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864 results for “numerative”
Melt Focusing Along Permeability Barriers at Subduction Zones and the Location of Volcanic Arcs: Numerical models
<p>The dataset includes 2-D subduction zone models calculated by Comsol Mutiphysics®, slab geometry, subduction parameters, and the prediction results.</p> <p>Each numerical model solves the thermal structure of 31 subduction zones. The 2-D slab geometry of each subduction zone is obtained from the compilations of global subduction geometries based on earthquake catalogs Slab 1.0 and Slab2 (Hayes et al., 2012; 2018). Each slab geometry is imported in the corresponding Comsol model as a text file format. Below the point where the slab depth data is unavailable, the slab interface is simply defined as a straight line with the same dip to the bottom of the computation domain. The subduction parameters used in the models are available in Table 1.</p> <p>Using the calculated thermal structure at 30 Ma, we approximate the locations of the arc as the apices of 5 isotherms at 100°C interval within 800°C – 1200°C. The predicted arc locations from each isotherm are reported in Table 2 as the horizontal distance from the trench. The actual arc location in each model is defined as the point on the surface where the slab interface reaches the subarc slab depth <em>H</em> in Table 1 and reported as the horizontal distance from the trench in Table 2. The slab water loss depth and rate obtained from van Keken et al. (2011) are presented in Table 2. In case of the maximum temperature above the water loss depth is higher than the experimentally-derived melting condition, 800°C, we report the horizontal distance from the trench. The width of the horizontal distance of slab water loss depth is assumed as the expected melting region. </p>
Falling balls in a viscous fluid with contact: Comparing numerical simulations with experimental data
<p>The full results of the numerical computations and the source code for the rigid-body ALE and rigid-body CutFEM discretisations as presented in "H. von Wahl, T. Richter, S. Frei and T. Hagemeier. ‘Falling balls in a viscous fluid with contact: Comparing numerical simulations with experimental data’. In: <em>Phys. Fluids </em>33.3, 033304 (2nd Mar. 2021). doi: <a href="http://10.1063/5.0037971">10.1063/5.0037971</a>. <a href="https://arxiv.org/abs/2011.08691">arXiv:2011.08691</a> [physics.flu-dyn]".</p>
Numerical response of predators to large variations of grassland vole abundance and long-term community change
<p>Voles can reach high densities with multi-annual population fluctuations of large amplitude, and they are at the base of predator communities in Northern Eurasia and Northern America. This status places them at the heart of management conflicts wherein crop protection and health concerns are often raised against conservation issues.<strong> Here, a 20-year survey describes the effects of large variations in grassland vole populations on the densities and the daily theoretical food intakes (TFI) of vole predators based on roadside counts.</strong> Our results show how the predator community responded to prey variations of large amplitude and how it reorganized with the increase in a dominant predator, here the red fox, which likely negatively impacted hare, European wildcat and domestic cat populations. This population increase did not lead to an increase in the average number of predators present in the study area, suggesting compensations among resident species due to intra-guild predation or competition. Large variations in vole predator number could be clearly attributed to the temporary increase in the populations of mobile birds of prey in response to grassland vole outbreaks. Our study provides empirical support for more timely and better focused actions in wildlife management and vole population control, and it supports an evidence-based and constructive dialogue about management targets and options between all stakeholders of such socio-ecosystems.</p> <p>The data set includes:</p> <p><b>S1 kml file</b>. Location of the study area (can be dropped in a Google Earth window or read from a GIS)</p> <p><b>S2 Excel file.</b> Road-side counts (sheet 1) and list of species observed (sheet 2)</p> <p><b>S3 Excel file.</b> Small mammal data</p> <p><b>S4 Excel file.</b> Data for computing theoretical daily food intakes</p> <p> </p>
Experimental and numerical data of an article on the prediction of wind load on pylons for a neutral atmospheric boundary layer ow over two successive hills
<p>his deposit contains experimental and numerical data related to the article entitled "Towards prediction of wind load on pylons for a neutral atmospheric boundary layer flow over two successive hills" that will be publised in Journal of Wind Engineering & Industrial Aerodynamics. It consists in post-processed experimental data (turbulence statistics) for flows around 2D hills, measured by PIV, and numerical date of k-epsilon simulations of the same configirations. All data are provided in matlab metadata format. Matlab functions are given to plot all the figures of the article.</p>
Data from: Canine sense of quantity: evidence for numerical ratio-dependent activation in parietotemporal cortex
<p></p><p>The approximate number system (ANS), which supports the rapid estimation of quantity, emerges early in human development and is widespread across species. Neural evidence from both human and non-human primates suggests the parietal cortex as a primary locus of numerical estimation, but it is unclear whether the numerical competencies observed across non-primate species are subserved by similar neural mechanisms. Moreover, because studies with non-human animals typically involve extensive training, little is known about the spontaneous numerical capacities of non-human animals. To address these questions, we examined the neural underpinnings of number perception using awake canine functional magnetic resonance imaging. Dogs passively viewed dot arrays that varied in ratio and, critically, received no task-relevant training or exposure prior to testing. We found evidence of ratio-dependent activation, which is a key feature of the ANS, in canine parietotemporal cortex in the majority of dogs tested. This finding is suggestive of a neural mechanism for quantity perception that has been conserved across mammalian evolution.</p><p></p>
Dataset of paper "Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques"
<p>Dataset of paper "<strong>Comparison of radiant intensity in aqueous media using experimental and numerical simulation techniques</strong>":</p><ul><li>Relative spectral intensity for each light source as recorded by ILT RAA4 spectroradiometer.</li><li>Material and optical properties of the quartz tube.</li><li>Wavelength specific refractive index and quantum yield in water.</li><li>Ray tracing data vs experimental data (FX-1 265).</li><li>Plot of peak intensity vs working distance (in air) and Comparison between radiometry and ray tracing in the presence of a quartz tube in front of the light source for FX-1 265.</li><li>Recorded peak intensity at multiple working distances using radiometry and ray tracing.</li><li>Comparison between average light loss at multiple distances in radiometry, ray tracing and transmission curve due to quartz material and Plot of percentage light loss for all wavelengths at multiple working distances due to quartz material.</li><li>Data on loss of intensity in comparison with the transmission curve.</li><li>Recorded peak intensity at multiple working distances between simulation tools.</li><li>Comparison between ray tracing, DOM and radiometry for FX-1 265, Uniformity plot obtained from radiometry, ray tracing interface and DOM.</li><li>Data on comparison between actinometry measurements and ray tracing.</li><li>Plot of intensity with wavelength between the two techniques.</li><li>Comparison between measured intensity in air and water medium for FX-1 265.</li><li>Plot of change in peak intensity as light propagates through the quartz tube within the UV fixture at multiple working distances for FX-1 265.</li><li>Simulated intensity in water at different points within the tube.</li></ul><p> </p>
Numerical data set belonging to: 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase'
<p>This data set corresponds to the paper 'A Finite Volume Parallel Adaptive Mesh Refinement Method for Solid-Liquid Phase Change', submitted to Numerical Heat Transfer, Part A: Applications. The numerical data is included in VTK format (to be read by paraView) for the following cases:</p><p>1) 2D Gallium melting in a rectangular cavity (70x50 elements, 140x100 elements, 280x200 elements, 560x400 elements, 1120x800 elements and adaptive mesh)</p><p>2) 3D Gallium melting in a hexagonal cavity (adaptive mesh)</p><p>3) 2D freeze-plug (both steady-state and melting transient): 110x300 elements, 220x600 elements, 440x1200 elements and adaptive mesh)</p><p>Due to the size of the data-set, the data has been split over 8 tar archives featuring a gzip compression. To unpack the data, run the command: cat paper_<i>amr</i>_<i>data.</i>tar.gz.* | tar xzvf -</p><p> </p>
Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics (Model Data)
<p>Model Data to reproduce plots from article "Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics". README contains information on where to access model and visualization tools.</p>
Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"
<p>Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the <strong><em>Journal of Advances in Modeling Earth Systems</em></strong> (JAMES).</p> <p>Scripts: Python and NCL</p> <p>Data: Netcdf, PNG, Python pickled objects</p>
Mixing phenomena in circular and rectangular cross-sectional T-mixers: Experimental and numerical assessment
<p>In the present work, the mixing performance of square and circular cross-sectional millimetre-sized T-mixers was experimentally and numerically investigated. The pure dilution of non-reacting species (blue and yellow food dyes) and the chemical reaction of two-coloured reactants (i.e., strong acid and strong base with a pH indicator) were captured experimentally by adopting a low-cost colorimetric digital image analysis technique. Turbulence was modelled by k − ε model coupled with the β-PDF approach to account for turbulence-chemistry interactions. The circular cross-sectional T-mixer showed better mixing efficiency than the square one. Moreover, an even larger mixing performances were obtained by setting unbalanced fluid flow rates. To exploit the low computational demanding k − ε model, the Cε<span><span><span>2</span></span></span> parameter was tuned, showing good reliability in describing the phenomena in the mixers. Mixing time estimations were based on both time averages and instantaneous quantities, and some possible limits of the approaches commonly adopted in the literature were identified.</p>
Wave-induced mixing in a numerical wave flume
<p>This dataset is a supplement to the article published in Earth System Dynamics. The article describes a semi-analytical solution to an advection–diffusion equation coupled with a nonlinear wavemaker model to investigate the effect of strong nonlinearity on wave-induced mixing. The numerical model is based on a pseudo-spectral solution of advection-diffusion equation and two theoretical approaches to the wavemaker problem - weakly-nonlinear analytical model and numerical model admitting higher nonlinearities. The results of numerical modeling of wave kinematics and associated mixing in a wave flume are provided.</p> <p>The dataset comprises numerically predicted velocity and temperature fields. Matlab scripts are available, which display the data. Moreover, Matlab scipts for solution of advection-diffusion equation with a weakly-nonlinear input are provided (description of the model is presented in the journal paper). Please refer to a readme file for further details.</p> <p>The data and the code may be freely used and modified.</p> <p> </p>
Supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is a supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p>The time evolution of horizontal distribution of LWP:</p> <ul> <li>SDM_lwp_2d_sdm.mp4: from nine SDM runs with different grid resolutions.</li> <li>SN14_lwp_2d_sn14.mp4: from nine SN14 runs with different grid resolutions.</li> </ul> <p>The time evolution of vertical profiles:</p> <ul> <li>sdm_profile_t.mp4: from nine SDM runs with different grid resolutions.</li> <li>sn14_profile_t.mp4: from nine SN14 runs with different grid resolutions.</li> <li>sdm_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SDM runs.</li> <li>sn14_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SN14 runs.</li> <li>sdm_50x5_incloud_t.mp4: comparison between original SDM and SDM without sedimentation.</li> </ul>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</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>
Dataset of velocity and density fields from numerical simulations
<p>This dataset contains the outcomes of numerical simulations conducted for a research paper titled "Transformation of internal solitary waves at the edge of ice cover" with the use a non-hydrostatic model (Maderich et al., 2012) and code of the non-hydrostatic model. <br>Folder FIG3 contains a *.zip archive with data corresponding to the following parameters: x-coordinate length (m), z-coordinate depth (m) and module of horizontal velocity field (m/s). Folders FIG4 and FIG6 within the archive contain information on the following parameters: x-coordinate length (m), z-coordinate depth (m) and density field (kg/m^3).<br>Folder MODEL contains fortran source code files, input files, and a short model description.</p>
Data from: Numerical data for systemic effects of photoactivated 5,10,15,20-tetrakis(N-methylpyridinium-3-yl) porphyrin on healthy Drosophila melanogaster
<p>Porphyrins are commonly used as photosensitizing agents (PA) in photodynamic therapy (PDT), a non-invasive method predominantly used for the treatment of subcutaneous tumors. Development of novel PA with increased tissue selectivity and effectiveness is important for the wider use of PDT in the treatment of various types of cancers. We used <em>Drosophila melanogaster</em> as a model organism to study the systemic effects of 5,10,15,20-tetrakis(<em>N</em>-methylpyridinium-3-yl)-porphyrin (TMPyP3). First, we defined the optimal feeding protocol and showed that TMPyP3 was absorbed and retained longer in the neuronal than the non-neuronal extracts. Hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) concentration increased 24 hours after the photoactivation of orally delivered TMPyP3 but only in the head extracts. After 7 days, regardless of the photoactivation, TMPyP3 led to lower concentration of H<sub>2</sub>O<sub>2</sub>, which correlated with the decreased climbing ability measured as negative geotaxis. The results suggest that systemic treatment with TMPyP3 may interfere with redox regulation and thus impair cellular signaling and behavioral output. Further studies are needed to establish the disruptive effect that porphyrins have on redox homeostasis, how long it persists, and mechanistic differences of retention between different tissues.</p>
RAPID input and output files corresponding to "Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset consists of output files of the study reported in:</p> <ul> <li>Tom, M., David, C.H., Marlis, K.M., Zimdars, P.A., Bonassies, Q., Wade, J., Cerbelaud, A., Pavelsky T., Huang, T. (In Review), Numerical Modeling as a Service on the Cloud: A Case Study of River Modeling.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary<br></strong><br>This dataset contains the results of RAPID river discharge simulations (January 1980, February 1980) for the Mississippi river basin using the CURRNT framework. The surface and subsurface runoff data were retrieved from NASA GLDAS Phase 2 dataset (Rodell et al., 2004) at a 3-hourly temporal resolution.<strong><br></strong></p> <p><strong>Software</strong></p> <p>The software used to produce the files in this dataset is available at <a href="https://github.com/czarmanu/currnt" target="_blank" rel="noopener">https://github.com/czarmanu/currnt</a>.</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to Mississippi River Basin.</p> <p><strong>Description of files</strong></p> <p>All files below were prepared by Manu Tom, using the software mentioned above.<br><br>1980-01</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for January 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the initial state of RAPID (zeros populated for Qout).</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for January 1980 from RAPID corresponding to the downstream point of each reach.</li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for January 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-02</p> <ul> <li><em>GLDAS_VIC_3H_1980-01_utc.nc4.</em> This <em>netCDF</em> file contains averaged and concatenated GLDAS 3-hourly products (1.0 degree, version 2.0, downloaded from NASA Earthdata using NSIDC earthaccess library) for February 1980.</li> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-01-31.</em></li> <li><em>Qout_pfaf_74_GLDAS_VIC_3H_1980-01.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) for February 1980 from RAPID corresponding to the downstream point of each reach. </em></li> <li><em>m3_riv_pfaf_74_GLDAS_VIC_3H_1980-01_utc.nc4</em>. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) for February 1980 from surface and subsurface runoff into the upstream point of each river reach. </li> </ul> <p>1980-03</p> <ul> <li><em>Qinit_pfaf_74_GLDAS_VIC_3H_1980-03.nc. This netCDF file contains the final state of RAPID after a simulation ending on 1980-02-29.<br><br></em></li> </ul> <p><strong>Other necessary links associated with this dataset:</strong></p> <p>RAPID model (David et al., 2011): <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a></p> <p>RRR: RAPID model pre-processor (David et al., 2019): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a></p> <p>GLDAS VIC 3H v2.0 outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20VIC%203H%20v2.0</a></p> <p>NSIDC Earthaccess library: <a href="https://github.com/nsidc/earthaccess">https://github.com/nsidc/earthaccess</a><br><br><strong>References</strong></p> <p>David, C. H., Maidment, D. R., Niu, G. Y., Yang, Z. L., Habets, F., and Eijkhout, V. (2011), River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a></p> <p>David, C. H., Hobbs, J., Turmon, M., Emery, C., Reager, J. T., Famiglietti, J. (2019). Analytical propagation of runoff uncertainty into discharge uncertainty through a large river network. Geophys. Res. Lett. 46, 8102–8113, <a href="https://doi.org/10.1029/2019GL083342.492">https://doi.org/10.1029/2019GL083342.492</a></p> <p>Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., et al. (2004). The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a></p>
Compression-driven viscous fingering: Experimental and numerical data
<p>These data are from experiments and numerical simulations of viscous fingering in a rigid, radial, oil-filled Hele-Shaw cell driven by the compression of a connected air reservoir. The methods and data are presented in the authors' joint-submissions and their supporting information:</p> <ul> <li>L. C. Morrow, C. Cuttle, and C. W. MacMinn, Gas compression systematically delays the onset of viscous fingering, Physical Review Letters 131, 224002 (2023).</li> <li>C. Cuttle, L. C. Morrow, and C. W. MacMinn, Compression-driven viscous fingering in a radial Hele-Shaw cell, Physical Review Fluids 8, 113904 (2023).</li> </ul> <p>We here provide all raw data for 2 experimental repetitions and 5 numerical repetitions, over the full range of parameters reported.</p> <p>See README file for full details.</p> <p>VERSION 2: The pressure data are now given in the correct units of Pa. Note that the pressure data in Version 1 are identical, except for the fact that they are mistakenly given in units of PSI.</p>
Thermal coupling mode in mantle-outer core convection predicted from an ultra-high-resolution numerical simulation of two-layer convection with a large viscosity contrast
<p>Movie of temperature and velocity fields in the highly viscous layer (HVL) and the low-viscosity layer (LVL) (left panels) and the close-up views focusing on the interior of the LVL (right panels). The viscosity contrast between the HVL and LVL is 10<sup>4</sup>.</p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</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.