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5 results for “Flow Irregularities”
Flow properties of slow granular flows of irregular grains
<p>This repository holds the tomographic data of slow granular flows of irregular grains in shear cell and in silo. We tested the 3D flow properties of realistic (non-spherical) granular materials with special emphasis on the orientational ordering of the grains and its effect on the flowability of the sample. In this collaborative work between the Wigner Research Centre (Budapest) and Otto-von Guericke University (Magdeburg), laboratory experiments have been performed in two geometrical configurations: (I) quasi-static shear flow in a split bottom Couette shear device and (II) slow flow in a silo.<br> <br> The read_me.txt contains the structure of the data. Additional information/data not included in this repository is available upon request.</p>
MIrreM Public Database on Irregular Migration Flow Estimates and Indicators
<p><span>This <em>Public Database on Irregular Migration Flow Estimates and Indicators</em>, in short MIrreM D5.2, is a MIrreM project deliverable under work package 5.</span><strong><span> </span></strong><span>This database provides an </span><span>inventory and critical appraisal of available estimates and indicators related to irregular migration flows. More specifically, <span>the database contains the country-level data collected by MIrreM’s national rapporteurs, as well as EU-level data from sources other than Eurostat. The datasets include </span>meta-level information on sources and methodology and a quality assessment based on MIrreM’s criteria. </span></p> <p><span>Users of this database are advised to consult the following <strong>companion document</strong> (henceforth, MIrreM Working Paper No. 9/2024) for a full discussion of the context, the underlying concepts, and the methodology used: </span></p> <p><span>Siruno, L., Leerkes, A., Hendow, M. & Brunovská, E. 2024. Working Paper on Irregular Migration Flows. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.%2010702228"><span>https://doi.org/10.5281/zenodo.</span> 10702228</a>.</span></p> <p><span>The MIrreM project is a follow-up to CLANDESTINO, which covered the period 2000-2007. MIrreM extends this to the subsequent period 2008-2023. The data covered in this database reflect what is available within this period. Most of the data was collected between June and October 2023, and thus in some cases, the data are only until 2022 pending complete reports for 2023. </span></p>
An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall
<p>Phase-averaged experimental data, phase- and spatial-averaged numerical data, and digitized roughness as described in: Dunbar, D., van der A, D.A., Scandura, T., O'Donoghue, T. (2023). An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall. Journal of Fluid Mechanics, 955, A33, doi:10.1017/jfm.2022.1090.</p> <p>If interested in the time-series data and/or the spatially varying numerical data please contact the corresponding author of the paper. </p>
dataset for "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator"
<p>This dataset contains the numerical simulation data describing the conveying behaviour of shaped-particle systems. Detailed study on the conveying behaviour is presented in a journal paper entitled "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Average particle speed (Fig. 7a) and XYZ components (Fig. 7b) for: Sphere at t = 2.1 s, Cube at t = 2.85 s, Biluna at t = 3.1 s, HexP at t = 3.45 s. (Data of Fig. 7)</p> <p>Dataset 2 - XYZ coordinates of a typical particle trapped in the granulator. (Data of Fig. 9)</p> <p>Dataset 3 - Particle retention numbers for different shaped particles. (Data of Fig. 10).</p> <p>Dataset 4 - Average particle speeds for different shaped particles. (Data of Fig. 11).</p> <p>Dataset 5 - Average axial particle speeds for different shaped particles (Data of Fig. 12).</p> <p>Dataset 6 - Residence time distributions for various shaped particles. (Data of Fig. 13)</p> <p>Dataset 7 - Normalised residence time distributions for various shaped particles. (Data of Fig. 14)</p> <p>Dataset 8 - Mean residence time for various shaped particles. (Data of Fig. 15)</p> <p>Dataset 9 - The variance of RTD for different shaped particles. (Data of Fig. 16)</p> <p>Dataset 10 - Cumulative exit age distribution for various shaped particles. (Data of Fig. 17)</p> <p>Dataset 11 - Normalised cumulative exit age distribution for various shaped particles. (Data of Fig. 18)</p> <p>Dataset 12 - Evolution of the cumulative power consumption for various shaped particles. (Data of Fig.19)</p> <p>Dataset 13 - Power consumption distribution for various shaped particles. (Data of Fig.20)</p>
Venous Irregularities, Flow and Perfusion in MS Study
ClinicalTrials.gov study NCT01593904. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.