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864 results for “numerative”
Numerical dataset for quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback
<p>The data consist of numerical results obtained from quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback.</p> <p>These data are used in the preprint titled <em>"Heat current and fluctuations between a dissipative qubit and a monitor under continuous measurement and feedback."</em></p> <p>We performed numerical simulations of the stochastic master equation using the supercomputer at ISSP.</p> <p>The contents are as follows:</p> <ul> <li>Numerical results for the correlation functions (<code>F*.dat</code>).</li> <li>Numerical results for the Fano factor and the power spectrum (<code>fano_factor*.dat</code>).</li> <li>Python code to calculate the Fano factor and the power spectrum from the correlation functions (<code>fano_factor_v6.py</code>).</li> <li>Figures illustrating the Fano factor and the power spectrum (<code>fig*.eps</code>).</li> </ul>
Data from Deng et al. (2025) "Secular Change of Preservation of Lunar Sinuous Rilles: Constraints from Lava Flow Numerical Modeling"
<p>These are the updated supplemental materials of our article, adding more groups of simulation and a sensitivity analysis of two parameters, cell width and plane slope.</p>
NuBe-DBBM: Numerical Benchmark for Drive-By Bridge Monitoring methods
<p>This repository contains an extensive dataset of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. In addition, the dataset includes results for different road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field of drive-by bridge monitoring.</p> <p>The dataset is made of a collection of individual files, each containing results and information about single vehicle crossing events. The dataset provides results for different dimensions of the problem, which are: monitoring scenario (DSA, DSB), bridge spans (B09, B015, B21, B27, B33, B39), damage location (DL25, DL50), damage magnitude (DM00, DM020, DM40), vehicle model (V1, V2, V5), road profile (P00, PA1, PA2), and event number (E0001, E0002, …, E0800). In total, the dataset contains 518 400 separate files conveniently categorized into a system of subfolders. Each file contains the simulated responses from a 2D representation of the vehicle-bridge interaction problem in Matlab environment. The files here are in <em>.mat</em> format. Refer to the document <em>ReadMe.pdf</em> for extended explanations about the filing structure and file contents. In addition, an extended description of the dataset and numerical modelling can be found in the associated journal publication listed below.</p> <p>Cantero D, Sarwar Z, Malekjafarian A, Corbally R, Makki Alamdari M, Cheema P, Aggarwal J, Noh HY, Liu J. Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods. Archives of Civil and Mechanical Engineering, Vol. 24, Article number 190, 2024.</p> <p>DOI: <a href="https://doi.org/10.1007/s43452-024-01001-9">https://doi.org/10.1007/s43452-024-01001-9</a></p>
Inputs to an idealized numerical model based on MITgcm and model results
<p>This dataset contains input files for model runs used in Leng et al. 2021, "Temporal Evolution of a Geostrophic Current under Sea Ice: Analytical and Numerical Solutions" (to be submitted to JPO). The model used in this study is an idealized channel configuration based on the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m. Some representative output is also included.</p> <p>code: contains code configuration.</p> <p>input: contains parameters, initial conditions, forcing, etc.</p> <p>output: contains model output.</p>
Optical properties of mineral dust aerosols with non-absorptive coating: a numerical investigation
<p>Model data for optical calculations of non-absorptive coated mineral dust aerosol</p> <p>This repository provides data used for plots in a manuscript submitted to Optics Express. The folder contains optical data from calculations performed with the T-matrix code. The subdirectory aspect ratio contains results for eleven aspect ratios with the coating ratio equal to 0.5 stored in respective directories. The directory coating ratio contains results for eleven coating ratios with an aspect ratio of 1.37. The directory naming of folders within the subdirectories derives from the parameter choice as indicated in the manuscript.</p>
Numerical simulation of groundwater flow in backfilled open-pits for BDZ deviation performance
<p>This dataset contains simulated groundwater flow entering multiple backfilled open-pits with mine wastes (flowrate results) and some Python script to visualize the data.<br> The objective was to predict the blast damage zone (BDZ) created by mine excavation on the flowrate through the backfilled wastes.<br> Flowrates were obtained by 3D numerical simulation using PFLOTRAN finite volume code (www.pflotran.org).<br> Conceptual model of the BDZ and equivalent permeability was derived from Rousseau and Pabst (in press) and Mourzencko et al. (2012) (see https://hal.archives-ouvertes.fr/hal-01196684/file/mourzenko2012.pdf).<br> Number of cases simulated: 40500, with 40496 converged numerical simulations.<br> Calculated were performed using the High Performance Computing Ressource of Calcul Quebec (www.calculquebec.ca/) and Compute Canada (www.computecanada.ca/)</p>
Numerical simulations of singular radiative fields in scattering media with refraction
<p>Monte-Carlo radiative transport simulation results along with the source code and auxiliary software</p>
Numerical simulations of Apollo S-IVB artificial impacts on the Moon
<p>Data set used in producing plots and data analysis in the paper Numerical simulations of Apollo S-IVB artificial impacts on the Moon, submitted to the ESS.</p> <p>Includes also input files for running simulations described in the paper.</p>
Numerical discrimination of sets of elements by Archerfish
<p>Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (<em>Toxotes jaculatrix</em>) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 <em>vs. </em>6 elements; Exp. 2: 2 <em>vs.</em> 3 elements); these were controlled for several combinations of physical variables (elements' size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 <em>vs.</em> 3, 5 <em>vs.</em> 8 and 6 <em>vs.</em> 9 in Exp. 1; 3 <em>vs.</em> 4, 3 <em>vs.</em> 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.</p>
Dataset for "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange"
<p>This dataset contains the sediment models, DNS flow field data, subsurface path data, and calculated transit time distributions for both the regular- and random-interface cases used in the paper: "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange" by Guangchen Shen, Junlin Yuan, and Mantha S. Phanikumar (Submitted to Water Resources Research). <br> Detailed introduction of each data file is as follows.</p> <p>1. DNS flow field data</p> <p>Flowfield_Reg.h5 and Flowfield_Ran.h5 contains the following fields for the regular and random cases, respectively. 'ni', 'nj', 'nk' are the numbers of grid points along x, y, and z directions. 'xc', 'yc', 'zc' are the cell center locations. 'u3d','v3d','w3d' are the three-dimensional time-averaged velocities at each grid point. 'vof' is the volume-of-fluid field used by the immersed-boundary method to prescribe the fluid-solid interface (vof=1 in fluid and 0 in solid), at each grid point. 'vof' contains the information of sediment grain distribution and bed roughness geometry. Only the subsurface data (those below the sediment crest) are shared due to dataset size limit.</p> <p>2. Particle-tracked subsurface flow paths and corresponding transit time distributions</p> <p>The mat files “xxx_pathline” store the (x,y,z) location of each point (saved as 'StrX', 'StrY, 'StrZ') along the subsurface paths, discretized by uniform steps of travel time (with time intervals of 0.1 for 'A' and 0.01 for 'MD' cases, normalized by channel height and friction velocity). The files “xxx_TT” store the array of transit times corresponding to the tracked paths, where the 1d array T is the transit time. 'A' denotes calculations based on time-mean advection only, while 'MD' denotes calculations accounting for additional molecular diffusion. 'Interface' and '3DiameterBelow' indicate that the particles were released at the interface and -3 D below the interface, respectively.</p>
Data accompanying the paper "Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies".
<p>Data accompanying the paper <strong>"Stable Numerical Implementation of a Turbulence Scheme with Two Prognostic Turbulence Energies",</strong> submitted to Monthly Weather Review.</p> <p>This repository contains results from the idealized simulations of GABLS1 case,<br> obtained by the OpenIFS single column model version with the TOUCANS two-energy<br> turbulence scheme.</p> <p>Each simulation has its own folder, containing single NetCDF file 'progvar.nc'.<br> Experimental settings, explained in the paper, are following:</p> <pre><code>----------------------------------------------------------------- folder timestep delta beta beta_tau [s] [1] [1] [1] ----------------------------------------------------------------- GABLS1_2TE-R_dt001s_delta00_bt15/ 1 0.00 1.0 1.5 GABLS1_2TE-R_dt045s_delta00_bt15/ 45 0.00 1.0 1.5 GABLS1_2TE-R_dt090s_delta00_bt15/ 90 0.00 1.0 1.5 ----------------------------------------------------------------- GABLS1_2TE-R_dt090s_delta25_bt15/ 90 0.25 1.0 1.5 GABLS1_2TE-R_dt090s_delta25_bt10/ 90 0.25 1.0 1.0 GABLS1_2TE-R_dt180s_delta25_bt10/ 180 0.25 1.0 1.0 -----------------------------------------------------------------</code></pre> <p>Names of the NetCDF fields examined in the paper are:</p> <pre><code>tke - turbulence kinetic energy [J/kg] tte - turbulence total energy [J/kg] efb3 - turbulent heat flux [W/m^2] t - thermodynamic temperature [K] </code></pre> <p>They are all 2D fields, indexed by the timestep number and the model level.<br> Turbulent heat flux is given on the model half levels, remaining quantities<br> on the model full levels. Heights of the model levels are stored in the<br> NetCDF fields:</p> <pre><code>height_f - full level heights [m] height_h - half level heights [m] </code></pre> <p>Simulation time in seconds is stored in the NetCDF field 'time'.</p> <p><strong>CAUTION:</strong><br> Since the simulations were run with the prescribed forcings and only with<br> the turbulence scheme activated, some fields in NetCDF files are meaningless<br> (e.g. skin temperature).</p>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</p>
Dataset related to Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones
<p>Additional model data to publication by Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones</p>
FiNER-139: A Financial Numeric Entity Recognition Dataset
<p>FiNER-139 is published with the article: FiNER: Financial Numeric Entity Recognition for XBRL Tagging</p> <p>In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022.</p> <p>We release FiNER-139, a dataset of 1.1M sentences with gold XBRL tags. Unlike typical entity extraction datasets, FiNER-139 uses a much larger label set of 139 entity types. Most annotated tokens are numeric, with the correct tag per token depending mostly on context, rather than the token itself.</p>
Core-mantle boundary topography data from numerical simulation of instantaneous mantle flow
<p>Calculated CMB topography data for models (a) H0, (b) H0D1, (c) H4, (d) H4P3, (e) H4P6, (f) H4P3D1, (g) H4P3W, and (h) H4P3D1W. Negative (positive) values indicate topographic depression (elevation). See Figure 2 of Yoshida (2008).</p>
Experimental and numerical calculation data of contact stiffness/Surface roughness/HPT
<p>Understanding the dynamic condition of the interface between a railway wheel and rail is important to reduce the risks and consider the effectiveness of countermeasures for tribological problems. Traditionally the difficulty in obtaining accurate non-destructive interfacial measurements has hindered systematic experimental investigations. Recently an ultrasound reflectometry technique has been developed as a direct observation method of a rolling-sliding interface, however the topography dependence under the high contact pressures in a wheel-rail contact has not been clarified. For this reason, a novel in-situ measurement of the contact stiffness using ultrasound reflectometry was carried out for three different levels of roughness. A contact pressure equivalent to that in a wheel-rail interface was achieved by using a high-pressure torsion test approach. The dynamic change of contact stiffness with slip was measured using ultrasound and the influence of roughness was investigated. The measured changes were validated using a newly developed numerical simulation, and mechanisms to explain the observed behaviour were proposed in terms of fracture and plastic deformation of the asperity bonds. These findings could help in understanding the traction characteristics for different roughness conditions and also assist in understanding damage mechanisms better, such as wear and rolling contact fatigue.</p>
Calculating Pile Downdrag: Experimental and Numerical Investigations
<p>Piles can be exposed to significant settlement (i.e., downdrag) and compressive force (i.e., dragload) if embedded in consolidating ground. Downdrag has been included in the design of piles, but there is uncertainty about how downdrag and dragload are efficiently considered in the pile design due to the differences in the existing design methods (e.g., AASHTO and FHWA). Although several field monitoring programs were successfully conducted on pile downdrag, fully instrumented pile model tests are still needed to investigate the responses at the soil-pile interface to improve the design of pile downdrag. This research focused on investigating the downdrag and skin friction at the soil-pile interface using fully instrumented small-scale pile tests. An aluminum pile (51×102×508 mm) was subjected to downdrag in a consolidating loose sand deposit. Innovative interface shear stress sensor (S3F) and strain gauges were installed on the pile surface to measure the drag forces and skin frictions at the soil-pile interface. Settlement plates were installed in the soil at different locations to measure the soil settlement profile. The S3F sensor was calibrated in the lab. Position transducers were also attached to the head and tip of the pile to measure the pile settlement profile. The lab test results will be used to calibrate a numerical model that can predict pile downdrag with a high level of accuracy, which can provide a more realistic estimation of pile downdrag, avoid the conservative pile foundation design, and possibly reduce the foundation cost.</p>
Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean - instantaneous numerical outputs
<p>The data set contains the netcdf files with the instantaneous fields produced by the WRF V3.6.1 numerical model at 2014-10-06_01:00:00 over the Ligurian Sea analyzed in Meroni, A. N., F. Desbiolles and C. Pasquero "Introducing new metrics for the atmospheric pressure adjustment to thermal structures in the ocean".</p>
NUMERICAL ANALYSIS OF FLOW STRUCTURE AND HEAT TRANSFER IN BUBBLING FLUIDIZED BEDS
<p>The videos show the movements of selected particles in different geometries of fluidized bed heat exchangers. Two geometries without auxiliary measures and one with air cushion technology are shown.</p>
Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate"</p> <p>There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters.</p> <p>In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.</p>
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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.