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864 results for “numerative”
NIMROD growth rates (1/s) by finite element poly_degree (columns) and case from "The Impact of Collisionality, FLR and Parallel Closure Effects on Instabilities in the Tomakak Pedestal: Numerical Studies with the NIMROD code”
<p>NIMROD growth rates (1/s) by finite element poly_degree (columns) and case from "The Impact of Collisionality, FLR and Parallel Closure Effects on Instabilities in the Tomakak Pedestal: Numerical Studies with the NIMROD code” as submitted to Physics of Plasmas</p>
Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension (Supporting data)
<p>This data accompanies the paper "Artificial viscosity model to mitigate numerical artefacts at fluid interfaces with surface tension", published in Computers & Fluids.</p>
The Impact of a Pressurized Regional Sea or Global Ocean on Stresses on Enceladus: Numerical models
<p>Comsol Multiphysics models solving the stress field in Enceladus' ice shell when a regional sea of global ocean is pressurized.</p> <p>Model parameters are included as part of the file name according to the template EnceladusT<em>t</em>D<em>d</em><em>Label</em>.mph with</p> <ul> <li><em>t</em> is the ice shell thickness</li> <li><em>d</em> is the thickness of the south polar sea or indentation</li> <li><em>Label </em>indicates model configuration <ul> <li><em>Fixed</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a no-slip boundary condition</li> <li><em>Roller</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a free-slip boundary condition</li> <li><em>Ocean</em>: The base of the ice shell is floating with a constant pressure condition; there is a single indentation at the South pole</li> <li><em>North</em>: The base of the ice shell is floating with a constant pressure condition; there are indentations at both poles, with the north pole indentation having half the thickness of the South pole indentation</li> </ul> </li> </ul> <p>There are two solved datasets in each model. The first uses a default value of the ocean angle (40°). The second results from a parameter sweep in which the sea angle varies systematically in increments of 2°.</p>
Data from Hesse et al. 2017: Preattentive Processing of Numerical Visual Information, Front Hum Neurosci., 11:70, 2017. doi: 10.3389/fnhum.2017.00070.
<p><strong>Data related to the following publication: </strong></p> <p>Hesse Philipp N., Schmitt Constanze, Klingenhoefer Steffen, Bremmer Frank (2017). Preattentive Processing of Numerical Visual Information. Frontiers in Human Neuroscience, 11: 70. doi: 10.3389/fnhum.2017.00070</p> <p><strong>Brief description of dataset:</strong></p> <p>The stimulus was presented on a TFT monitor (size: 41,8° x 24,3°) 52 cm in front of the participants in a dark, sound attenuated and electrically shielded room. During the experiment EEG was recorded continuously. We used 64 Ag/AgCl active electrodes located according to the extended international 10-20 system. </p> <p>The numerosity stimulus consisted of a continuously displayed black fixation target in the center of the gray screen. Additionally in each trial either one, two or three circular white patches were shown 200 ms after trial onset. These were presented for a random duration between 400 ms and 500 ms either in the left or right visual field. Two different types of patches were presented: i) the radius of the patches had the same value (0.65°) and therefore the patch size was the same (“SizeCon”) ii) the total area of the patches was conserved which resulted in the same total luminance independent of the number of patches (“LumCon”). After a random time between 400 ms and 700 ms after stimulus offset the trials ended.</p> <p>In this study we conducted an oddball experiment with an oddball-ratio of 1:4. In each block consisting of 30 trials a standard-amount of patches (one, two or three) was presented in 80% of all trials (24 trials). The two remaining quantities of patches were shown in 10% (3 trials) of the trials each. This presentation scheme allowed us to compare trials with identical physical properties because each amount of patches served as deviant and standard trial in different blocks. Attention of the participants was drawn off the white patches by a demanding detection task at the fixation target. A total number of 432 blocks consisting of 30 trials was presented to each of the 10 participants.</p> <p>EEG data were evaluated offline. The mastoids (TP9 and TP10) were chosen as new reference. A second-order, zero phase shift Butterworth filter with cutoff frequencies 0.5 and 40 Hz was applied to the continuously recorded data before it was sliced in individual trials that had a time range from 200 ms before to 500 ms after stimulus onset. A baseline correction was performed using with the signals from -110 ms to 0 ms. As a last step trials with eye movement artifacts or electrode signals that exceeded a difference of ±100 µV within an interval of 100 ms were excluded in an artifact rejection step.</p> <p> </p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Numerical simulation of the equatorial plasma bubble: the effect of seeding by the vertical winds and random background noise perturbations.
<p>A wide variety of small-amplitude waves widely exist in the ionosphere and have significant effects on the evolution of equatorial plasma bubbles. In this paper, we simulated equatorial plasma bubbles (EPB) seeded by vertical neutral wind perturbations with wavelengths of 125 km and 250 km, and compared the morphology characteristics of plasma bubble structures with those under random noise perturbations in the background density. The numerical results showed that both vertical winds and random background noise perturbations can contribute to the growth of plasma bubbles, and the perturbations under additional random background noise can promote the growth of the plasma bubble structures faster. Additionally, several processes of the nonlinear behavior of bifurcated EPB structures, including bifurcation, pinching, and small-scale turbulent structures, were successfully obtained. Our simulation captured supersonic flows within the low-density plasma structures characterized by vertical velocities of about 1.5 km/s, which is consistent with experimental studies found in the literature.</p>
Supplementary data and scripts for the publication "Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO"
<p>##Information of directories. Each directory contains necessary data and scripts to do the corresponding analysis</p> <p>1. Benchmarking/: benchmarking analysis for heliano. (For Figure 3, Supplementary Table S2)<br>2. GeneCapture/: analysis for captured genes within HLEs. (For Figure 6, Supplementary Table S11)<br>3. HLE_model_species/: analysis for heliano results on three model species. (For Table 1, Figure 4, Supplementary Table S5-9, Supplementary Figure 4)<br>4. HLE_sampled_species/: analysis for heliano results on 404 sampled species. (For Figure 5, Supplementary Table S10, Supplementary Figure 5)<br>5. hmm_model/: build hmm model for Hel and Rep domains. (For RepHel.hmm that is important to detect and classify HLEs)<br>6. Phylogenetical_tree/: making phylogenetical trees. (For Figure 1, Supplementary Figure 2-3)<br>7. Sample_species/: about how to sample species from various genome assemblies. (Supplementary Table S3)<br>8. HLE_feature/: information about the position of RepHel domain and the length of HLE sequences. (Supplementary Figure 1)</p>
Numerical data for "Signatures of Kondo-Majorana interplay in ac response"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Signatures of Kondo-Majorana interplay in ac response</em>, published as Phys. Rev. B <strong>109</strong>, 075432 (2024), DOI: 10.1103/PhysRevB.109.075432, and other data obtained within the same project.</p>
Numerical simulation of friction extrusion: Process characteristics and material deformation due to friction
<p>This study employs a finite element thermo-mechanical model, using a Lagrangian incremental setting to investigate friction extrusion (FE) under varying process conditions. The incorporation of rotation in FE generates substantial frictional heat, leading to significantly reduced process forces in comparison to conventional extrusion (CE). The model reveals the interplay between temperature, strain, and strain rate across different microstructural zones of the resulting wire. Specifically, the sticking friction condition in FE enhances initial shear deformation, aligning with a homogeneous spatial strain distribution and predicting complete grain refinement in the extruded wire, as per Zener-Hollomon calculations. On the other hand, under the sliding friction condition in FE, the shear deformation is reduced which results in an inhomogeneous microstructure in the extruded wire. The analysis of material flow in the workpiece reveals distinct transitions from the base material to the thermo-mechanically affected zones. The simulated process force, thermal history, and microstructure during sliding friction conditions align well with the findings from performed friction extrusion experiments.</p>
Рис. 4. РаЗведочный шурф, виден слой раковин (раковинная куча) и масса створок спиЗулы сахалинской (Spisula sachalinensis) (Желтая стрелка). Fig. 4. A prospecting pit, a layer of shells (shell-midden) and numerous valves of Spisula sachalinensis (yellow arrow) are seen. in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 4. РаЗведочный шурф, виден слой раковин (раковинная куча) и масса створок спиЗулы сахалинской (Spisula sachalinensis) (Желтая стрелка). Fig. 4. A prospecting pit, a layer of shells (shell-midden) and numerous valves of Spisula sachalinensis (yellow arrow) are seen.
Numerical Calculation of the Thermodynamic Properties of Silver Erbium Alloys for Use in Metallic Magnetic Calorimeters - Data
<p>Data from simulations of the specific heat and magnetization of Ag:Er alloys. The parameter range we consider are temperatures between 1mK and 1K, external magnetic fields of up to 20mT, and erbium concentrations of up to 2000ppm.</p>
Numerical experiments of ENSO and Arctic sea ice
<p>The data named "CTRL_40yrs_output.nc", “EXP_ALL_40yrs_R.nc”, “EXP_SIC_40yrs.nc”, “EXP_SST_40yrs_R.nc” are the output of last 40 model years for CTRL, EXP_ALL, EXP_SIC, and EXP_SST, which are run for totally 60 model years based on CAM5.</p>
Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"
<p>These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).</p> <p>Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.</p> <p>The repository contains the following files:</p> <p>grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.</p> <p>Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.</p> <p>For details on the simulation, refer to the article.</p>
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
Numerical Evaluation of Spin Foam Amplitudes beyond simplices
<p>Dataset containing intertwiners and vertex amplitude evaluations for cuboid and frustum data associated with the paper arXiv:2201.09902.</p> <p>Data comes in two groups, one for cuboid boundary data, one for frustum boundary data of the coherent vertex amplitude.</p> <p>Intertwiner data are stored as text files, where the name of the files contain the three representations necessary to specify either cuboid or frustum intertwiners.</p> <p>Folders results contain the evaluations of vertex amplitudes. Again, the files contain all representations necessary to specify the hypercuboid (six representations) or hyperfrustum (three representations). See the paper for more details.</p> <p>(To get the actual values of the spins, they need to be devided by 2.)</p> <p>Code to automatically read in the intertwiner files and compute the vertex amplitude can be found at: https://github.com/CourtA96/SpinfoamAmplitudesOfCuboidsAndFrustra</p>
Research data and code for Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants
<p># Research data repository</p> <p>## Introduction</p> <p>This repository contains the research data, scripts and codes to process the simulation and generate the figures in research article: <br> <em>"Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants"</em></p> <p>This work uses the discrete 3D mesh of the thorax of a newborn that is available at: http://doi.org/10.5281/zenodo.4916863</p> <p>This article has been submitted and publied in Journal of Biophotonics:<br> - DOI: 10.1002/jbio.202200041<br> - Link: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041">https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041</a></p> <p>## Article status</p> <p> [X] Submitted <br> [X] Under review <br> [X] Corrections <br> [X] Published<br> <br> ## Content</p> <p>- Folder "data": This folder needs to be unzipped and contains the raw data from the simulation, as well as some processed data needed to generate the figures. This folder contains all the data necessary to generate the figures. However some intermediate data files (the Photon Hitting Density values interpolated on the elements of the mesh) are not given here because the files are too big. These can be created using the convertPHD2element script.<br> - Folder "function": This folder needs to be unzipped and contains some functions that are used by the scripts to process data or generate the figures<br> - Matlab .m files: The .m files are scripts that are used to generate the figures (generateFigX.m) or to process the data computeYYY.m. The description of each script and function is given in the comment section at the beginning of each file.</p> <p>## Licence<br> This data is published under the creative common CC-BY licence. You are free to use this data as long as you cite this dataset and the article (when DOI available)</p> <p>## Digital Object Identifier<br> DOI: 10.5281/zenodo.5996855</p> <p>## Authors<br> Simulation: Andrea Pacheco<br> Article writing: Andrea Pacheco<br> Data processing and figure generation: Andrea Pacheco and Baptiste Jayet<br> Conceptualisation, investigation, review and editing: Emilie Krite Svanberg, Hamid Dehghani and Eugene Dempsey<br> Project supervision: Stefan Andersson-Engels</p> <p>## Funding<br> The research leading to these results was funded by Science Fundation Ireland project no. SFI/15/RP/2828</p>
Stormtime ring current heating of the ionosphere and plasmasphere Numerical information for the figures
<p>This archive contains data files needed to reproduce selected figures from the following manuscript:</p> <p>Stormtime ring current heating of the ionosphere and plasmasphere<br> by J. Krall, M.-C. Fok, J. D. Huba, and A. Glocer</p> <p>This manuscript was submitted to Journal of Geophysical Research: Space<br> Physics in February, 2022</p> <p>Specifically, this archive contains one IDL file for each figure. If run, the<br> IDL script reads one or more of the datasets and re-creates the figure.<br> Each IDL script is a text-formatted file with a header that explains each file.<br> Individual datasets are either text-formatted or unformatted FORTRAN data<br> files. Also included is a namelist file giving SAMI3 input parameters.</p>
T2Dv2 numeric columns annotation
<p>This files contains the annotation of numeric columns of the T2Dv2 dataset.</p>
Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) - Numerical Simulations
<p>The Idealized Planar Array study for Quantifying Spatial heterogeneity (IPAQS) is the result of a National Science Foundation (US) funded project, that aims at studying the effect of surface thermal heterogeneities of different length-scale on the atmospheric boundary layer. This project consisted of a computational effort (dataset here included), and an experimental effort (dataset being prepared for publication). </p> <p><strong>Overview of the numerical (Large Eddy) simulations:</strong></p> <p>The simulations are separated into two sets to study the differences between heterogeneous and homogeneous surfaces. In the first set, a total of seven configurations are considered, all with a homogeneous surface temperature fixed at a value of <span class="math-tex">\(T_s\)</span> = 290 K, and for which the geostrophic wind speed has been increased from 1 to 15 m s<sup>-1</sup> (i.e., U<sub>g</sub> = 1, 2, 3, 4, 6, 9, 15 m s<sup>−1</sup> ). These homogeneous cases are referred to as Homog-X, where X indicates the geostrophic wind speed corresponding case (see Margairaz et al. 2020a). In the second set, the surface temperature is distributed amongst square patches, where the temperature of each patch is determined by sampling a Gaussian distribution with a mean temperature of 290 K and a standard deviation of 5 K. In this case, three different patch sizes were considered (i.e., l<sub>h</sub> = 800, 400, and 200 m). The sizes of the heterogeneities were chosen to be of similar size (l<sub>h</sub> /l<sub>d</sub> ≈ 1), half the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/2), and about a quarter of the size (l<sub>h</sub> /l<sub>d</sub> ≈ 1/4) of the largest flow motions within the represented thermal boundary layer, assuming that this is of the order of the boundary-layer height (l<sub>d</sub> ∼ z <sub>i</sub> ). These heterogeneities are typically not resolved in NWP models. These cases have been studied for the same geostrophic wind speeds indicated above, and hereafter are referred to as PYYY-X-, where X indicates the corresponding geostrophic wind speed, and YYY refers to the size of the patches (e.g., P800_Ug1_ would be the heterogeneous case with patches of 800 m, and forced with Ug = 1 m s<sup>−1</sup> ). Additionally, for the case with larger patches, three different random distributions of the patches were considered to evaluate the potential effect of a given surface distribution for all geostrophic wind speeds. In this dataset we only include case v3. The LES imposed surface temperature distributions emulate the surface thermal conditions observed in Morrison et al. (2017 QJRMS, 2021 BLM, 2022 BLM), where measurements of the surface temperature were taken with a thermal camera at the SLTEST site of the US Army Dugway Proving Ground in Utah, USA. This is an ideal site with uniform roughness and a large unperturbed fetch, where surface thermal heterogeneities are naturally created by differences in surface salinity. In all studied cases, the surface roughness is assumed homogeneous, with z<sub>0</sub> = 0.1 m, and representative of a surface with sparse forest or farmland with many hedges (Brutsaert 1982; Stull 1988). The initial boundary-layer height is set to z<sub>i</sub> = 1000 m. The temperature profile is initialized with a mean air temperature of 285 K. At the top of the initial boundary layer, a capping inversion of 1000 m is used to limit its growth. The strength of this inversion is fixed at Γ = 0.012 K m<sup>−1</sup>. The atmospheric boundary layer (ABL) is considered dry and the latent heat flux is neglected in all cases. Further, in all simulations, the surface heat flux is computed using MOST, as explained in Margairaz et al. 2020a, where the surface temperature is kept constant in time throughout the simulations. Thus, there is no feedback from the atmosphere to the surface as the surface temperature does not cool down or warm up with local changes in velocities. As a consequence, the ABL gradually warms up as the simulations progress, and hence becomes less convective over time. However, the runs are not long enough for this to be significant. In addition, to ensure a degree of homogeneity within each patch and a certain degree of validity of MOST, note that even for the heterogeneous cases with the fewest amount of grid points per patch, a minimum of eight grid points is granted in each horizontal direction. The domain size is set to (L<sub>x</sub>, L<sub>y</sub>, L<sub>z</sub>) = (2π, 2π, 2) km at a grid size of (Nx , Ny , Nz ) = (256, 256, 256) resulting in a horizontal resolution of <span class="math-tex">\(\Delta\)</span>x = <span class="math-tex">\(\Delta\)</span>y = 24.5 m and a vertical grid spacing of <span class="math-tex">\(\Delta\)</span>z = 7.8 m. A timestep of <span class="math-tex">\(\Delta\)</span>t = 0.1 s is used to ensure the stability of the time integration. The two sets of simulations span a large range of geostrophic forcing conditions, allowing the study of the effect on the structure of the convective boundary layer (CBL) above a patchy surface compared to a homogeneous surface. The procedure used to spin up the simulations is the following: a spinup phase of four hours of real time is used to achieve converged turbulent statistics, which is then followed by an evaluation phase. During the latter, running averages are computed for the next hour of real time (dataset here published). Statistics have been computed for averaging times of 5 min to 1 h, showing statistical convergence at 30-min averages with negligible changes between the 30-min and the 60-min averages. The simulations cover a wide range of atmospheric stability regimes ranging from −z<sub>i</sub>/L < 5 to −z<sub>i</sub>/L > 700, and hence spanning from near neutral to highly convective scenarios.</p> <p><strong>Description of the Dataset as included in the NetCDF files:</strong></p> <p>Data for each study case is included in two files, one for momentum related variables, and one for temperature related variables. For example, the following files "P200_Ug1_Momentum.nc" and "P200_Ug1_Scalar.nc", include the 1h averaged variables for momentum and temperature for the case of 200 m surface patches with 1 m/s geostrophic winds. </p> <p>Each corresponding momentum file "PXXX_UgX_Momentum.nc" includes the following variables in a Python Xarray structure:</p> <ul> <li>'avgU' = mean streamwise wind speed; 'avgV' = mean spanwise wind speed; 'avgW' = mean vertical wind speed, 'avgP' = mean dynamic modified pressure field (<span class="math-tex">\(p^*\)</span>, see Margairaz et al 2020a),</li> <li>'avgU2', 'avgV2', 'avgW2' = correspond to <span class="math-tex">\(\overline{UU}\)</span>, <span class="math-tex">\(\overline{VV}\)</span>, and <span class="math-tex">\(\overline{WW}\)</span>, where the capital indicates the LES filtered variable.</li> <li>'avgUV', 'avgUW', 'avgVW' = correspond to <span class="math-tex">\(\overline{UV}\)</span>, <span class="math-tex">\(\overline{UW}\)</span>, and <span class="math-tex">\(\overline{VW}\)</span>. These variables together with the ones above are used to compute the Reynolds stress components (e.g. <span class="math-tex">\(R_{xz} = \overline{U}\overline{W} - \overline{UW}\)</span>).</li> <li>avgU3', 'avgV3', 'avgW3', 'avgU4', 'avgV4', 'avgW4' = correspond to the equivalent but instead of squared they are cubed and to the 4th power.</li> <li>'avgtxx','avgtyy','avgtzz','avgtxy','avgtxz','avgtyz' = These represent the corresponding averaged subgrid scale (SGS) stress.</li> <li>'avgdudz','avgdvdz','avgNut','avgCs' = Represent the averaged vertical derivatives, an averaged subgrid Nusselt number, and the Cs coefficient computed in the SGS model.</li> </ul> <p>Overall, there are a total of 26 variables related to the momentum field. Alternatively, the temperature fields are included in the "PXXX_UgX_Scalar.nc" files. These files include 10 variables, </p> <ul> <li>'avgT' = mean Temperature field, 'avgT2' = corresponds to <span class="math-tex">\(\overline{TT}\)</span>, 'avgUT' = correspond to <span class="math-tex">\(\overline{UT}\)</span>, 'avgVT' = correspond to <span class="math-tex">\(\overline{VT}\)</span>, 'avgWT' = correspond to <span class="math-tex">\(\overline{WT}\)</span>; one can use these terms to compute the corresponding Reynolds averaged turbulent fluxes as is the case for momentum. </li> <li>'avgUT_sgs','avgVT_sgs','avgWT_sgs' = These represent the corresponding subgrid scale fluxes.</li> <li>'avg_nus', avg_ds' = averaged subgrid Nusselt number, and the Ds coefficient computed in the scalar SGS model.</li> </ul> <p>All variables output from the LES are normalized by Tscale = 290 [K] when it includes dimensions of temperature, u_scale = 0.45 [m/s], when it relates to velocity fields, and z<sub>i</sub> = 1000 [m] for length scales.</p> <p>The only output variables that are expressed in dimensional form are those for the surface temperature included in the files "SurfTemp_DXXX.nc"</p> <p>Together with the data files we include a Python script that loads the data and includes it in two Xarray structures that one can then use to work with the datasets. </p>
Local chromatin context dictates the genetic determinants of the heterochromatin spreading reaction. Analysis Code, Numerical and Primary data.
<p>Uploaded under this Zenodo DOI is the following:</p> <p>1. the Analysis Code used for Flow Cytometry analysis in the paper, GO complex analysis (Figure 3) and Hit visualization (Figure 1, 2 S1, S4 Figs).</p> <p>2. The primary Flow Cytometry data from both the initial screen (ScreenFlowFCS) and validation experiments (ValidationFlowFCS) are included as .zip files.</p> <p>3. a .zip folder is uploaded that contains all the analysis code for the ChIP-Seq experiments. </p> <p>4. Excel worksheets that contain the numerical source data for all qPCR bar plots.</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.