
<p>Cite the source of the dataset as:</p> <blockquote> <p>Mamta, K. (2024): South Asian Numerals Database (SAND). Leipzig: Max Planck Institute for Evolutionary Anthropology.</p> </blockquote>
CLDF dataset derived from Chan's "Numeral Systems of the World's Languages." from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chan, Eugene (eds.) 2019. Numeral Systems of the World's Languages. Accessed: 2019-09-30</p> </blockquote>
Numerically predicted permeability of over 6500 artificially generated fibrous microstructures
<p>This data set was generated in the project "ML4ProcessSimulation - Machine Learning for Simulation Intelligence in Composite Process Design" (Leibniz Collaborative Excellence funding program: K377/2021), at Leibniz-Institut für Verbundwerkstoffe GmbH. The goals were to create a comprehensive data set for training different neural networks and to gain insight into the influence of fiber structure on permeability. The models represent the fiber structure within fiber bundles in fiber-reinforced plastic composites (FRPC). Over 6500 structure models were generated in the software GeoDict® [1] and the permeability tensor of these models was numerically calculated in the GeoDict® module FlowDict [2]. The zip files contain the structure file (<i>gdt</i>), the model generation result file (<i>FiberGeo_[...].gdr</i>) and the flow simulation result file (<i>LIRStokesResult_[...].gdr</i>). For each zip file is a JSON meta data file available and in addition the gdr files contain all input and output data of the model generation and the flow simulation. The file <i>Table_of_Parameter_studies_and_model_pictures.jpg</i> gives an overview of the parameter studies and exemplarily shows two models each.The data set is divided into three parameter studies: </p><ul><li>1_Parameter_study_round_fibers with round fibers by varying the fiber volume content (fvc), fiber diameter (fdia) and fiber orientation (fdir). For each modeling parameter, 5 - 100 models (random seed or RS) were generated, all differing due to the randomized fiber positioning during model generation.</li><li>2_Parameter_study_elliptical_fibers with elliptical fibers that was varied based on different aspect ratios (asp1, asp2, asp3). In addition, fdia and fvc were varied and 5 models (RS) were calculated. </li><li>3_Parameter_study_undulation with elliptical fibers, whose undulation was varied. In addition, fdia and fvc were varied and 12 models (RS) were calculated.</li></ul><p><i>[1] J. Hilden, S. Rief, and B. Planas, GeoDict 2023 User Guide. FiberGeo handbook. DE: Math2Market GmbH, 2023. Accessed: Oct. 26, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></p><p><i>[2] J. Hilden, S. Linden, and B. Planas, "GeoDict 2023 User Guide. FlowDict handbook." Math2Market GmbH, 2023. Accessed: Jul. 31, 2023. [Online]. Available: https://doi.org/10.30423/userguide.geodict</i></p>
The Sensitivity of SSVEPs to Variations in Numerical Material for Automatic Processing of Small Magnitudes
<p>We investigated the human ability to automatically process small magnitude information, using an oddball fast-periodic visual stimulation paradigm featuring a periodic alteration of magnitude (2 vs. 4) at a frequency of 1.25 Hz. Participants were exposed to various types of numerical content, such as biological (fingers), analogical (dots), and symbolic (Arabic digits), presented either in their conventional format (canonical) or in alternative formats (non-canonical), all synchronized at a base rate of 6.25 Hz. Our primary objective was to ascertain the sensitivity of steady-state visual evoked potentials (SSVEPs) to subtle small magnitude variations in relation to the specific type of numerical material.</p><p>SSVEPs were consistently observed at the base rate, corresponding to the presentation of the visual stimuli. Variations across conditions in terms of their location is examined, as well as the amplitude of the SSVEPs which is influenced by the type of material presented. Additionally, oddball SSVEPs emerged at a frequency of 1.25 Hz (and its harmonics) for each numerical content, suggesting the ability to discriminating the change of magnitude in each instance. However, the neural response exhibited distinctive characteristics based on the type of material presented. </p><p>These findings demonstrate that SSVEPs, while maintaining consistency in their presence across conditions, exhibit a sensitivity to variations in the type of numerical material, shedding light on the neural processes involved in the automatic processing of small magnitude information.</p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
CLDF dataset derived from Barlow's "Numerals of the Pacific" from 2025
<p>Cite the source of the dataset as:</p> <blockquote> <p>Barlow, Russell (ed.). 2025. Numerals of the Pacific: A collection of numeral terms in Austronesian and Papuan languages.</p> </blockquote>
Numerical Data Set Belonging to: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'
<p>This is the numerical data set belonging to the Nureth conference paper entitled: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'. </p> <p>The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat' represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively. </p> <p>The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2). </p> <p>The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2).</p> <p>Likewise, the files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).</p> <p> </p> <p> </p>
Test and numerical data of a vapour-injection scroll compressor in a heat pump with R1234ze(E)
<p>The dataset contains the experimental results of a water-to-water heat pump tested at the lab for different water temperatures. The refrigerant used is the HFO R1234ze(E). The scroll compressor is equipped with an eco port. The numerical results of a validated semi-empirical model are also included for a standard suction pressure drop model and an improved one.</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
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.