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2,712 results for “fluid”
Metaproteomic Analysis of Sarracenia Purpurea Pitcher Fluid at Harvard Forest 2012-2017
Aquatic ecosystem enrichment can lead to distinct and irreversible changes to undesirable states. Understanding changes in active microbial community function and composition following organic-matter loading in enriched ecosystems can help identify biomarkers of such state changes. In a field experiment, we enriched replicate aquatic ecosystems in the pitchers of the northern pitcher plant, Sarracenia purpurea. Shotgun metaproteomics using a custom metagenomic database identified proteins, molecular pathways, and contributing microbial taxa that differentiated control ecosystems from those that were enriched. The number of microbial taxa contributing to protein expression was comparable between treatments; however, taxonomic evenness was higher in controls. Functionally active bacterial composition differed significantly among treatments and was more divergent in control pitchers than enriched pitchers. Aerobic and facultative anaerobic bacteria contributed most to identified proteins in control and enriched ecosystems, respectively. The molecular pathways and contributing taxa in enriched pitcher ecosystems were similar to those found in larger enriched aquatic ecosystems and are consistent with microbial processes occurring at the base of detrital food webs. Detectable differences between protein profiles of enriched and control ecosystems suggest that a time series of environmental proteomics data may identify protein biomarkers of impending state changes to enriched states.
Individual Differences in Fluid Reasoning and RAPM-like Problem Solving
Open the record for dataset details and reuse information.
Changing Brine Inputs into Hydrothermal Fluids: Southern Cleft Segment, Juan de Fuca Ridge
<p>In 2016 temperature recorders were recovered, temperatures were measured, and fluid samples were collected from Vent 1, a high temperature (338°C) hydrothermal discharge site on the southern Cleft Segment of the Juan de Fuca Ridge. Coupled with previous sampling efforts, this collection represents a 32-year record of discharge from a single chimney structure, the longest record to date. Remarkably, the fluid has remained brine-dominated for more than three decades. This brine formed during phase separation and segregation prior to initial observations in 1984. Although the chloride concentration of the discharging fluid has decreased with time, the fluid temperature has remained nearly constant for at least 3.3 years and probably for 15 or even 22 years. Compositions of the discharging fluids are consistent with inputs from a deep-sourced brine, which was last equilibrated at >400° C at a depth consistent with the base of the sheeted dikes and the brittle-ductile transition. This brine mixed (diffusion or dispersion) with a likely non-phase-separated, hydrothermal fluid prior to discharge. A survey of hydrothermal endmember fluids with chlorinities in excess of 700 mmol/kg shows, with the exception of Fe, a single trend between major ion concentrations and chlorinity even though data are from a range of crustal compositions, spreading rates, and water and magma depths. Calculated deep-sourced brines from hydrothermal fluid data are similar to data based on fluid inclusions and estimates of brine assimilation in magmas. A better understanding of brines is required given their potential duration of discharge and capacity for mobilizing metals.</p> <p>The data in the attached 10 tables represent the supplemental data in a paper published in <em>Geochemistry, Geophysics, Geosystems</em>. The data include temperature data from long-term records, chemical data from hydrothermal effluent from Vent 1 on the Cleft Segment, sediment data, and sulfide chimney data.</p>
Mechanical data of rotary shear fluid pressurised experiments for the manuscript: "Fluid pressurisation and earthquake propagation in the Hikurangi subduction zone"
<p>Mechanical data of rotary shear fluid pressurised experiments.</p> <p>Tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Downstream Pore Pressure: Pressure_ds (MPa)</li> <li>Confining Pressure: Pressure_conf (MPa)</li> <li>Upstream pore pressure: Pressure_us (MPa)</li> <li>Temperature of the upstream boundary of the gouge layer: Temperature_us (°C)</li> <li>Thickness of the gouge layer: Thickness (mm).</li> </ul>
Videos of fluid flow in contact interfaces
<p>These videos demonstrate the capabilities of the computational framework presented in [1] to solve complex coupled problem of viscous thin fluid flow in contact interfaces while handling the possibility of the fluid to be trapped in pockets surrounded by contact zones.</p> <p>[1] Andrei G. Shvarts, Julien Vignollet, Vladislav A. Yastrebov "Computational framework for monolithic coupling for thin fluid flow in contact interfaces" https://arxiv.org/abs/1912.11292v3</p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Simulations of Rising Air Bubbles in Viscoelastic Fluids
<p>Videos, plots and the underlying data, generated from simulations of rising air bubbles in viscoelastic fluids for varying relaxation times and bubble volumes. The underlying model for the viscoelastic fluid is the Giesekus model, which was discretized with the fully implicit log-conformation approach. The free surface is modeled using an interface-tracking method. For more details cf. Knechtges, P. <em>Simulation of Viscoelastic Free-Surface Flows</em> PhD thesis (RWTH Aachen, 2018).</p>
Graphic Illustration of Verity Mathis' Talk: Virome composition in fresh bat guano, frozen and fluid-preserved bat tissues
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Verity Mathis at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Data from: Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space
<p>This data accompanies the paper entitled <strong>Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space.</strong></p> <p> </p> <p>The zip archive contains the results of Lattice Boltzmann Molecular Dynamics simulations of the systems investigated and presented in the manuscript. Computational Fluid Dynamics data are in VTK format. Molecular Dynamics trajectories are in XYZ format.</p>
Data for Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX
<p>Dataset for</p> <p>M Kloewer, S Hatfield, M Croci, PD Dueben and TN Palmer, 2021. Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16, in review.</p> <p>This dataset contains data from simulations with <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> with varying number formats and with or without a compensated time integration. All other parameters are shared between simulations. All .tar.gz are packed folders of the same name that contain netCDF files presenting velocities u,v, sea surface height eta, and tracer sst (sea surface temperature)</p> <ul> <li>run0002. Float16 simulation with compensated summation in the time integration.</li> <li>run0003. Float16 simulation without compensated summation in the time integration.</li> <li>run0004. Float64 reference simulation (without compensated summation in the time integration).</li> <li>run0005. Float16/32 mixed-precision simulation. No compensated time integration.</li> </ul> <p>Additionally, parameter.txt in each run summarizes all model parameters and progress.txt was created to monitor the progress of the data output during simulation. The file benchmarking.jld2 stores data for the benchmarking of the different runs as Julia's <a href="https://github.com/JuliaIO/JLD2.jl">JLD2 format</a> (a subset of HDF5).</p> <p>This dataset was created using the Isambard UK National Tier-2 HPC Service operated by GW4 and the UK Met Office, and funded by the Engineering and Physical Sciences Research Council EPSRC.</p> <p>For more details, see <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> or the preprint <a href="http://doi.org/10.1002/essoar.10507472.2">Kloewer et al, 2021</a>.</p>
Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics
<p>This folder contains the Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics presented in Al Jahdali et al., "Optimized explicit Runge--Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics," Computers & Mathematics with Applications, 2022.</p> <p>Specifically,</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_ADV.txt">Butcher_coefficients_ADV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the 2D advection equation.</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_IEV.txt">Butcher_coefficients_IEV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the isentropic vortex propagation for the compressible Euler equations.</p> <p> </p> <p> </p> <p> </p>
Fluid Drag Reduction by Magnetic Confinement
<p>Drag reduction of viscous liquid (Honey) with ferrofluid APG314.</p> <p>Friction factors and Reynolds number.</p> <p>Numerical simulations</p> <p>Microfluidics drag reduction</p> <p> </p>
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"
<p>This is a dataset for the article "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891 (Full citation data will be added upon final publication of the article.)</p> <p>This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.</p> <p> </p> <p> </p>
Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review - Metadata
<p>This file is the metadata related to the publication "Quantification of ADHD Medication in Biological Fluids with Liquid Chromatography: A Comprehensive Review".</p>
Dataset for the evaluation of Supercritical Fluid Chromatography Polar Stationary Phases with OH moieties
<p>Raw data used for the evaluation of supercritical fluid chromatography stationary phases with OH moieties published in article "Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with OH moieties" in Analytical Chemistry, 2024. Data set contains: (i) chromatograms of 107 analytes measured on silica, hybrid silica, and diol column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet). </p>
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
ScienceDex guides
Understand access before you commit
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.