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3,206 results for “property (T)”
Datasets for "Constraints on the geometry and frictional properties of the Main Himalayan Thrust using co-, post- and interseismic deformation in Nepal" "
<p>This contains GNSS and InSAR data used in the publication "Constraints on the geometry and frictional properties of the Main Himalayan Thrust using co-, post- and interseismic deformation in Nepal". GNSS data come as separate text files for east and north displacements and errors. Each text file consists of a number of columns. Each file has 40 columns, where the first column is time in decimal years after the Gorkha earthquake and the following columns correspond to each GNSS station. The time series are those obtained after the removal of "non-postseismic" deformation such as linear and sinusoidal terms</p> <p>The InSAR data are in the form of GMT GRD files at 200m resolution. Data are from Sentinel-1a track 19 and are a derived work of Copernicus data, subject to the ESA use and distribution conditions. The name of each GRD file is as follows: t19_{date1}_{date2}_hgt_ramp_200m.grd. Date 1 and date 2 are the dates of the two images used to make each interferogram.</p>
parallelpro/numax: Source code for "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax" by Li, Yaguang et al. (2024)
<p>This repository contains the datasets and Python scripts used in the paper "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity, and Numax" by Yaguang Li et al. (2024). The stellar models used in this project are available on <a href="https://doi.org/10.5281/zenodo.12815718">Zenodo</a>.</p>
Properties of intermediate- to high-mass stars in the young cluster M17 Characterizing the (pre-)Zero Age Main Sequence
<p>Data and extra figures of the paper "Properties of intermediate- to high-mass stars in the young cluster M17 -- Characterizing the (pre-)Zero Age Main Sequence" by Frank Backs. Published in Astronomy and Astrophysics.</p> <p>The observed data and the fitted models are included. The fitting algorithm Kiwi-GA is available on Github: https://github.com/sarahbrands/Kiwi-GA</p> <p>Additional figures are available. These figures give an overview of the best fit models for each line like Fig. 1 in the paper. </p>
Data accompanying: Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures
<p>These are the data accompanying the publication titled: <em>Impact of interface traps on charge noise and low-density transport properties in Ge/SiGe heterostructures</em></p> <p>The repository contains:</p> <ul> <li>FigurePlotting.ipynb : JupyterNotebook used to load the data and plot the Figures.</li> <li>FigureData : folder that contains the raw/analysed data needed for plotting the Figures.</li> </ul> <p>The JupyterNotebook is organised in sections corresponding to the Figures and Supplementary Figures of the last version (published) of the paper. For each Figure/SuppFigure the needed data (stored in .txt or .pkl files in the FigureData folder) is loaded and plotted.</p> <p> </p>
Gas permeability and mechanical properties of dust grains at hyper- and zero-gravity
<p>Two-phase flow experimental data obtained from the TEMPusVoLA facility over the course of three Zero-gravity parabolic flight campaigns (<strong>PFCs</strong>). The experiment, designated 'System 1' , contains low-pressure gas and analogue dust particles (of diverse compositions, shapes, and sizes) that experience a low-pressure gas cross-wind normal to the gravitational force vector. During steady flight, the particles pack into the lower end of the chamber and the layer of granular media creates a pressure differential, which is used to extract permeability coefficients in both the continuum and molecular-diffusion dominated regimes. We compare the permeability both with and without the effect of gravity and make comparisons with the conditions on low-gravity objects in the solar system, such as planetesimals and the dust particles that comprise them. In particular, we are able to compare and contrast the cohesiveness of the dust-regolith analogue types, which has consequences for formation theories of Solar-system bodies. </p> <p> </p> <p><strong>Data products: </strong></p> <p><strong>Pressure, acceleration</strong></p> <p>Each file corresponds to a particular parabolic flight campaign and day of flight. The 4th Swiss PFC lasted 1 day in 2020. The 75 ESA PFC lasted 3 days in 2021, and the the 78th ESA PFC lasted 3 days in 2022. Data was obtained in System 1 during all flights of all campaigns, and for a set of 12-16 parabolas. On each parabola, we studied the sample in one out of three chambers and changed the gas mass flux rate after cycling through all three samples. The below files were used to correlate changes in sample gas permeability with changes in gravitational loading. </p> <p>-- Raw pressure files output from 6-channel pressure logger measuring pressure as function of time as .csv files: DATALOG_DATE. </p> <p>-- Raw acceleration files output from accelerometer measuring gravitational load on aircraft.</p> <p><strong>Imaging data</strong></p> <p>-- Movie sequences obained with high-resolution thor-labs camera. All three chambers were imaged, despite only one chamber being subject to active gas flow at any given time. movie1 and movie 2 are referenced in the associated publication. </p> <p><strong>Multimeter data</strong></p> <p><strong>-- </strong>Output from photo-diodes measured across 9 channels using a Keithley Multimeter/Multiplexor. Acquisition speed was 27 Hz. </p>
Description of Mont-Vert hillslope database: measured properties along soil cores and associated metadata
<p>This dataset includes data and metadata of twenty soil cores collected in Mont-Vert banana plantation (Le Robert, Martinique, France) in February 2023. These data were collected in order to study the chlordecone (an organochlorine insecticide) redistribution along a cultivated hillslope. To this end, radiocesium activities (artificial radionuclide) and chlordecone concentrations were measured in soil core subsamples (5-cm increments). </p>
FIGURE 2 in A new species of Ternstroemia (Pentaphylacaceae) from La Amistad Binational Park and World Heritage Property, Costa Rica and Panama
FIGURE 2. Living material of Ternstroemia amistadensis Q. Jiménez & D. Santam.: A, leafy branch, showing lower surface of leaves with dark punctations. B, decomposed fruit and sepals. C, mature fruit. Images are of living material (prior to pressing) of Solano et al. 5721 (MO). Photos by Daniel Solano Peralta.
FIGURE 1. Ternstroemia amistadensis Q in A new species of Ternstroemia (Pentaphylacaceae) from La Amistad Binational Park and World Heritage Property, Costa Rica and Panama
FIGURE 1. Ternstroemia amistadensis Q. Jiménez & D. Santam.: A, fertile branch with fruit, with enlarged view of lower leaf surface with black punctations. B, flower bud. C, immature fruit, with an insertion highlighting the unequal bracteoles borne opposite or subopposite the base of the calyx. D, mature fruit, with section cleared to reveal seeds. E, seeds. Illustration based on images of living material and herbarium specimens of Santamaría 6706 (INB).
Datasets of Aerosol properties retrieved from AOD time series at selected GAWPFR stations
<p>The datasets of aerosol properties retrieved from Aerosol Optical Depth (AOD) measurements from Precision Filter Radiometers (PFR) using the model Generalized Retrieval of Atmosphere and Surface Properties-Aerosol Optical Depth (GRASP-AOD). They correspond to 3 stations of the GAW-PFR network (Davos, Izana and Hohenpeissenberg) and one associated (Lindenberg).</p> <p>Each file corresponds to a station (identifier DAV for Davos, HPB for Hohenpeissenberg and IZO for Izaña). The file names include the identifier for each station, the starting and ending year of the measurements. The first 4 lines of the file include information about the station and the 5th about the columns of the file. The following columns are included in every text file: date(1-3), time UTC (4-6), air mass (7), PFR AOD at 368, 412, 500 and 862 nm (8-11), retrieved PFR AOD from GRASP at 368, 412, 500 and 862 nm (12-15), Angström Exponent (16), Fine mode aerosol optical depth (17), Coarse mode aerosol optical depth (18), Total volume concentration (19), Fine mode volume concentration (20), Coarse mode volume concentration (21), Effective radius (22), Fine mode volume median radius (23), Coarse mode volume median radius (24), Fine mode geometric standard deviation (25), Coarse mode geometric standard deviation (26), Inversion absolute error (27), Inversion relative error (28).</p> <p>The dataset includes the full time series days per station. At stations there was a CIMEL sun photometer from AERONET measuring in parallel, which we used for the validation of the aerosol properties.</p> <p>We also include a report describing the work in more detail, including the validation process and results.</p>
The Data for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation'
<p>Produced synthetic kilonova spectra for 'Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation' using the Sedona radiative transfer code. Each model is an h5 file with the following groups:</p> <ul> <li>Lnu - Array of length # of timesteps by # of frequency bins that contains the spectral sequence of each kilonova model in cgs units (erg/s/Hz)</li> <li>click - Array of length # of timesteps by # of frequency bins for the number of Monte Carlo particles that make up Lnu</li> <li>mu - Center of polar angular bins (not useful since simulations are 1D)</li> <li>mu_edges - Edges of polar angular bins (not useful since simualtions are 1D)</li> <li>nu - Array of length # of frequency bins that are the central frequencies of a bin (Hz)</li> <li>nu_edges - Array of length # of frequency bins +1 that are the edges of each frequency bin (Hz)</li> <li>phi - Center of azimuthal angular bins (not useful since simulations are 1D)</li> <li>phi_edges - Edges of azimuthal angular bins (not useful since simulations are 1D)</li> <li>time - Times at which each spectrum was generated (Days)</li> <li>time_edges - Edges of time bins (Days)</li> </ul> <p>Each kilonova spectra file is named according to <atomic dataset>_<thermalization prescription>_KN_<lanthanide fraction>X_lan_<characteristic velocity>v_<mass>M_1D_spec.h5 where:</p> <ul> <li>atomic dataset is one of: HULLAC, ATOMIC, or Autostructure</li> <li>thermalization prescription is one of: local or global</li> <li>lanthanide fraction is the fraction of lanthanides in the ejecta by mass</li> <li>characteristic velocity is the kinetic velocity of the ejecta in units of the speed of light</li> <li>mass is the mass of the ejecta in units of solar masses</li> </ul>
Dataset for the article : Non-invasive estimation of in vivo optical properties and hemodynamic parameters of domestic animals: a preliminary study on horses, dogs, and sheep
<div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This dataset includes all the necessary data to understand and replicate the figures and tables presented in the article titled "Non-invasive Estimation of In Vivo Optical Properties and Hemodynamic Parameters of Domestic Animals: A Preliminary Study on Horses, Dogs, and Sheep." Specifically, it contains the raw measurement curves, along with the optical and hemodynamic parameters derived from the analysis of these raw data.</p> </div> </div> </div> </div> </div> </div>
Measurement report: The variation properties of aerosol hygroscopic growth related to chemical composition during new particle formation days in a coastal city of southeast China
<p>The aerosol scattering hygroscopic growth factor (f(RH)), along with conventional air pollutants and meteorological parameters were simultaneously observed from February to April 2022 in Xiamen, a coastal city of southeast China.</p>
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties using Machine Learning
<p>This dataset accompanies a study that investigates the use of machine learning (ML) approaches for predicting seven different physical properties of 140 binary copolymers. Two computational methods were employed: a random forest (RF) model based on molecular descriptors and a Graph Neural Network (GNN) using 2D polymer graphs. These methods were applied in both single- and multi-task settings to explore the strengths of each approach in capturing various polymer properties.</p> <p>The dataset includes two files:</p> <ol> <li> <p><strong>Dataset.xlsx</strong>: Contains the following information for each of the 140 copolymers:</p> <ul> <li>Polymer names</li> <li>SMILES notation of the monomers</li> <li>Fraction of monomers in each copolymer</li> <li>Simulated values (calculated using molecular dynamics simulation) and experimental values of various physical properties, including density, specific heat capacity at constant pressure ad volume ,radius of gyration, linear expansion coefficient, volume expansion coefficient, and bulk modulus .</li> </ul> </li> <li> <p><strong>Descriptors.xlsx</strong>: Provides the molecular descriptors calculated using PaDEL-Descriptor software, which were used as input for the RF model to predict polymer properties.</p> </li> </ol> <p>This work provides insight into the comparative strengths of descriptor- and graph-based representations in machine learning models for predicting material properties. It also highlights the importance of selecting appropriate molecular representations based on the nature of the properties being predicted.</p>
Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'
<div> <div>This dataset contains the data produced for the above paper. The dataset consists of:</div> <div> </div> <div>- input nanopillars before deformation (molecular_dynamics/nanopillars)</div> <div>- stress-strain curves acquired by deforming the nanopillars (molecular_dynamics/stress_strain_curves)</div> <div>- weights of the CNNs trained to predict mechanical properties of the nanopillars (machine_learning/train_CNN)</div> <div>- Grad-CAM fields of the predictions (machine_learning/train_CNN)</div> <br> <div>Codes used for creating and analyzing the dataset are available at https://github.com/tekoivisto/nanopillar-ML</div> </div>
Description of suspended sediment and soil samples database: measured properties associated metadata
<p>From the perspective of open science, this dataset includes data and metadata of soil and suspended sediment samples collected across three catchments in Martinique (French West Indies) : Galion river catchment, Lézarde river catchment and Salée river catchment. During one year (June 2022 - July 2023), suspended sediment samplers were installed in the three river networks. In order to determine the signature of the potential erosion sources, soil samples were collected across the three catchments. In order to quantify suspended sediment source contributions and chlordecone contamination of suspended matter transiting in river, a set of different properties were measured in soil and suspended sediment samples: radionuclide activities, carbon and nitrogen isotopes, elemental geochemistry, visible colorimetry, chlordecone and its degradation products concentrations</p>
Observational Properties of Harmonic EMIC waves: Statistical Study
<p>The data is used for the EMIC event list in the paper.</p>
Data covering Q10 and soil physicochemical properties of China's forest ecosystems
<p><span>Data including experiment description, site geographic location, climate variables, initial edaphic variables, and factors related to substrate quality, substrate availability, and microbial properties both before and after the treatments.</span></p>
Inlists used for studying the astrophysical properties of the Sirius binary system
<p>Using mesa-r23.05.1 a study of the sirius system was performed, both as a non-interacting binary, as well as test suites that take sirius b up until the wd cooling phases in an attempt to better understand the age of the system and the limits of the progenitor mass. Additionally, we hope to put constraints on the temperature, luminosity, and white dwarf mass by comparing this simulation to HST data. Lastly, we use these models to calculate an initial to final mass relation for the system.</p> <p>The inlists used have been included as well as some mod files for different metallicities and initial masses. It is split into three sections pertaining to the binary suite, and single star evolutions where we create carbon and oxygen white dwarfs for lower initial masses of sirius b, as well as a test suite that creates a neon oxygen white dwarf for larger initial masses.</p> <p>The mesasdk version is from July of last year (version 23.7.3)</p>
Dataset for "Insect frass from upcycling vegetable by-products with cereals: effects on the soil properties, plant development and soil invertebrate fitness"
Open the record for dataset details and reuse information.
X-Shooting ULLYSES: Massive Stars at low metallicity VI. Atmosphere and mass-loss properties of O-type giants in the SMC
<p>Figures from X-Shooting ULLYSES: Massive Stars at low metallicity VI. Atmosphere and mass-loss properties of O-type giants in the SMC. The individual fits of the stars and their explored parameter space. </p> <p>Caption:</p> <p>Overview of the fitting results of AV\,80. The \textit{top} part shows the line profiles included in the fitting process, with the name of each feature indicated in the bottom left. The green line shows the best fit, with the shaded region showing the 1$\sigma$ uncertainty on the model fit. The black vertical bars indicate the observed flux, with the length indicating the uncertainty. The horizontal axis shows the wavelength in \AA. The \textit{bottom} part shows the distribution of $1/\chi^2_{\rm r}$ for each parameter (indicated on the top left), with $\chi^2_{\rm r}$ the reduced $\chi^2$ value. Each scatter point indicates one {\sc Fastwind} model calculated by Kiwi-GA. The color indicates the generation in which the model was computed, with light gray the first generation and black the last. The vertical yellow line indicates the best fit value for each parameter, and the shaded regions are the 1 and 2 $\sigma$ confidence intervals. The distributions in the red shaded area are from the optical only GA fit.</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.