Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
67
datasets available to search
ShareScore release 0.9.0
Dataset results
67 results for “phase field”
Data bundle for "Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine learning' </p> <p>The raw data is given as 'yprime.h5' - this contains patterns and metadata in the Bruker-exported format.</p> <p>Scripts for dataset decomposition into latent factors are given in 'Scripts'.</p> <p>Our spherical analysis code is included in 'SphericalAngleDF'.</p> <p>Outputs of our analysis code are contained in 'Analysis'.</p> <p>Figures for the paper are included in 'Figures'.</p> <p> </p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </p>
Phase Field Simulation of Morphotropic phase boundary of relaxor ferroelectrics
<p>Phase-field simulation of a mixed system of Tetragonal and Rhombohedral phases representing the morphotropic phase boundary of relaxor ferroelectrics</p>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties".</p> <p>It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Gravitational waves from a cosmological vacuum phase transition - scalar field value
<p>Movie based on Figure 2 of the paper <a href="https://doi.org/10.1103/PhysRevD.97.123513">Gravitational waves from vacuum first-order phase transitions: from the envelope to the lattice</a> [<a href="https://arxiv.org/abs/1802.05712">arXiv:1802.05712</a>]. This movie originally appeared as Supplemental Material associated with the paper.</p> <p><em>Caption based on original figure caption</em>: Slices through a simultaneous nucleation simulation with parameters <span class="math-tex">\(R_\mathrm{c} M = 7.15\)</span>, <span class="math-tex">\(N_\mathrm{b} = 64\)</span> and <span class="math-tex">\(R_* M = 56.32\)</span> showing the expansion, collision, and oscillatory phase of the scalar field. The scalar field value is shown in blue, and the gravitational wave energy density is shown in red. Note that the range of the colourbar for the gravitational wave energy density changes with time. During the oscillatory phase the gravitational wave energy density becomes very uniform and the “hotspots” are deviations on the sub percent level.</p> <p>The movie is also available on Vimeo, <a href="https://vimeo.com/255031420">here</a>.</p>
Perturbative effective field theory expansions for cosmological phase transitions, dataset
<p>This dataset is the work of Oliver Gould and Tuomas V.I. Tenkanen. It collects the numerical data from the paper <a href="https://arxiv.org/abs/2309.01672">"Perturbative effective field theory expansions for cosmological phase transitions"</a> (2023). It primarily contains data from perturbative calculations of the thermal evolution of the real-triplet extended Standard Model at two benchmark parameter points.</p><p>In addition, for comparison to the perturbative results, we have included data of the scalar quadratic condensates as a function of temperature from the lattice Monte-Carlo simulations of Lauri Niemi, Michael J. Ramsey-Musolf, Tuomas V.I. Tenkanen and David J. Weir, from the paper <a href="https://doi.org/10.1103/PhysRevLett.126.171802">"Thermodynamics of a Two-Step Electroweak Phase Transition"</a> (2020). We thank the authors for granting permission to reproduce this data here.</p><p>Everything is contained within the archive file <a href="https://zenodo.org/api/records/10353066/draft/files/triplet_two_step_data.tar.gz/content">triplet_two_step_data.tar.gz</a>, a tarball compressed with Gzip. For further details and for the context of this dataset, see the above papers. Details of the conventions used in the dataset can be found in the accompanying README.md file.</p>
Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"
<p>This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"</p> <p>Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions.</p> <p>The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary. </p>
DataSet: Structural and optical properties of gold nanosponges revealed via 3D nano-reconstruction and phase-field models
<p>These are the main raw and processed data for the publication "Structural and optical properties of gold nanosponges revealed<br> via 3D nano-reconstruction and phasefield models".</p> <p>Abstract:<br> Nanoporous gold nanoparticles are subject of intensive research due to their unique morphology, which leads to electric field localizations generating a strongly nonlinear optical response, allowing a wide range of applications. However, accurate predictions of physical properties require detailed knowledge of the sponges’ chaotic nanometer-sized geometrical structures, posing a metrological challenge. Therefore, a main goal is to obtain computer models with equivalent structural and optical properties. To understand the sponges’ morphology, a procedure for their accurate three-dimensional reconstruction using focused ion beam tomography is presented. Next, a small number of morphological key parameters is derived that sufficiently characterize the complex topology. Additionally, a new simulation method for the computer-aided creation of finite-sized sponges with adjustable geometric properties is presented. It is shown that if certain morphological parameters are similar for computer-generated and experimental sponges, their optical response, including number and locations of field localizations, are also similar. Finally, the anisotropy of the experimental sponges is analyzed and an easy-to-use procedure to replicate arbitrary anisotropies in computer-generated sponges is presented.</p>
DeepBacs – Escherichia coli release from stationary phase - Bright field segmentation dataset and StarDist model
<p>Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation.</p> <p>Additional information can be found on this<a href="https://github.com/HenriquesLab/DeepBacs/wiki"> github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli </em>cells of an overnight culture and the manually annotated segmentation mask.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field and segmented mask images </p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 2 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset)</p> <p>512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series)</p> <p><strong>Data annotation</strong>: Images were annotated using the Fiji freehand selection tool.</p> <p><strong>Image preprocessing</strong>: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped</p> <p><strong>StarDist model</strong></p> <p>The StarDist 2D model was trained from scratch for 200 epochs on 33 paired image patches (image dimensions: (512, 512 px²), patch size: (512 x 512 px²)) with a batch size of 2, 80 rays, grid size 1, 4-fold data augmentation and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.13) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU.</p> <p>Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578 </p>
Exploring Bifurcations in Bose-Einstein Condensates via Phase Field Crystal Models
<p>Supplementary data for the following paper: Alina Barbara Steinberg, Fabian Maucher, Svetlana Gurevich, Uwe Thiele, "Exploring Bifurcations in Bose-Einstein Condensates via Phase Field Crystal Models"</p>
Magnetic structure and field-dependent magnetic phase diagram of Ni2In-type PrCuSi
<p>Data sets for original figures in the article 'Magnetic structure and field-dependent magnetic phase diagram of Ni<sub>2</sub>In-type PrCuSi' published in <a href="https://iopscience.iop.org/article/10.1088/1361-648X/aae28d/meta">J. Phys.:Condens. Matter 30 (43) 2018</a>. The file name of each xls file corresponds to the figure number in the published article. The files can be opened using the Excel program. If there are sub-figures, or multiple frames in each figure, the data of each sub-figure is stored in separate sheets within one xls file. The files with the file extension 'vesta' can be opened using the freely available program <a href="https://jp-minerals.org/vesta/en/">VESTA</a>.</p>
Preferential growth of intermetallics under temperature gradient at Cu–Sn interface during transient liquid phase bonding: insights from phase field simulation
<p>The data of (i) heats of transport values and (ii) coefficients for expressions of free energy density of phases used to generate the results in the paper titled "Preferential growth of intermetallics under temperature gradient at Cu–Sn interface during transient liquid phase bonding: insights from phase field simulation" are provided in this dataset.</p> <p><br> <strong>(i )</strong> The heats of transport (Q*) values of Cu and Sn species in LIQUID (Sn-rich), IMC (CU6SN5) and FCC (Cu-rich) phases at T=523.15 K (<strong>constant cold side temperature</strong>) are available in <em>heat_of_transport.csv</em> file. The numerical quantities in the Q* column of the file are expressed in the unit of kJ/mol. In this work, these Q* values have been independently validated to work for applied thermal gradients (<span class="math-tex">\(\nabla T\)</span><sub>a</sub>) of <strong>1.5E+5 K/m </strong>and <strong>1.5E+6 K/m </strong>Thus, the following meanings hold true for the column names of this data file:</p> <p><strong>phase</strong> - it is the name of a phase studies (e.g. Cu-rich FCC phase, Sn-rich LIQUID phase and Cu<sub>6</sub>Sn<sub>5</sub> IMC phase. the data type is string, and has no unit. </p> <p><strong>species</strong> - the element Cu and Sn of the binary Cu-Sn system. the data type is a string, and has no unit. </p> <p><strong>T (K)</strong> - it is the <strong>constant temperature (T = T<sub>cold</sub> = 523.15 K) at the bottom cold edge </strong> of a rectangular computational domain of width = 498 nm and height = 747 nm. the data is a float value, and has a unit of K. <strong> The information about cold edge temperature T<sub>cold</sub> being the constant reference temperature, and the hot edge temperature being T<sub>hot</sub>= T<sub>cold </sub>+ <span class="math-tex">\(\nabla T\)</span><sub>a</sub> , is a novelty of this work. </strong>While most of the other works are based upon hot edge being maintained at constant temperature by a thermal heater, this work presents the data with the temperature of cold edge maintained constant by a thermal cooler. </p> <p><strong>Q* (kJ/mol)</strong> - The data of heat of transport values expressed in terms of unit of kJ/mol can be either negative or positive. In this work, the values presented in the table have been validated for applied vertical thermal gradients of (<span class="math-tex">\(\nabla T\)</span><sub>a</sub>) of <strong>1.5E+5 </strong>and <strong>1.5E+6 K/m .</strong></p> <p> </p> <p><strong>(ii)</strong> The chemical free energy density of a phase i (i = LIQUID, IMC or FCC ) at 523.15 K has been expressed with the function f<sub>i </sub>= 0.5 * A<sub>i</sub> * (c<sub>i </sub>- c<sub>eq,i</sub>)<sup>2</sup> + B<sub>i</sub> * (c<sub>i </sub>- c<sub>eq,i</sub>) + C<sub>i</sub>; where c<sub>i</sub> is the mole-fraction (composition) of Sn in a phase. The data consisting of the numerical values of coefficients A<sub>i</sub>, B<sub>i</sub> and C<sub>i</sub> on the units of J/m<sup>3</sup> are provided in the file free_energy_density.csv. Besides these coefficients, the file also consists the quantified values of the equilibrium composition c<sub>eq,i </sub>at each phase. It is to be noted that c<sub>eq,i</sub> has no units.</p> <p> </p> <p> </p> <p> </p>
Large scale simulation of pressure induced phase-field fracture propagation using Utopia
<p>Utopia is an open-source C++ library for parallel non-linear multilevel solution strategies. Utopia provides the advantages of high-level programming interfaces while at same time a framework to access low level data-structures without breaking code encapsulation. Complex numerical procedures can be expressed with few lines of code, and evaluated by different implementations, libraries, or computing hardware. In this paper we investigate the parallel performance of our implementation of the recursive multilevel trust-region (RMTR) method based on the Utopia library. RMTR is a globally convergent multilevel solution strategy designed to solve non-convex constrained minimization problems. In particular, we solve pressure induced phase-field fracture propagation in large and complex fracture networks. Solving such problems is deemed challenging even for a few fractures, however, here we are considering realistic and idealized networks with up to 1000 fractures.</p>
Evolution of field-induced metastable phases in the Shastry-Sutherland lattice magnet TmB4
<p>Open data for "Evolution of field-induced metastable phases in the Shastry-Sutherland lattice magnet TmB4 ", Phys. Rev. B, 120, 060407, 2020 (R)</p>
Crystalline Morphology Formation in Phase-Field Simulations of Binary Mixtures
<p>Simulation data used for the publication "Crystalline Morphology Formation in Phase-Field Simulations of Binary Mixtures" in Journal of Materials Chemistry C, Royal Society of Chemistry (2023). See README file for more details.</p>
Effect of impeller rotational phase on the FDA blood pump velocity fields
Open the record for dataset details and reuse information.
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Dataset: A phase field model combined with genetic algorithm for polycrystalline hafnium zirconium oxide ferroelectrics
<p>The folder includes generated data MATLAB scripts to read/plot the polarization-electric field (PE) hysteresis curves. The dataset contains phase field generated polycrystalline grain structure, simulated domain structures during polarization reversal, and symmetric PE curves (measured and simulated).</p> <p><strong>Polycrystalline grain structures</strong>: The output files are in the *.txt format, readable by MTEX to generate orientation maps.<br> Column(1) Column(2) Column(3) Column(4) Column(5)<br> X Y φ(rad) θ(rad) 𝜓(rad)<br> ... ... ... ... ...<br> ... ... ... ... ...<br> ... ... ... ... ...</p> <p><strong>Domain structures</strong>: The output files are in the *.csv format, which can be visualized by programs like ParaView.<br> Column(1) Column(2) Column(3) Column(4)<br> X Y Z P<br> ... ... ... ...<br> ... ... ... ...<br> ... ... ... ...</p> <p><br> <strong>PE curves</strong>: The output files are in the *.txt format, readable by MATLAB.<br> Column(1) Column(2)<br> E(MV/cm) P(μC/cm²)<br> ... ...<br> ... ...<br> ... ...</p> <p><strong>List of datasets:-</strong><br> Fig. 1: pecurves/calib_func1.txt (Calibrated p(e)), pecurves/measpe_hf50.txt (Measured PE curve), pecurves/simpe_calib.txt (Simulated PE curve).<br> Fig. 2(a): polcr_struc/xy_col.txt (XY top view), polcr_struc/yz_col.txt (YZ side view), polcr_struc/xz_col.txt (ZX side view)<br> Fig. 2(b): pecurves/simpe_gaopt.txt (Simulated PE curve), pecurves/measpe_hf50.txt (Measured PE curve).<br> Fig. 3: pecurves/calib_func.txt (Calibrated p(e)), pecurves/gaopt_func.txt (GA optimized p(e)).<br> Fig. 5: pecurves/simpe_gaopt.txt (Case 1), pecurves/simpe_elast.txt (Case 2).<br> Fig. 4(e): dom_struc/dom_profile1.csv, (f) dom_struc/dom_profile2.csv, (g) dom_struc/dom_profile3.csv, (h) dom_struc/dom_profile4.csv, (m) dom_struc/dom_profile5.csv, (n) dom_struc/dom_profile6.csv, (o) dom_struc/dom_profile7.csv, (p) dom_struc/dom_profile8.csv<br> Fig. 6: pecurves/simpe_gaopt.txt (GA fit coefficients), pecurves/simpe_ldc1.txt (Set 1), pecurves/simpe_ldc2.txt (Set 2).<br> Fig. 7(a): pecurves/simpe_gaopt.txt (𝝂₀ = 1.0), pecurves/simpe_fr80.txt (𝝂₀ = 0.8), pecurves/simpe_fr50.txt (𝝂₀ = 0.5).<br> Fig. 7(b): pecurves/measpe_hf50.txt (Hf₀.₅Zr₀.₅O₂), pecurves/measpe_hf75.txt (Hf₀.₇₅Zr₀.₂₅O₂).<br> Fig. 8: pecurves/simpe_fr38.txt (Simulated PE curve), pecurves/measpe_hf75.txt (Measured PE curve).<br> Fig. 9: pecurves/simpe_gaopt.txt (Random non-textured), pecurves/simpe_tex001.txt ([001] fiber textured), pecurves/simpe_tex111.txt ([111] fiber textured).<br> Fig. 10(a): polcr_struc/xy_equ.txt (XY top view), polcr_struc/yz_equ.txt (YZ side view), polcr_struc/xz_equ.txt (ZX side view)<br> Fig. 10(b): pecurves/simpe_colmor.txt (Columnar grain microstructure), pecurves/simpe_equmor.txt (Equiaxed grain microstructure).</p> <p><strong>List of MATLAB scripts:</strong><br> Fig 1: matlab_scripts/fig1.m<br> Fig 2(b): matlab_scripts/fig2b.m<br> Fig 3: matlab_scripts/fig3.m<br> Fig 5: matlab_scripts/fig5.m<br> Fig 6: matlab_scripts/fig6.m<br> Fig 7(a): matlab_scripts/fig7a.m<br> Fig 7(b): matlab_scripts/fig7b.m<br> Fig 8: matlab_scripts/fig8.m<br> Fig 9: matlab_scripts/fig9.m<br> Fig 10(b): matlab_scripts/fig10b.m<br> </p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </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.