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369 results for “droplet”
Data for the paper "Dispersion of particles in a sessile droplet evaporating on a heated substrate"
<p><span><span>This file contains the data generated for each case demonstrated in the paper titled "Dispersion of particles in a sessile droplet evaporating on a </span>heated substrate" <br>Authors: Aman Kumar Jain, Fabian Denner, and Berend van Wachem. <br><br>The repository contains the 7 folders for 7 cases performed in stage 2 of the simulation described in table 4 of the paper. Cases C1 and C2 involve a droplet on a substrate at Ts = 25 ◦ C, while C3 and C4 involve a substrate at Ts = 50 ◦C. Marangoni stresses are considered in the even-numbered cases and neglected in the odd-numbered ones. The case names ending with suffix S denotes the standard silica particles and the case names ending with suffix N denotes neutrally buoyant articles. <br></span></p> <p><span>Along with these folders a python script "ParticleCombined.py" is added which uses the particle position data in each case folder to calculate the particle surface density. <br><br>Each case folder contains: <br>The fluid fields, mesh, and particle information are stored in folders Fields, DMs, Meshes and Particles. <br>A .xmf wrapper file is provided to read the simulation results in Paraview. <br>The "results.xmf" file shows the fluid data such as velocity, pressure and liquid volume fraction. The liquid volume fraction value, alpha, tracks the interface of an evaporating sessile droplet. <br>The "results_DEM.xmf" shows the particle data such as the position, velocity and other data sets associated with the particles. <br><br>Each case folder contains five *.csv files which contain the information of particle position for 5-time instances and are processed using the python script "ParticleCombined.py". <br><br>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 452916560.<br></span></p>
Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer – Extended Data
<p>This dataset contains the Underlying Data to the paper “ Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer”.</p> <ul> <li> “deformation_size” folder containing scatter plots of σ with respect to GUVs rest radii <ul> <li>sd_deform_scatter_H1</li> <li>sd_deform_scatter_H2</li> <li>sd_deform_scatter_H3</li> </ul> </li> <li>“magnetic_device_support” folder containing the .stl files for 3D-printing the magnets-support of the magnetic device <ul> <li>magnetic_device_support_part1</li> <li>magnetic_device_support_part2</li> </ul> </li> <li>“size_distribution_magnetic” folder containing size distribution histograms comparing 100:0 DOPC:cholesterol and 60:40 DOPC:cholesterol samples, under the application of magnetic fields <ul> <li>sd_magnetic_size_dist_allfields</li> <li>sd_magnetic_size_dist_H1</li> <li>sd_magnetic_size_dist_H2</li> <li>sd_magnetic_size_dist_H3</li> </ul> </li> <li>“size_distribution_T0vsON” folder containing size distribution histograms comparing pristine samples (t<sub>0</sub>) and samples after overnight storage (ON), for different DOPC:cholesterol ratios <ul> <li>sd_size_dist_60_40</li> <li>sd_size_dist_71_29</li> <li>sd_size_dist_85_15</li> <li>sd_size_dist_100_0</li> </ul> </li> </ul>
Contact angle measurements of droplets on the mandibles of antlions
<p class="MsoNormal">Antlion larvae are fluid-feeding ambush predators that feed on arthropods trapped in their funnel-shaped pits built in sandy habitats; however, details are lacking about their feeding mechanism. Here we tested the hypothesis that the antlion, <em>Myrmeleon crudelis</em>, has adaptations that facilitate fluid feeding in sandy habitats. We measured contact angles of water droplets and used the capillary-rise technique to assess mouthpart wettability. A structural organization was discovered that provides a hydrophobic-hydrophilic wetting dichotomy that would simultaneously support self-cleaning and fluid uptake and is enabled by antiparallel movements of the maxillae. The mouthparts also are augmented by their material properties, including maxillae and mandible tips that are heavily sclerotized for piercing prey and mandibular teeth with resilin that would assist in preventing tooth breakage. Our findings provide insight on how antlion larvae have overcome the challenges of fluid feeding in sandy habitats, which likely contributed to their success and widespread distribution.</p>
Benchmark: Axisymmetric liquid droplets on viscoelastic substrates
<p>We report on the data from a numerical benchmark of a stationary axisymmetric droplet on viscoelastic Neo-Hookean substrates obtained using FEniCS. Numerical results using our Lagrangian phase-field approach are compared with results by Van Brummelen et al. and by Aland & Mokbel.</p>
Raw images corresponding to article "Relative assessment of cloth mask protection against ballistic droplets: a frugal approach"
<p>The images correspond to scans used to obtain the data reported on V. Márquez-Alvarez, J. Amigó-Vega, A. Rivera, A. J. Batista-Leyva, and E. Altshuler, <em>Relative assessment of cloth mask protection against ballistic droplets: A frugal approach</em>. PLOS ONE 17, e0275376 (2022). doi: <a href="https://doi.org/10.1371/journal.pone.0275376">10.1371/journal.pone.0275376</a>.</p>
Lipid droplet quantification datasheet of different MIGA2 constructs expressing cells
<p>Lipid droplet quantification datasheet of different MIGA2 constructs expressing Hela cells: WT, MIGA2 KO, MIGA2 KO cells transfected with WT MIGA2, MIGA2 KO cells transfected with MIGA2 mutants.</p>
Hairpin protein partitioning from the ER to Lipid Droplets involves major structural rearrangements
<div> <p>The project includes dataset from MD simulations and EPR measurements.</p> <p>Description of the MD simulation dataset:<br>-Data type: MD simulations of UBXD8 peptide at varying depths/conformations in POPC bilayer, POPC/Triolein:Cholesteryl oleate monolayer, and in Bilayer-Lipid droplet setup. <br>-Force fields: All-atom simulations were carried out using Charmm36 force field. The parameters for Triolein and Cholesteryl oleate are derived from Olarte et al., 2020 and were obtained from the corresponding authors of that publication. Coarse-grained simulations were carried out using Martini force field. <br>-Simulation Package: All simulations were carried out using GROMACS 2021 simulation package.<br>-File types: The uploaded files include structure files in PDB format, input parameter files (.mdp), topology (topol.top), and force field files.</p> </div> <div>Description of the EPR dataset:<br>- Data type: Experimental spectroscopic measurements, Easyspin simulation and analysis<br>- Files are with filename extensions: DSC, DAT<br>- Information on origin of the data:<br>- EPR spectroscopic measurements with filename extensions DSC and DTA.<br>- EPR spectroscopic simulation and analyses with filename extension m.<br>- EPR simulations were generated using Easyspin version 5.2.36 and Matlab version 23.2.0.2428915.<br>- X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with ER4123D cavity produced by Bruker.</div>
Effectiveness of a suction device for containment of pathogenic aerosols and droplets
<p>This repository contains the raw data used to plot the figures in the following article:</p> <p>Kai Lordly, Ahmet E. Karataş, Steve Lin, Karthi Umapathy, and Rohit Mohindra, Effectiveness of a suction device for containment of pathogenic aerosols and droplets, PLOS ONE, 2024.</p>
Scan with droplet fusion event
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Dataset for "Aerosol mixing state, new particle formation, and cloud droplet number concentration in an urban environment "
<p>Inverted HTDMA data, parcel model input, and parcel model output associated with the manuscript. HTDMA data are from the TRACER campaign and inverted using the LSQ1 or LSQ2 method described in Petters (2021). Model input is a 4 lognormal aerosol mode representation of the aerosol size distribution during TRACER, obtained by fitting measured size distributions obtained from mobility-based and optical-based sensors. Hygroscopicity is assigned for each mode based on observed size-resolved hygroscopicity from the HTDMA measurements. Model output refers to parcel model simulations using the pyrcel model (Rothenberg and Iompar).</p> <p>Petters, M. D.: Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data, Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, 2021. </p> <p>Rothenberg, D. and lompar: darothen/pyrcel: Pyrcel v1.3.2, , https://doi.org/10.5281/zenodo.8378595, 2023.563</p>
Crater diameter of granular polystyrene layer with various diameter of grains and Weber number of water droplet
<p>Datas about experiments of impacts made in 2021-2023. A water droplet impacting a polystyrene granular layer of 4 differents grains diameters. These datas give the crater diameter obtain in function of the Weber number of the droplet. A file from tomography of the granular layer before impact is added.</p>
Supplementary videos for the paper ``Actin Droplet Machine''
<p>The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, .i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the i-th electrode, there is a `1' in the i-th position of the string, and `0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers.</p>
Autooxidation of nitrous acid to nitric acid in supermicron aqueous droplets is acid accelerated
<p>This is the processed Raman data and calibration data for the experiments used in "Autooxidation of nitrous acid to nitric acid in supermicron aqueous droplets is acid accelerated" submitted to <em>Chemical Science</em> on Aug 2024</p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Fluorescent Calibration
<p>Fluorescent and darkfield images for the synergism fluorescent calibration droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 9)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 6)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 8)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 7)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 5)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Synergism Assay (Part 4)
<p>Fluorescent and darkfield images for the synergism droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </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.