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3,206 results for “property (T)”
Data from: Acoustic and electrical properties of Fe-Ti oxides with application to the deep lunar mantle
<p>The overturn of titanium-rich mantle cumulates has been invoked to explain the structure and dynamics of the Moon. These dense cumulates are stable at the core-mantle boundary (CMB) and could explain field anomalies inferred from geophysical studies. We report the first acoustic and electrical experiments on natural ilmenite-rutile aggregates up to 4.5 GPa and 1920 K. Seismic velocities show a weak pressure and temperature dependence, with Vs ~4.2 (+/-0.2) km/s and Vp ~ 8.0 (+/-0.2) km/s at the CMB conditions. Conductivity increases by a factor > 10<sup>4</sup> over 373-1920 K and is >10<sup>3</sup> S/m above 1573 K. Seismic and electrical mixing models of Fe-Ti oxides - olivine rocks based on our results indicate that field velocity and conductivity estimates are reproduced satisfactorily with 3-16% Fe-Ti oxides and 20% melt. Interactions between this Ti-rich and melt-bearing layer and the adjacent core likely affect the cooling and magnetic history of the Moon.</p>
Dataset for Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging
<p>In this repository, we provide all the datasets that are needed to reproduce the analysis conducted in the paper entitled "Estimating soil hydraulic properties from oven-dry to full saturation using inverse modeling and shortwave infrared imaging."</p> <p><br> codes: This folder contains Python codes to run the forward and inverse modeling. Install the following packages.<br> notebook, fenics, numpy, pandas, matplotlib, scipy, numdifftools, and lmfit for inverse modeling (needs to be run on Linux).<br> data: This directory contains data used in the inverse modeling.<br> gif: This directory contains GIF movies of the upward infiltration experiments.</p> <p>readme.xlsx: This file explains which data are used for each figure in the paper.</p>
Potential and training data for 'Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential'
<p>Fitted potential, training and testing data.</p>
Investigation of cirrus clouds properties in the Tropical Tropopause Layer using high-altitude limb scanning near-IR spectroscopy during the NASA-ATTREX Experiment
<p>Data results of the findings of the AMT-2023-85 research article, titled: "Investigation of cirrus clouds properties in the Tropical Tropopause Layer using high-altitude limb scanning near-IR spectroscopy during the NASA-ATTREX Experiment", submitted to the Atmospheric Measurement Techniques journal on 20 Apr 2023.</p>
To enhance CO2 saturation prediction from seismic data by joint use of attenuation and elastic properties: A machine learning approach
<p>Wang and Zhao submitted for GJI</p>
Influence of Molecular Hydrogen on Bulk and Interfacial Properties of Three Imidazolium-Based Ionic Liquids by Experiments and Molecular Dynamics Simulations
<p>Original materials including both experimental and simulation data.</p> <p>Pictures (.bmp) of pendant drop method and the surface light scattering signals (.asc).<br>Raw simulation data for the surface tension. The topology files and all the final structure files(.gro) are included for the two systems at all studied temperature and pressures.</p>
Influence of calcination temperature and particle size distribution on the physical properties of SrFe12O19 and BaFe12O19 hexaferrite powder - part 2
Open the record for dataset details and reuse information.
Computing solubility and thermodynamic properties of H2O2 in water
<p>The input files used for GROMACS and BRICK software in this work has been uploaded. The dataset contains .itp files for the H2O2 forcefields used and the .mdp file used for a NPT run on aqueous H2O2 solution. GROMACS was used to determine the densities, viscosities and diffusion coefficients of H2O2 in water. Also, BRICK input files (forcefield.in, settings.in, topology.in, H2O2, water, restart.in and weightfunction.in) has been uploaded. BRICK was used to calculate the solubility (Henry coefficient) of H2O2 in water. </p>
Impact of CO2-rich seawater injection on the flow properties of basalts
<p>This is the dataset that was used to run the pore network simulations in the published work "Impact of CO2-rich seawater injection on the flow properties of basalts" by E. Stavropoulou, C. Griner and L. Laloui 2024 in the International Journal of Greenhouse Gas Control.</p> <p>More precisely the provided data are the segmented porosity tomographies before (pre-CO2) and after (post-CO2) exposure to CO2 for cores 05-02, 08-02 and 08-03.</p> <ul> <li>05-02-pre and -post: 58 μm/px</li> <li>08-02-pre and -post: 52 μm/px</li> <li>08-03-pre and -post: 52 μm/px</li> </ul> <p>Additional data, e.g. the scripts to run the simulations (based on openPNM) can be provided upon request.</p>
Thermophysical properties of NaF-KF-UF4
<p>Thermophysical properties data collection of the molten fuel salt candidate NaF-KF-UF4 for advanced reactor development. A dedicated paper will be published shortly.</p>
Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Collected data from finite element simulations to calculate the mechanical properties of innovative CLT using ABAQUS
<p>A dataset is collected from finite element computation using ABAQUS for different configurations of innovative CLT. In the dataset, we have the inputs (w, t1, t2, t3, t4, s, E_L, G_LZ, G_CZ) describing the microstructure of innovative CLT, and the elastic properties (Membrane stiffness: A11, A22, In-plane Poisson effect stiffness: A12, In-plane shear stiffness: A33, Bending stiffness: D11, D22, Out of plane Poisson effect stiffness: D12, Torsional stiffness: D33, out of plane Bending Gradient shear compliances: h11, h12, h16, h22, h26, h33, h34, h35, h44, h45, h55, h66) deriving from the FE computations. We have also the results of closed-form solutions that predicts these elastic properties of innovative CLT. </p>
The compiled dataset on the physicochemical properties of snow used for evaluation
<p>This repository contains scirpts and files used to generate the results of the study titled "Simulations of Snow Physicochemical Properties in Northern China using WRF-Chem" from Xia Wang, Lei Geng and Chun Zhao. All the original simulation and observation data used by Xia Wang to evaluate the physical and chemical properties of snow. This comprehensive dataset includes detailed information on various snow characteristics, such as snow cover, snow depth, and the presence of black carbon (BC), dust, and nitrates.<span> </span></p>
Reproducing nonlinear ground response and pore pressure variations using in-situ soil properties
<p>The zip file named 'Seismic and pore water pressure data.zip' contains all the seismic and pore water pressure data in our study.</p><p>The zip file named 'WLA model.zip' contains the numerical models used in our study.</p>
Thermal properties of 'athermal' granular materials
Open the record for dataset details and reuse information.
Experimental Data - Physicochemical Properties of 20 Ionic Liquids Prepared by the Carbonate-Based IL (CBILS) Process
Open the record for dataset details and reuse information.
Dataset for "Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: comparison of volcanic ash and desert dust" published in GeoHealth
<p>Data Repository for:</p> <h2><strong>Physicochemical properties and bioreactivity of sub-10 µm geogenic particles: comparison of volcanic ash and desert dust</strong></h2> <p>Ines Tomašek<sup>1,2,3*</sup>, Julia Eychenne<sup>1,2</sup>, David E. Damby<sup>4</sup>, Adrian Hornby<sup>5,6</sup>, Manolis N. Romanias<sup>7</sup>, Severine Moune<sup>1</sup>, Gaëlle Uzu<sup>8</sup>, Federica Schiavi<sup>1</sup>, Maeva Dole<sup>1</sup>, Emmanuel Gardès<sup>1</sup>, Mickael Laumonier<sup>1</sup>, Clara Gorce<sup>1</sup>, Régine Minet-Quinard<sup>2,9</sup>, Julie Durif<sup>9</sup>, Corinne Belville<sup>2</sup>, Ousmane Traoré<sup>10,11</sup>, Loïc Blanchon<sup>2</sup><sup>†</sup>, Vincent Sapin<sup>2,9</sup><sup>†</sup></p> <p><sup>1</sup>Laboratoire Magmas et Volcans (LMV), CNRS, IRD, OPGC, Université Clermont Auvergne, France.</p> <p><sup>2</sup>Institute of Genetic Reproduction and Development (iGReD), Translational Approach to Epithelial Injury and Repair Team, CNRS, INSERM, Université Clermont Auvergne, France.</p> <p><sup>3</sup>Istituto Nazionale di Geofisica e Vulcanologia (INGV), Osservatorio Etneo, Catania, Italy.</p> <p><sup>4</sup>Volcano Science Center, U.S. Geological Survey (USGS), USA.</p> <p><sup>5</sup>Department of Earth and Atmospheric Sciences, Cornell University, USA.</p> <p><sup>6</sup>Department of Cellular and Molecular Biology, School of Medicine, University of Texas at Tyler, USA.</p> <p><sup>7</sup>Institut Mines-Télécom (IMT) Nord Europe, Centre for Energy and Environment, Université Lille, France.</p> <p><sup>8</sup>Université Grenoble Alpes, IRD, CNRS, INRAE, INP-G, IGE (UMR 5001), France.</p> <p><sup>9</sup>Centre Hospitalier Universitaire (CHU) Clermont-Ferrand, Biochemistry and Molecular Genetics Department, France.</p> <p><sup>10</sup>Centre Hospitalier Universitaire (CHU) Clermont-Ferrand, Infection Control Department, France.</p> <p><sup>11</sup>Laboratoire Microorganismes: Génome Environnement (LMGE), UMR, CNRS, Université Clermont Auvergne, France.</p> <p><sup>†</sup>These authors share the last authorship.</p> <p><sup>*</sup>Correspondence.</p>
Physicochemical properties (viscosity, electrical conductivity) of betaine based deep eutectic solvents
<p>New deep eutectic solvents (DES) based on betaine as a hydrogen bond acceptor and urea, lactic acid, glycerol, 1,2-propanediol, and xylitol as hydrogen bond donors have been prepared. Viscosity and electrical conductivity were investigated. </p>
Schiff Base Crosslinked Hyaluronic Acid Hydrogels with Tunable and Cell Instructive Time-Dependent Mechanical Properties
<p>This folder contains raw data for the paper titled "<strong>Schiff Base Crosslinked Hyaluronic Acid Hydrogels</strong><strong> with Tunable and Cell Instructive Time-Dependent Mechanical Properties</strong>" authored by Taha Behroozi Kohlan, Yanru Wen, Carina Milena Mini, and Anna Finne-Wistrand, published in <em>Carbohydrate Polymers</em> journal. The raw data presented here contains NMR, FTIR, SEC, swelling and stability, and rheology data used to create figures.</p> <p><strong>Abstract:</strong></p> <p>The dynamic interplay between cells and their native extracellular matrix (ECM) influences cellular behavior, imposing a challenge in biomaterial design. Dynamic covalent hydrogels are viscoelastic and show self-healing ability, making them a potential scaffold for recapitulating native ECM properties. We aimed to implement kinetically and thermodynamically distinct crosslinkers to prepare self-healing dynamic hydrogels to explore the arising properties and their effects on cellular behavior. To do so, aldehyde-substituted hyaluronic acid (HA) was synthesized to generate imine, hydrazone, and oxime crosslinked dynamic covalent hydrogels. Differences in equilibrium constants of these bonds yielded distinct properties including stiffness, stress relaxation, and self-healing ability. The effects of degree of substitution (DS), polymer concentration, crosslinker to aldehyde ratio, and crosslinker functionality on hydrogel properties were evaluated. The self-healing ability of hydrogels was investigated on samples of the same and different crosslinkers and DS to obtain hydrogels with gradient properties. Subsequently, human dermal fibroblasts were cultured in 2D and 3D to assess the cellular response considering the dynamic properties of the hydrogels. Moreover, assessing cell spreading and morphology on hydrogels having similar modulus but different stress relaxation rates showed the effects of matrix viscoelasticity with higher cell spreading in slower relaxing hydrogels.</p>
Data for Influence of Coverage Dependence on the Thermophysical Properties of Adsorbates and its Impact on Microkinetic Models
<p>Data and scripts for the preprint "Influence of Coverage Dependence on the Thermophysical Properties of Adsorbates and its Impact on Microkinetic Models".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.