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236 results for “Hydrodynamics”
LiftWEC deliverable D4.4: Dataset from 2D experimental test campaign, with calculated hydrodynamic forces
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. </em> <em>This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. </em><em>This new version contains all data from version 1 of the first set, which consists of measurement from the experimental testing, plus results from the data analysis calculating the hydrodynamic forces. These forces are calculated by removing the static force and the centrifugal force. For a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Demonstration of kilohertz operation of hydrodynamic optical-field-ionized plasma channels
<p>The compressed file contains the raw data used in the publication "Demonstration of kilohertz operation of Hydrodynamic Optical-Field-Ionized Plasma Channels," <em>Physical Review Accelerators and Beams </em><strong>25</strong>, 011301 (2022) DOI: 10.1103/PhysRevAccelBeams.25.011301.</p> <p>Further information on the organization of the data is provided in the README file included in the compressed file.</p> <p> </p>
Observing emergent hydrodynamics in a long-range quantum magnet
<p>Here lies the experimental data and the analysis codes for the manuscript titled "Observing emergent hydrodynamics in a long-range quantum magnet" to be published in Science.</p>
Dataset for Integrated hydrodynamic and machine learning models
<p>The dataset is the supplement to our publication in <a href="https://www.nonlinear-processes-in-geophysics.net/">Nonlinear Processes in Geophysics</a> (https://doi.org/10.5194/npg-2021-36). To use this data, please give us credit by citing our article.</p>
Datasets for "Resolving the microscopic hydrodynamics at the moving contact line"
<p>Datasets for the article:</p> <p>"Resolving the microscopic hydrodynamics at the moving contact line" <br> Amal K. Giri, Paolo Malgaretti, Dirk Peschka, and Marcello Sega<br> Phys. Rev. Fluids <strong>7</strong>, L102001<br> DOI: 10.1103/PhysRevFluids.7.L102001</p> <p>Includes:</p> <ol> <li>GROMACS input files</li> <li>Modifications to the GROMACS source code thermostat as described in the article</li> <li>Instructions on how to invoke the patched version of GROMACS with decoupled directions</li> <li>Matlab datafiles with FE solutions and scripts to analyse and compare them to MD velocity field (also included)</li> </ol> <p> </p> <p>See also: <br> https://github.com/Marcello-Sega/pytim<br> https://github.com/dpeschka/stokes-free-boundary</p>
The Response of Bed Elevation to Small-Scale Hydrodynamics within Coastal Mangroves
<p>Dataset for the paper titled "The Response of Bed Elevation to Small-Scale Hydrodynamics within Coastal Mangroves", including raw and processed data.</p>
Assessing Hydrodynamic resistance in Microfluidics: A Case Study - datasets
<p><strong><span>Abstract: </span></strong><span>Hydrodynamic resistance is a critical parameter in microfluidics, affecting device functionality and performance.</span><span> However, quantifying hydrodynamic resistance in microfluidics is a challenge due to many influencing factors and the difficulties associated with the precise measurements of low flow rates (< 10 </span><span><span>m</span></span><span>L/min) and pressure drops (< 5 kPa). This article presents a simple experimental test method for assessing hydrodynamic resistance, correlating with theoretical and numerical calculations. The results demonstrate good agreement between benchtop and theoretical data, suggesting a potential standardized method for assessing hydrodynamic resistance in microfluidic devices.</span></p> <p> </p> <p><span>In the files attached: Dataset</span></p> <p> </p> <p><strong><span>Funding:</span></strong><span> This project (20NMR02 MFMET) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. V.S. would like to acknowledge the FCT, I.P., for funding of the Research Unit INESC MN (UID/05367/2020) through pluriannual BASE and PROGRAMATICO and project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy – i4HB</span></p>
Fig. 11 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 11: RMSE profiles for temperature (A) and salinity (C). All associated profiles differences (Argo-model) for temperature (B) and salinity (D) in 6 discrete depths (10 m dark blue, 20 m light blue, 30 m red, 40 m pink, 50 m green, 60 m yellow).
Fig. 10 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 10: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from all the associated profiles of the study area (Fig. 1). Profile differences (Argo – model) of the average temperature (green line) and salinity (brown line) (B). T-S diagram of all Argo and model associated profiles for two depth layer zones (Argo: 200-800m light blue, 800-2000 m dark blue) (Model: 200-800 m pink, 800-2000 m red) (D).
Fig. 9 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 9: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from the associated profiles during the "winter" periods (November – April). The associated profiles for the "summer" periods (May – October) are shown in (B) and (D) for the temperature and salinity respectively.
Fig. 7 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 7: A: Argo salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). B: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). C: Model salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). D: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 8 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 8: A: Argo salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). B: Model salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 6 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 6: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the southern Ionian.
Fig. 5 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 5: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the northern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the northern Ionian.
Fig. 3 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 3: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Adriatic region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the south Adriatic.
Fig. 4 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 4: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the Otranto Strait. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the Otranto Strait.
Fig. 1 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 1: SANI model bathymetry (A). The geographical area covered by SANI model (red rectangular) and the divided sub-regions SA (Southern Adiatic - yellow), OS (Otranto Strait - green), NI (Northern Ionian – brown) and SI (Southern Ionian – blue). All the available (966) Argo profiles for the period 2008-2012 from 21 individual floats denoted with different colours according to their WMO number (B).
Binary data file needed for the Shen et al. (2011) equation of state implemented in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is downloaded automatically from this repository on running Phantom **</p> <p>Contains information needed to load the <a href="http://adsabs.harvard.edu/abs/2011PhRvC..83c5802S">Shen, Horowitz & Teige (2011)</a> equation of state for nuclear matter in Phantom simulations</p> <p>The data file is a binary data file that enables a fast read of the information listed in the ascii tables given in the supplementary material of the Shen et al paper. The original ascii data files can be found here:</p> <p><a href="https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802">https://journals.aps.org/prc/supplemental/10.1103/PhysRevC.83.035802</a></p> <p>For information on how to read this file, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_shen.f90">eos_shen.f90</a>)</p>
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
Data files for the tabulated MESA equation of state in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<div> <div> <div> <p>** These tables are automatically downloaded from this repository when running phantom **<br><br>This tabulated equation of state in PHANTOM is adapted from the logPgas − Temperature equation of state tables provided with the open source package Modules for Experiments in Stellar Astrophysics MESA (Paxton et al. 2011). Details of the data, originally compiled from blends of equations of state from Saumon, Chabrier, & van Horn (1995) (SCVH), Timmes & Swesty (2000), Rogers & Nayfonov (2002, also the 2005 update), Potekhin & Chabrier (2010) and for an ideal gas, are outlined by Paxton et al. (2011).</p> <p>Code to read these tables is available as part of phantom (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_mesa_microphysics.f90">src/main/eos_mesa.f90</a>). The original version of these tables and the module to read them was contributed by Tom Constantino from the MUSIC code (<a href="https://ui.adsabs.harvard.edu/abs/2017A&A...600A...7Ga">Goffrey et al. 2017</a>), and the phantom implementation described in <a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. (2018)</a>.The current tables were created by Tom Reichardt for the paper Reichardt et al. (2020):<br><br><a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract">https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract</a></p> </div> </div> </div> <p>Figure 1 in Reichardt et al. (2020) shows the pressure, temperature, Gamma and P/Pideal shown as a function of internal energy and density from these tables</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.