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185 results for “Granular”
stationary_granular_flow_seismicity_and_optics
<p>Raw data acquired during the study of seismic sources emitted by a laboratory landslide: a stationary granular flow in an inclined flume. The data consists in images acquired by a fast camera and accelerometers. The scripts to treat the data are also shared.</p>
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
High Granularity Electromagnetic Shower Images
<p>This is a limited subset of the data used for training in <strong>arXiv:2005.05334</strong>. The network architectures and instructions to generate more data are available at <a href="https://github.com/FLC-QU-hep/getting_high">here. </a></p> <p>Electromagnetic calorimeter for the ILD consists of 30 active silicon layers in a tungsten absorber stack with 20 layers of 2.1 mm followed by 10 layers of 4.2 mm thickness respectively. We project the sensors onto a rectangular grid of 30×30×30 cells. Each cell in this grid corresponds to exactly one sensor, resulting in total of 27k channels.</p> <p>The file has the following structure:</p> <ul> <li> Group named <em>30x30</em> <ul> <li> <em>energy</em> : Dataset {1000, 1}</li> <li> <em> layers</em> : Dataset {1000, 30, 30, 30}</li> </ul> </li> </ul> <p>The <em>energy</em> specifies the true energy of the incoming photons in units of GeV, where <em>layers</em> represent the energy deposited (MeV) in 30 layers of the calorimeter in an image data format. This file contains approximately 24.000 showers.</p>
CaliParticles: A Benchmark Standard for Experiments in Granular Materials
<p>Granular materials are discrete particulate media that can flow like a liquid but also be rigid like a solid. This complex mechanical behavior originates in part from the particles shape. How particle shape affects mechanical behavior remains poorly understood. Understanding this micro-macro link would enable the rational design of potentially cheap, light weight or robust materials. To aid this development, we have produced a set of standard particle shapes that can be used as benchmarks for granular materials research. Here we describe the collection of benchmark shapes. Some part of the particles are modeled on superquadrics, others are custom designed. The particles used so far were made from polyoxymethylene (POM) and Thermoplastic elastomers (TPE) whose specifications are also listed. The benchmark shapes are available as molds in a plastics manufacturing company, whose contact information is also included. The company is capable of making other molds as well, giving access to more particle shapes. The same particle shapes can thus also be made in different types of (colored) plastic, and in amounts of 50.000 particles or more, larger than conveniently be produced with a 3D printer. We also provide the associated .step and .stl files in the repository in which this document is included. </p>
GrainLearning: A Bayesian uncertainty quantification toolbox for discrete and continuum numerical models of granular materials
GrainLearning is a Bayesian uncertainty quantification and propagation toolbox for computer simulations of granular materials. The software is primarily used to infer and quantify parameter uncertainties in computational models of granular materials from observation data, also known as inverse analyses or data assimilation. Implemented in Python, GrainLearning can be loaded into a Python environment to process the simulation and observation data, or alternatively, as an independent tool where simulation runs are done separately, e.g., via a shell script.
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering Patterns in a Granular Basalt
<p>Data used in the Milici et al. <em>Geobiology </em>article "The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering in Granular Basalt". The data result from a greenhouse experiment in which 14 genotypes of Alfalfa <em>(Medicago</em> sativa) were grown in an unweathered granular basaltic tephra, exposed to an early successional soil microbial community, and replicated across three different soil moisture treatments. This experiment seeks to identify the roles of vascular plants and soil microbes on mineral weathering. Please see the article for full project description. </p> <p>General File Descriptions:</p> <p>"AllPerformanceGeochem.csv" contains both the performance and geochemistry data associated with each plant grown in the experiment and is used for the majority of the analyses.</p> <p>"FullCensusTimeSeries.csv" contains the growth and survival data for the plants across the entire 3 month duration of the experiment and is used only to calculate survival rate and growth rate.</p> <p>"pottingsoilmass.csv" contains the data for alfalfa grown in potting soil and is used to compare how much the basalt limited plant growth relative to a potting soil control. </p> <p>These data are cleaned and formatted for analysis via the code in the github repository linked to this data repository. </p> <p> </p>
A phenomenological law for complex granular materials from Mohr-Coulomb theory
<p>The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3). </p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Granular Material Candidates
<p>A database of 14 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Dataset - Gravisensors in plant cells behave like an active granular liquid
<p>Dataset corresponding to data shown in article : https://hal.archives-ouvertes.fr/hal-01425298v2 (avalanches of statoliths pile in wheat coleoptile cells, avalanches of silica micro-particles in biomimetic cells, vertical fluctuations of statoliths in wheat coleoptile cells or extracted from their cells).</p> <p>Examples of python scripts that open and plot the data are also included.</p>
Electron Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a positron passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the positrons with energy ranging from 20 to 350 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</p>
The (in)sensitivity of granular creep to materials and boundaries
<p>Experimental data reported in the publication "The (in)sensitivity of granular creep to materials and boundaries".</p> <p>jupyter notebook codes are included for data analysis and generating figures in the paper.</p> <p>datasets for Kaolinite material and for smooth and rough boundaries</p>
Dynamic strength characterisation of wet granular ice layers
<p>Dataset for the research paper "Dynamic strength characterisation of wet granular ice layers" submitted to the International Journal of Impact Engineering.</p>
Pion Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a pion passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the pions with energy ranging from 10 to as high as 500 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</p>
Simulation data and scripts for CFD-DEM simulation of saturated bi-disperse granular flows
<ul> <li>Data set 1 - contains the raw data required to replicate and validate all plots in the main article. </li> <li>Sample case - a .zip file which includes codes which are needed to simulate a CFD-DEM case of a steady granular flow in water with cyclic boundaries in the stream wise direction. Also enclosed is a ReadMe.txt file detailing the implementation instructions for both Esys particle and OpenFOAM codes. Download links for Esys particle and OpenFOAM are also included</li> <li>Geo file generator - a .zip file which includes Esys particle codes that can be used to generate a .geo file specifying the initial position of the particles used in the test simulations. A ReadMe.txt file is enclosed with more detailed implementation instructions.</li> </ul>
Sealing in terracotta impressed with a cast of a Yaudheya coin; coarse granular on the reverse
<p>Sealing in terracotta impressed with a cast of a Yaudheya coin; coarse granular on the reverse. British Museum 1892,1014.50.b.</p> <p> </p> <p> </p>
Dataset accompanying the article: Analyzing X-Ray tomographies of granular packings
<p>This dataset (and the added analysis software) belong to the article: <em>Analyzing X-Ray tomographies of granular packings</em> in Review of Scientific Instruments.</p> <p>The abstract of the article: Starting from three-dimensional volume data of a granular packing, as e.g. obtained by X-ray Computed Tomography, we discuss methods to first detect the individual particles in the sample and then analyze their properties. This analysis includes the pair correlation function, the volume and shape of the Voronoi cells and the number and type of contacts formed between individual particles. We mainly focus on packings of monodisperse spheres, but we will also comment on other monoschematic particles such as ellipsoids and tetrahedra. This paper is accompanied by a package of free software containing all programs (including source code) and an example three-dimensional dataset which allows the reader to reproduce and modify all examples given.</p> <p> </p>
Continuously fluctuating selection reveals extreme granularity and parallelism of adaptive tracking
<p>Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown. We generated high-frequency, genome-wide allele frequency data from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December, a period of ~12 generations. Adaptation throughout the fundamental ecological phases of population expansion, peak density, and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of allele frequency change to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal fitness-relevance of standing variation that is likely to be masked by inference approaches based on static population sampling, or insufficiently resolved time-series data. We propose such fine-scaled temporally fluctuating selection may be an important force maintaining functional genetic variation in natural populations and an important stochastic force affecting levels of standing genetic variation genome-wide.</p>
Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans
<p>This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size. </p>
Datasets, codes and video clips for the laboratory flume tests of granular flow
<p>Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.</p> <p>The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.</p> <p><strong>S1_data_granular flow_velocity</strong> provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.</p> <p><strong>S2_data_granular flow_stress</strong> provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.</p> <p><strong>S3_data_granular flow_flow depth</strong> provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs. </p> <p><strong>S4_data_granular flow_deposit</strong> provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.</p> <p><strong>S5_data_granular flow_density</strong> gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.</p> <p>The videos of the granular flows under different experimental conditions during their propagation are provided in <strong>'S6_video_granular flow.zip'</strong> to show the granular flow behavior and its evolution. <strong>S6_video_granular flow</strong> includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow . </p> <p><strong>S7_codes_data analysis</strong> provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth. </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.