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Dataset results
567 results for “Simulated datasets”
Paper dataset of "ARAMIS: a Martian radiative environment model built from GEANT4 simulations"
<p>Supplementary materila dataset concerning the article : <a href="https://doi.org/10.1051/swsc/2024032">https://doi.org/10.1051/swsc/2024032 .</a></p> <p>In the version v1, the proton flux where not giving for both cone and total view which is corrected here. Beside there was a wrong upload file for proton_aramis_2015_2016.dat, which has been corrected as well.</p>
Simulation dataset for New Insights on the High Reconnection Rate and the Diminishment of Ion Outflow
<p>The dataset contains simulation data used in the paper of "New Insights on the High Reconnection Rate and the Diminishment of Ion Outflow". Data are generated from a particle-in-cell simulation using the VPIC code. Please see the paper for the descriptions of the simulation setup.</p> <p>The fields_moments.gda files in runs.7z are fields and plasma moments data of the simulations presented in the paper. The time step in unit of twci for each simulation is: A1:0.025, A2:0.1, A3:0.25, A4:0.25, A5:0.5, B1:0.1, B2:0.1, B3:0.1, B4:0.025, B5:0.05, C1:0.02, C2:0.02. Each file contains float-type data arrays with a size of (Lx/Δ)×1×(Lz/Δ), corresponding to x-y-z dimensions, respectively. Lx, Lz and Δ for each simulation are presented in the paper. Data can be read by softwares like IDL, python, matlab, etc., using the standard data reading methods.</p> <p>The runs_Hparticle_t_x_z files contain ion particle data for the time step t, for the domain of x=[x-0.03, x+0.03]di, z=[z-0.03,z+0.03]di. The files are IDL save files. To read the data, use restore, file=file in IDL. Each data chunk constitues of the following variables in order: all_vxs, all_vys, all_vzs, ws, all_xs, all_ys. Here all_vxs, all_vys and all_vzs are velocities in unit of light speed (c); all_xs and all_ys are x and z locations in unit of de; ws represents the weight of the particles, so the contribution of a particle to the phase space density needs to multiply by this ws factor (In the paper, ws factors are re-normalized while the relative weights between particles remain the same).</p>
Dataset and simulation files on Quantifying Wetting Dynamics with Triboelectrification
<p>The uploaded data contains software and results about the simulation of Triboelectrification and Wetting Dynamics of Multiscale Polymer Surfaces.</p> <p>Folders structure:<br> A) Folder name: NanopillarTextureDropletSim<br> 1) Wolfram Mathematica Notebook file "laplace.solvers.random.nb";<br> Code for the simulation of droplet wetting on a nanopillar forest (nanopillar surface imported from input file). It consists of three sections: one for FEM simulation on deterministic forest arrangement (section fem), one for analytical modelling of droplet wetting on random forest (section fem random measured - analytical), and the final section with the FEM simulation on the random forest (fem random generated - FEM). The different sections return embedded results (graphs or numerical) and results saved to file.<br> 2) Input file of (single realization of) nanopillar forest "pillars.random.xls";<br> Single realization of nanopillar forest, xls file. Measured from optical acquisition. Needed as input for the "laplace.solvers.random.nb".<br> B) Folder name: MicrocubeTextureDropletSim<br> 1) Wolfram Mathematica Notebook file "MicroTextureGenerator.nb";<br> Code for generation of the random microcubes texture. The different sections return embedded results (graphs or numerical) and results saved to file, such as the file "functionpython.py" used as input surface for FEniCS wetting mchanics simulation.<br> 2) FEniCS input file "contact.angle.3D_mesh.refinement.v3.2018.py";<br> Python code to run simulation of wetting dynamics with FEniCS FEM solver. The code consists of a main computation loop and several subroutines, each dedicated to perform specific computation step (such as adaptive mesh refinement nearby triple lines). The most important numerical parameters are N (e.g. with assignment N = 128) controlling the initial side discretization number of the computation square, and max_number_of_refinements (e.g with assignment max_number_of_refinements = 4) which controls the number of consecutive refinements of the solution near the triple lines. The code will save several *.pvd files containing the deformed droplet surface (top surface) and corresponding bottom surface. The code will also save the file "wet_area.dat", which contains (in each line) the (dimensionless) applied squeezing pressure, the normalized wetting area and the droplet free-surface normalized area.<br> 3) Wolfram Mathematica Notebook file "contact.angle.micro.one.plate.nb";<br> The file is used to simulate the whole droplet contact on single plate, under constant droplet volume. The different sections return embedded results (graphs or numerical) and results saved to file. As input, among the other physical parameters, the effective contact angle is needed.<br> 4) Wolfram Mathematica Notebook file "contact.angle.micro.nb";<br> The code consists of two sections, the first for droplet on single plate and the second for the droplet among two plates. In particular, the second section allows to determine the penetration vs squeezing load for the two-plates case. As input, among the other physical parameters, the effective contact angles are needed.<br> C) Folder name: Sloshing<br> 1) Wolfram Mathematica Notebook file "cylinder.sloshing.nb";<br> It contains code for the simulation of sloshing dynamics in a partially-filled tank, as well as extra analysis on the sloshing dynamics.<br> D) Folder name: Example.FEniCS.results<br> It contains a full set of FEniCS simulation results (at three simulated squeezing pressures) for the input python code "temp.contact.angle.3D_mesh.refinement.v3.2018.py".</p>
AMTraC-19 (v7.7d) Dataset: Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia
<p>A preprint paper describing scenarios which generated this dataset can be accessed here: https://arxiv.org/abs/2107.06617. Please cite this work when using the dataset:<br> S. L. Chang, C. Zachreson, O. M. Cliff, M. Prokopenko, Simulating transmission scenarios of the Delta variant of SARS-CoV-2 in Australia, arXiv: 2107.06617, 2021.</p> <p>Abstract. An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing"). Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model successfully predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.</p> <p>The AMTraC-19 source code (v7.7d) is released on Zenodo: https://zenodo.org/record/5778218</p>
Dataset for: Phosphorus mobilization from intact soil monoliths flooded under simulated summer versus spring snowmelt with intermittent freeze-thaw conditions
<p>Enhanced phosphorus (P) release from flooded, anaerobic soils have been extensively studied under summer temperatures, but not under cold temperatures with intermittent freeze-thaw events. We investigated the temperature and freeze/ thaw effects during flooding on the release of P to floodwater from soil monoliths (15-cm depth) collected from eight agricultural fields in Manitoba. Soil monoliths were flooded with reverse osmosis water and incubated for 56 d under simulated summer flooding (SSF, 22±1 ℃), or snowmelt flooding with intermittent freeze thaw (IFT, 4±1 ℃ with intermittent freezing) in triplicates. Redox potential (Eh), pore water and floodwater dissolved reactive P (DRP) concentrations, pH and concentrations of Ca, Mg, Fe and Mn were determined weekly. In seven soils, Eh decreased rapidly with days after flooding (DAF) under SSF to values < 200 mV, but not under IFT. Pore water and floodwater DRP concentrations significantly increased with DAF in all soils under SSF, and in seven soils under IFT. While DRP concentrations were consistently greater under SSF than IFT in four soils, other soils had similar concentrations at certain DAFs. Significant relationships between ion concentrations and redox status , that fitted both IFT and SSF data in most soils, suggests that similar redox-driven mechanisms are responsible for the P release; however, less P is released under IFT than SSF, since soils were not severely reduced under IFT. Substantial P release in a few soils under IFT, appeared to be unrelated to redox status, suggesting other P release mechanisms that are not redox driven.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches"
<p>Dataset of Paper "Optimization and parallelization of the Discrete Ordinate Method for radiation transport simulation in OpenFOAM: Hierarchical combination of shared and distributed memory approaches":</p> <ul> <li>Time profiling of the DOM model stages.</li> <li>Speed-up and parallel fraction of the “global results generation” stage in the DOM model.</li> <li>Comparison of computational time between original and modified DOM model stages.</li> <li>Scalability of the peer-to-peer communication (OpenFOAM) and master-slave communication (ANSYS Fluent) architectures.</li> <li>Results of the benchmarking of the model with ANSYS Fluent in three reactors.</li> <li>Mesh of the jerrycan.</li> <li>Mesh of the anular reactor</li> <li>Mesh of the tubular reactor couple to a compound parabolic collector.</li> </ul>
Simulated and Real datasets used for benchmarking MARGARET
<p>A collection of real and simulated datasets used for benchmarking MARGARET against other state-of-the-art TI methods</p>
Simulation dataset of "Deformation of electron distributions due to Landau trapping by the whistler-mode wave"
<p>The file "Figure1a_data.dat" stores the data which is used to plot Figure1a. The file "Figure1cd_data.dat" stores the data which are used to plot Figure1c and Figure1c. And the other files are similar. </p> <p>The name and array of each parameter corresponding to that in Figures are stored in the files.</p>
Dataset for XBeach model calibration and simulation of sample storms in an intermediate-to-dissipative, microtidal coast (Sabaudia, Italy)
<p>The archive contains XBeach input data and datasets used for the calibration of the hydrodynamic model XBeach and subsequent simulation of six sample storms at different tidal levels at the intermediate-to-dissipative, microtidal coast of Sabaudia (Tyrrhenian Sea, Italy).</p> <p>VERSION v2 (January 24, 2022): uploaded a new version of the dataset to support the revised version of the manuscript.</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</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>
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.