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3,206 results for “property (T)”
Universal spatial properties of coral reefs
<p>Georeferenced database on the spatial properties of all individual shallow-water tropical coral reefs worldwide. The dataset was obtained by processing and analyzing the global-scale coral reef benthic data provided by the Allen Coral Atlas (ACA), a publicly available dataset of high-resolution satellite imagery and machine learning-based coral reef classifications. </p> <p>The original data, already divided into different coral provinces, was segmented to identify the individual reefs of each province using a label assignment algorithm. This allows to analyze several spatial properties of coral reefs such as the size distribution, area-perimeter relationship, fractal dimensions and shape measures.</p> <p>The dataset contains the following measures for each individual reef in each coral province:</p> <ul> <li>Area (m²)</li> <li>Perimeter (m)</li> <li>Surface fractal dimension</li> <li>Perimeter fractal dimension</li> <li>Compactness</li> <li>Diameter ratio</li> <li>Distance to nearest reef</li> <li>Longitude</li> <li>Latitude</li> <li>Geometry</li> </ul>
Data for "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks".</p><p>The file "MOF_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.</p>
data from "Fractal properties of isolines at varying altitude revealing different dominant geological processes on Earth"
<p>The file contains the data used to produce Fig.4 for the paper "Fractal properties of isolines at varying altitude revealing</p><p>different dominant geological processes on Earth", by Andrea Baldassarri, Marco Montuori, Olga Prieto-Ballesteros,</p><p>and Susanna C. Manrubia, Journal of Geophysical Research: PlanetsVolume 113, Issue E9, https://doi.org/10.1029/2007JE003066</p>
Magnetic properties and chemical analysis of remagnetised carbonates of South America
<p>We investigate the magnetic properties and chemical composition of remagnetised carbonate rocks in South America. These rocks play a crucial role in understanding past climates and the behaviour of Earth's magnetic field. Unfortunately, the primary remanence of these rocks is often altered by secondary components in a process known as remagnetisation. In South America, we observe evidence of a continent-wide remagnetisation event in Neoproterozoic carbonates from sedimentary basins separated by significant distances. To shed further light on this phenomenon, we employ both traditional macroscopic rock magnetic analysis and advanced synchrotron-based nano/micro imaging and chemical analysis techniques.</p>
Stellar properties of observed stars stripped in binaries in the Magellanic Clouds - Observations and Results
<p>This Zenodo repository is one of three Zenodo repositories related to the article "Stellar properties of observed stars stripped in binaries in the Magellanic Clouds" by Y. Götberg, M.R. Drout, A.P. Ji, J.H. Groh, B.A. Ludwig, P.A. Crowther, N. Smith, A. de Koter, and S.E. de Mink. In the article, we analyze the optical spectra of ten stars and measure their stellar properties using spectral fitting. This repository contains observational data and the resulting measurements for the stellar properties of the stars analyzed in the article, along with best fit spectral models and spectral models used to estimate the wind mass loss rate of the stars. Below, we describe the content in more detail:</p> <ul> <li><strong>0_ReadMe.txt</strong>: A text file where we describe some more details regarding the content.</li> <li><strong>S2_stacked_stellar_spectra.tar.gz (4.6 MB):</strong> The spectra we use for obtaining stellar properties for the observed stars.</li> <li><strong>Table2.txt</strong>: the apparent AB magnitudes with associated errors for the stars we analyze in the paper.</li> <li><strong>Table3.txt</strong>: the stellar properties for the stars we analyze in the paper. These parameters are obtained by spectral fitting.</li> <li><strong>S5_spectra_best_fit_models.tar.gz (14 MB):</strong> The spectral energy distributions and normalized spectra for the best-fit models, recomputed such that the radius and bolometric luminosity also matches. This tarball contains best-fit spectra for all of the stars in text-files labeled with the format SED_StarX_xxx.txt (~70 MB when inflated). The stellar parameters are presented in Section 5.</li> <li><strong>S5_full_best_fit_models.tar.gz (1.9 GB):</strong> The full CMFGEN version of the best-fit models for each individual star. This tarball contains a tarball for each star, which when inflated becomes ~250-550 MB each. </li> <li><strong>S7_spectra_mdot_models (31 MB): </strong>The spectral energy distributions and normalized spectra for the models used in the mass-loss analysis that we present in Section 7.</li> <li><strong>S7_full_mdot_models (3.5 GB):</strong> The full CMFGEN version of the spectral models used for the mass-loss rate variation presented in section 7 (excluding the best-fit models, which are provided separately).</li> </ul>
Frictional properties of natural granite fault gouge under hydrothermal conditions: A case study of strike-slip fault from Anninghe Fault zone, southeastern Tibetan Plateau
<p>We performed friction experiments on natural granite gouge under hydrothermal conditions to investigate roles of the Anninghe Fault (ANHF) on seismogenesis in the continental crust. In this dataset, we report processed data after correction. Detailed information about the files in the zip-files is given in the explanatory file Lei-et-al-2023-Data-Description.pdf.</p>
Measurement and simulation of optical properties of nanostructured silicon heavily implanted with selenium
<p><strong>Summary:</strong></p> <p>This is the collection of datasets used to plot the line art figures for the journal paper “Extended Infrared Absorption in Nanostructured Si Through Se Implantation and Flash Lamp Annealing”.</p> <p><strong>Methods:</strong></p> <p>The experimental and calculation methods for generating the datasets are described in the original paper and in the supplementary materials.</p> <p><strong>File Description:</strong></p> <ul> <li>The filenames for all files match the figure captions from the original paper and supplementary materials.</li> <li>Each file represents a specific plot, with all files provided in CSV format.</li> <li>Each column in the file represents a set of variable data.</li> <li>The datasets corresponding to each curve can be identified by comparing the first-row header information with the figure legend.</li> </ul> <p><strong>Credit:</strong></p> <p>When using the dataset/figures, please cite the original paper as: Radfar, B., Liu, X., Berencén, Y., Shaikh, M.S., Prucnal, S., Kentsch, U., Vähänissi, V., Zhou, S. and Savin, H. (2024), Extended Infrared Absorption in Nanostructured Si Through Se Implantation and Flash Lamp Annealing. Phys. Status Solidi A 2400133. <a title="https://doi.org/10.1002/pssa.202400133" href="https://doi.org/10.1002/pssa.202400133" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/pssa.202400133</a></p>
Quantifying the light-absorption properties and molecular composition of brown carbon aerosol from sub-Saharan African biomass combustion
<div> <p>Data presented in figures of the peer-reviewed journal article "Quantifying the light-absorption properties and molecular composition of brown carbon aerosol from sub-Saharan African biomass combustion" by Moschos et al.</p> <p>Disclaimer: Please contact the dataset creator prior to inclusion of data in any upcoming publication.</p> </div>
Data for "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt grain boundaries in aluminum"
<p>This repository contains the raw data of experimental STEM images and of the simulations for the paper "Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum".</p>
Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques
<p><span>The dataset is divided into three sections within the worksheet.</span></p> <p><span> </span><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></p> <p><span> </span><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></p> <p><span><span>The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).</span></span></p>
Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties
<p>Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis</p> <p>1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:</p> <ul> <li>Classification label vectors for the three analyzed metabolic concentration cases: <ul> <li>Reference case: class_vector_train_ref.mat</li> <li>Extreme1 case: class_vector_train_ex1.mat</li> <li>Extreme2 case: class_vector_train_ex2.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: training_set_ref.mat</li> <li>Extreme1 case: training_set_ex1.mat</li> <li>Extreme2 case: training_set_ex2.mat</li> </ul> </li> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ref.mat</li> <li>Extreme1 case: ccXTR_ex1.mat</li> <li>Extreme2 case: ccXTR_ex2.mat</li> </ul> </li> <li>Thermodynamics-based Flux Analysis (TFA) models for the three cases: <ul> <li>Reference case: tfa_ref.mat</li> <li>Extreme1 case: tfa_ex1.mat</li> <li>Extreme2 case: tfa_ex2.mat</li> </ul> </li> <li>Parameter names identical for the three cases <ul> <li>parameterNames.mat</li> </ul> </li> </ul> <p>2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).</p> <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>ccXTR_ValidNeg.mat</li> </ul> </li> <li>Parameter sets used in validation <ul> <li>validation_set_neg.mat</li> </ul> </li> </ul> <p>3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_neg_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_neg_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_neg_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_neg_agg.mat</li> <li>Extreme1 case: validation_set_ref_neg_agg.mat</li> <li>Extreme2 case: tvalidation_set_ref_neg_agg.mat</li> </ul> </li> </ul> </li> </ul> <ul> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_pos_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_pos_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_pos_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_pos_agg.mat</li> <li>Extreme1 case: validation_set_ex1_pos_agg.mat</li> <li>Extreme2 case: validation_set_ex2_pos_agg.mat</li> </ul> </li> </ul> </li> </ul> <p>4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_neg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_neg_reassignment.mat</li> </ul> </li> </ul> </li> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_pos.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_pos_reassignment.mat</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Optical properties of germania and titania at 1064nm and at 1550nm
<p>This dat accompanies the publication with the same title in the Classical and Quantum Gravity Focus Issue on low-noise thin-film coatings.</p> <ul> <li>The files named Comp_... contain the RBS results shown in Fig.1/Table 1.</li> <li>The transmission spectra files show the spectra as measured for all samples and heat treatment temperatures. The summary files show an overview of the fit results for refractive index and thickness at 1550nm resulting from fits using different optical models and the software SCOUT.</li> <li>There are two tables of absorption results: one shows a summary of the individual absorption results in ppm measured on various points on each sample; the second file shows a summary of the average absorption per sample and heat treatment step, the refractive index and thickness used, and the resulting extinction coefficient k. The extincion coefficient was calculated using the software tfcalc.</li> <li>The Raman files include the raw data for Raman measurements presented in the article. </li> </ul>
Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis
<p>This material constitutes the dataset used for the computation of the results of the paper Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis by Robin & al. </p> <p>The folders "Bennu", "Dimorphos", "Itokawa" and "Ryugu" contains the raw images from PDS used for this study as well as the images with the outlined boulders made with segmentanygrains (https://github.com/zsylvester/segmenteverygrain), and the associated morphological descriptor values. The folder "ExtremeCases" contains the raw and outlined images for extreme roundness cases.</p> <p>This material also contains the main figures of the paper as well as the code to remake most the figures.</p> <p>Last version of this manuscript has been submitted to Nature Communications on May 14th, 2024 and accepted on May 17th, 2024.</p> <p><strong>Abstract</strong>: </p> <table> <tbody> <tr> <td>Planetary defense efforts rely on estimates of the mechanical properties of asteroids, which are difficult to constrain accurately from Earth. The mechanical properties of asteroid material are also important in the interpretation of the Double Asteroid Redirection Test (DART) impact. Here we perform a detailed morphological analysis of the surface boulders on Dimorphos using images, the primary data set available from the DART mission. We estimate the bulk angle of internal friction of the boulders to be 32.7 ± 2.5° from our measurements of the roundness of the 34 best-resolved boulders ranging in size from 1.67 to 6.64 m. The elongated nature of the boulders around the DART impact site implies that they were likely formed through impact processing. Finally, we find striking similarities in the morphology of the boulders on Dimorphos with those on other rubble pile asteroids (Itokawa, Ryugu and Bennu). This leads to very similar internal friction angles across the four bodies and suggests that a common formation mechanism has shaped the boulders. Our results provide key inputs for understanding the DART impact and for improving our knowledge about the physical properties, the formation and the evolution of both near-Earth rubble-pile and binary asteroids.</td> </tr> <tr></tr> </tbody> </table>
Supplementary Information for Chemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires
<p>This repository contains supplementary information (SI-1 and SI-2) related to the article entitled "Chemical Properties of the Southeast Asian Haze from Indonesian Peatland Fires" published in Global Environmental Research (GER, Volume 27, No.1, Pages 37–48, Year 2023, <a href="https://doi.org/10.57466/ger.27.1_37" target="_blank" rel="noopener">https://doi.org/10.57466/ger.27.1_37</a>). SI-1 contains the newly created dataset used in GER and SI-2 contains supplementary documents for Sections 4 and 6 as well as tables and figures referred to but not included in the main article of GER.</p>
Plant silicon content as a proxy for understanding plant community properties and ecosystem structure
<p>Main dataset from the paper entitled "Plant silicon content as a proxy for understanding plant community properties and ecosystem structure".</p>
Data_paper_Influence of buffer/protective layers on the structural and magnetic properties of SmCo films on Silicon
<p>Integration of Samarium Cobalt hard magnets on silicon requires buffer/protective layers that can enhance the magnetic properties of the magnet while preserving its structure and chemical composition after post-annealing treatments needed for the formation of the magnetically hard phase. In this work, a comparison of Samarium-Cobalt films for five different buffer/protective layers, namely Ti, W, TiW, Ta, Cr and two different annealing temperatures, 650°C and 750°C, is presented. Depending on materials and annealing temperatures, magnetic properties such as saturation and coercivity of the SmCo film can be finely tuned. We show that coercivity up to 3.65 T or saturation magnetization up to 0.95 T can be reached by proper choice of the relevant process parameters: deposition temperature, material for the buffer/protective layer and annealing temperature. Such value of coercivity is among the highest found in literature for thin films of SmCo.</p>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Determination of antibacterial and photothermal properties of novel composites based on graphene oxide/reduced graphene oxide, gold nanoparticles, and graphene quantum dots
<p>HR-TEM.zip - HR-TEM files, file type .jpg</p> <p>FTIR.zip - FTIR spectra, file type .spa</p> <p>Photoluminescence.zip - PL spectra, file type .opju</p> <p>UV-Vis.opju - Origin file with UV-Vis spectra combined</p> <p>Raman 532 nm.opju - Origin file with Raman spectra combined</p> <p>ABDA.opju - Origin file with singlet oxygen production measurements</p> <p>Contact angle.png - Image with contact angle values</p> <p>Antibacterial analysis.png - Image representing antibacterial growth inhibition analysis</p> <p>XRD.zip - XRD spectra, file type .dat</p>
Raw data to "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions"
<div> <p>This directory contains the data used to generate the numerical results in the work "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions [1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>[1]: P. Adelhardt, A. Duft and K. P. Schmidt, Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions, <a href="https://arxiv.org/abs/2408.13145">arXiv:2408.13145</a></p> <p> </p> </div>
Phase & Property Data for the NbCrVWZr Alloy System
<h4><strong>General Information</strong></h4> <p>This dataset contains a comprehensive collection of properties, compositions, and phase predictions for alloys from a simplicial grid sampling of the NbCrVWZr quinary alloy system with a spacing of 0.05 mole fraction. The dataset is structured to facilitate analysis of predicted material properties and phase stability for corresponding alloy compositions. Each row corresponds to a unique alloy composition.</p> <h4><strong>Column Descriptions</strong></h4> <ol> <li> <p><strong>umap0, umap1</strong>:<br>2D coordinates obtained from Uniform Manifold Approximation and Projection (UMAP), used for dimensionality reduction and visualizing the high-dimensional alloy composition space.</p> </li> <li> <p><strong>Nb, Cr, V, W, Zr</strong>:<br>Elemental mole fractions for Niobium (Nb), Chromium (Cr), Vanadium (V), Tungsten (W), and Zirconium (Zr) in the alloy</p> </li> <li> <p><strong>PROP LT (K)</strong>:<br>Liquidus temperature (LT) in Kelvin computed by ThermoCalc</p> </li> <li> <p><strong>PROP ST (K)</strong>:<br>Solidus temperature (ST) in Kelvin computed by ThermoCalc</p> </li> <li> <p><strong>PROP 500C CTE (1/K)</strong>, <strong>PROP 1000C CTE (1/K)</strong>, <strong>PROP 1500C CTE (1/K)</strong>:<br>Coefficients of thermal expansion (CTE) at 500°C, 1000°C, and 1500°C, reported in reciprocal Kelvin</p> </li> <li> <p><strong>EQ 1273K THCD (W/mK)</strong>, <strong>EQ 1523K THCD (W/mK)</strong>, etc.:<br>Thermal conductivity (THCD) in W/(m·K) computed during equilibrium calculations by ThermoCalc at various temperatures (e.g., 1273K, 1523K).</p> </li> <li> <p><strong>EQ 1273K Density (g/cc)</strong>, <strong>EQ 1523K Density (g/cc)</strong>, etc.:<br>Density in g/cm³ computed during equilibrium calculations by ThermoCalc at the respective temperatures.</p> </li> <li> <p><strong>EQ 1273K MAX BCC</strong>, <strong>EQ 1273K SUM BCC</strong>, etc.:</p> <ul> <li><strong>MAX BCC</strong>: Mole fraction of the most abundant body-centered cubic (BCC) phase predicted for the alloy at the specified temperature, predicted with ThermoCalc</li> <li><strong>SUM BCC</strong>: Total mole fraction of all BCC phases predicted at the specified temperature, predicted with ThermoCalc</li> </ul> </li> <li> <p><strong>EQ 2/3*ST THCD (W/mK)</strong>, <strong>EQ 2/3*ST Density (g/cc)</strong>:<br>Thermal conductivity and density computed at two-thirds of the ThermoCalc-predicted solidus temperature.</p> </li> <li> <p><strong>Pugh_Ratio_PRIOR</strong>:<br>Rule of mixture estimate of Pugh's ratio (ratio of bulk modulus to shear modulus)</p> </li> <li> <p><strong>YS 1000C PRIOR</strong>, <strong>YS 1500C PRIOR</strong>:<br>Yield strength (YS) at 1000°C and 1500°C, predicted using the strength model by Maresca and Curtin</p> </li> <li><strong>Single BCC</strong>:<br>Indicates whether a single BCC phase is predicted (1 for yes, 0 for no) from below Scheil ST down to 1000°C</li> <li> <p><strong>SCHEIL ST, SCHEIL LT:</strong></p> <ul> <li><strong>SCHEIL ST</strong>: Solidus temperature derived from Scheil-Gulliver solidification simulations</li> <li><strong>SCHEIL LT</strong>: Liquidus temperature derived from Scheil-Gulliver solidification simulations</li> </ul> </li> <li> <p><strong>Kou Criteria</strong>, <strong>Kou Criteria Normalized</strong>:<br>Values related to the Kou criteria, which predict susceptibility to solidification cracking. Predicted with ThermoCalc Scheil simulations.</p> </li> <li> <p><strong>SCHEIL BCC_B2</strong>, <strong>SCHEIL BCC_B2#2</strong>, <strong>SCHEIL MAX BCC</strong>:</p> <ul> <li><strong>MAX BCC</strong>: Fraction of the most abundant BCC phase predicted from Scheil simulations</li> <li><strong>BCC_B2</strong>: Prediction of the ordered BCC (B2) phase fraction from Scheil simulations</li> <li><strong>SCHEIL BCC_B2#2</strong>: <ul> <li>This refers to a <strong>secondary BCC </strong>structure predicted by ThermoCalc during the solidification process using Scheil simulation</li> <li>The #2 suffix indicates that this is not the primary BCC phase but another distinct composition set, potentially representing A miscibility gap within the BCC phase</li> </ul> </li> </ul> </li> <li> <p><strong>composition_string</strong>:<br>A string representation of the alloy composition</p> </li> <li> <p><strong>Creep Merit</strong>:</p> <ul> <li>Creep merit index, computed at 2000 degrees Celcius, with units of second per square meter [s m^-2]</li> </ul> </li> <li> <p><strong>25/500/1000/1300/1500/2000 Min Creep [VG/NH/CB/PL5] [1/s]</strong>:</p> <ul> <li>Minimum creep rates at various temperatures (in Celcius), measured in strain per second [1/s]</li> <li><strong>VG</strong>: Viscous Glide (Jaswon-Cottrell) creep model</li> <li><strong>NH</strong>: Nabarro-Herring creep model</li> <li><strong>CB</strong>: Coble creep model</li> <li><strong>PL5</strong>: Power-law creep with n=5</li> </ul> </li> </ol>
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.