Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,652
datasets available to search
ShareScore release 0.9.0
Dataset results
1,652 results for “particles”
Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane
<p><strong>Data set</strong> from Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane.<br> <br> - Publication : Cibaka-Ndaya C., O'Connor K., Opeyemi Idowu E., Parker M. A., Lebraud E., Lacomme S., Montero D., Sanz Camacho P., Veinot J. G.-C., Roiban I.-L., Drisko G. L.. Understanding the formation mechanisms of silicon particles from the thermal disproportionation of hydrogen silsesquioxane. <em>Chemistry of Materials</em>, In press, <a href="https://dx.doi.org/10.1021/acs.chemmater.3c01448">⟨10.1021/acs.chemmater.3c01448⟩</a>. <a href="https://hal.science/hal-04211253">⟨hal-04211253⟩</a><br> <br> <strong>Dataset production context : </strong> Crystalline silicon particles sustaining Mie resonances are readily obtained from the thermal processing of hydrogen silsesquioxane (HSQ). Here, the mechanisms involved in silicon particle formation and growth from HSQ are investigated through real time in situ analysis in an environmental transmission electron microscope and X-ray diffractometer. The nucleation of Si nanodomains are observed starting around 1000 °C. For the first time, a highly mobile intermediate phase is experimentally observed, thus demonstrating a previously unknown growth mechanism. At least two growth processes occur simultaneously: the coalescence of small particles into larger particles and a growth mode by particle displacement through the matrix toward the HSQ grain surface. Post-synthetic characterization by scanning electron microscopy further evidences the latter growth mechanism. The gaseous environment employed during synthesis impacts particle formation and growth under both in situ and ex situ conditions, impacting particle yield and structural homogeneity. Understanding the formation mechanisms of particles provides promising pathways for reducing the energy cost of this synthetic route.<br> <br> <br> <strong>For more information see the article.</strong></p>
Lake Tahoe Particle size distribution (PSD) Profile data
Profiles of particle size distribution (PSD) taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe particle size distribution (PSD) data for discrete water samples
Particle size distribution data measured on discrete water samples from Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Laboratory experiments testing pH, alkalinity and particle impacts on Mn removal
Laboratory experiments were conducted to investigate impacts of pH, alkalinity, and presence of particles on Mn removal in freshwater. The dataset includes monitoring data from: 1) a 14-day experiment in Mn(II) solutions in nanopure water, 2) a 24-hour experiment in Mn(II) solutions in nanopure water, and 3) a 10-day experiment in water from two drinking water reservoirs. The 14-day pH and alkalinity laboratory experiment was conducted starting October 30, 2022 and included sample collection and pH monitoring on day 0, 1, 4, 7, 10, and 14. The 24-hour pH and alkalinity laboratory experiment was conducted starting February 20, 2023 and included sample collection and pH monitoring at 0, 1, 2, 6, 12, and 24 hours. The reservoir water laboratory experiment was conducted starting March 22, 2023 and included sample collection and pH monitoring on day 0, 1, 4, 7, and 10. This experiment tested Mn removal in water collected from the lower water column of Falling Creek Reservoir (FCR) and Carvins Cove Reservoir (CCR), located in Vinton, Virginia, USA and Roanoke, Virginia, USA respectively. Both reservoirs are owned and operated by the Western Virginia Water Authority and are managed as drinking-water sources for the city of Roanoke, VA, USA.
Figure data for "Detection of tar brown carbon with the single particle soot photometer (SP2)"
<p>Data contained in Figures 2 and 4 of Corbin and Gysel-Beer 2019. https://doi.org/10.5194/acp-2019-568</p>
Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods
<p>Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHARP instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM<sub>2.5 </sub>values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM<sub>2.5</sub> values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM<sub>2.5</sub> monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.</p>
Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations
<p>This repository contains .npy files, loaded like:</p> <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> <p>which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations <a href="https://github.com/jmackereth/galactic-assembly-art.git">(explored in this repository)</a>.</p> <p>the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).</p>
Nano-sized calcium carbonate particles in cement mortars (DS18)
<p>This dataset will provide the selection of the optimal mix-design of cement mortars, optimizing the characteristics of nanoCaCO3 particles (additional percentages, morphology, particle size distribution, crystal phase) according to their use in cement-based composites. These commercial nanoparticles have characteristics comparable with those of the synthesized particles used up to now in the RECODE project.</p>
Field measurements of aerosol particles near a runway at Narita International Airport, Japan
<p>Field measurements of aerosol particles were conducted at an observation point ~180 m from the centerline of runway A (~140 m from the edge of the runway) at Narita International Airport (NRT), Japan, in February 2018. The online aerosol instruments used for the field measurements consisted of an ultrafine condensation particle counter (UCPC; Model 3776, TSI, d<sub>50</sub> = 2.5 nm), a condensation particle counter (CPC; Model 3771, TSI, d<sub>50</sub> = 10 nm), a scanning mobility particle sizer (SMPS; Model 3080, TSI), and an engine exhaust particle sizer (EEPS; TSI). The other instruments included a carbon dioxide (CO<sub>2</sub>) monitor (Model LI-840, Li-Cor Biosciences) and meteorological sensors. The sampling inlet for the UCPC, CPC, and SMPS was switched between an unheated (room temperature) mode and a 350°C heated mode every eight hours during selected time periods to measure the total and the non-volatile particles, respectively. The EEPS was operated independently from the UCPC/CPC/SMPS inlet system and it measured the unheated particle number size distributions during the entire period.</p>
Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)
<p>This folder contains the output of the following simulation: </p> <p>We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: <span class="math-tex">\(k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}\)</span>, where <span class="math-tex">\(C= 10^{-4/3}\)</span>. The normalization parameters are the speed of light <span class="math-tex">\(c = 1\)</span>, the vacuum permittivity <span class="math-tex">\(\epsilon_{0} = 1\)</span>, the magnetic permeability <span class="math-tex">\(\mu_{0} = 1\)</span>, the Boltzmann constant <span class="math-tex">\(k_{b}=1\)</span>, the elementary charge <span class="math-tex">\(q=1\)</span>, the ion mass <span class="math-tex">\(m_{i}=1\)</span>, the density of ions and electrons <span class="math-tex">\(n_{i}=n_{e}=1\)</span> and the ion inertial length <span class="math-tex">\(d_{i}=c/\omega_{pi}\)</span> where <span class="math-tex">\(\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}\)</span> is the ion plasma frequency. We set <span class="math-tex">\(\beta_{s,\parallel}=1\)</span> and <span class="math-tex">\(T_{s,\parallel}/T_{s,\perp}=1\)</span>, where <span class="math-tex">\(\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}\)</span> is the ratio between the plasma pressure parallel to the background magnetic field <span class="math-tex">\(\mathbf{B}_{0}\)</span> and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to <span class="math-tex">\(B_{0}=V_{A}/c\)</span>, where <span class="math-tex">\(V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}\)</span> is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of <span class="math-tex">\(m_{i}/m_{e} = 100\)</span> so that <span class="math-tex">\(d_e = 0.1 d_{i}\)</span> where <span class="math-tex">\(m_{e}\)</span> is the electron mass and <span class="math-tex">\(d_{e}\)</span> is the electron inertial length. The simulation box size is <span class="math-tex">\(L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}\)</span> and the spatial resolution is <span class="math-tex">\(\Delta x =\Delta y = \Delta z = 0.06d_{i}\)</span>. We use a time step <span class="math-tex">\(\Delta t =0.06/ \omega_{pi}\)</span>. In our normalisation, the Debye length <span class="math-tex">\(\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c\)</span> defines the minimum spatial distance that needs to be resolve in the simulation and <span class="math-tex">\(\lambda_D=0.07d_i\)</span>.</p> <p>This output corresponds to <span class="math-tex">\(t=120 \omega_{pi}\)</span>. </p> <p>These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project<br> dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".</p>
Data availability for "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles"
<p>Data availability for the paper titled "Photolytic Radical Persistence due to Anoxia in 1 Viscous Aerosol Particles" by Peter A. Alpert et al. This repository contains all data tables and files necessary to reproduce plots. Also included are open source ".hdf5” files that contain all data for X-ray microscopy images and ".dat" files having the raw data for mie resonance scattering to derive size change and mass loss. Please see the "Readme.pdf" file for more information.</p>
Spatial distributions of Solar Energetic Particle Fe ions at t=12 h
<p>These images are supplementary to Figure 2 of the publication entitled "Solar Energetic Particle drifts and the energy dependence of 1 AU charge states" by S. Dalla, M.S. Marsh, M. Battarbee, accepted by Astrophysical Journal (2016). The paper is also available at https://arxiv.org/abs/1610.05104.</p> <p>In each figure contour plots of locations of Fe ions of charges Q=20, 16, 12 and 8 at time t=12 hr are shown. The first image gives z vs r_xy projections and the second figure y vs x projections, with only particles within 20 degrees of the heliographic equator included in the x-y projections.</p>
Dataset for wave-by-wave particle tracking in the surf zone
<p>This dataset comprises 49 trajectories with 3D positions of buoyant tracers reconstructed from stereo camera imaging using two cameras and a standard triangulation process. The data is extracted from stereo image frames of the sea surface, captured at a rate of 30 frames per second. These images were collected between 15:13:00 and 17:18:59 UTC on September 7, 2019, near the island of Sylt, Germany.</p> <div>An appropriate coordinate system was used to better represent the tracer position time series for the analysis of tracers position, velocity and acceleration. </div> <div>Details about the coordinate system are provided in the associated manuscript and supporting information as well as in </div> <div><a title="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722" target="_blank" rel="noopener noreferrer">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL095722</a></div> <div> </div> <div>The dataset is organized into four columns: the frame time [µs]; the X coordinate [m], defining the horizontal position with the origin at the base of Pole 2 (see above referenced paper) and oriented shoreward; the Y coordinate [m], denoting the transverse position perpendicular to the direction of wave propagation ; </div> <div>and the Z coordinate [m], specifying the vertical position with the axis oriented upward.</div> <p>These coordinates were obtained using a triangulation algorithm and adjusted using a coordinate system transformation to yield a precise, physically meaningful representation of the trajectories. The data is provided in .mat (MATLAB) format</p>
Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows
<div> <div><span># Data repository for the paper</span></div> <br> <div><span># </span><span>_Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_</span></div> <br> <div><span>Corresponding author:</span></div> <div><span>Berend.van.Wachem@multiflow.org</span></div> <br> <div><span>This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:</span></div> <br> <div><span>-</span><span> .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper</span></div> <div><span> </span></div> <div><span>-</span><span> .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations</span></div> <br> <div><span>The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.</span></div> <br> <div><span>The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.</span></div> <br> <div><span>The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations. </span></div> <br> <div><span>The naming style of the raw data files and the subfolders for each section is explained in the following:</span></div> <br> <div><span>The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.</span></div> <br> <div><span>The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of</span></div> <div><span>the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:</span></div> <div><span>"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach</span></div> <div><span>number (column). </span></div> <br> <div><span>The python scripts have been tested with Python 3.11.5.</span></div> <br> <div><span>PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.</span></div> <br> <div><span>The python scripts in the directory Correlations/ are split in three main functions in two files:</span></div> <div><span>-</span><span> Getter.py (read the .csv files storing the coefficients - separate functions</span></div> <div><span> for the drag, lift and torque coefficients)</span></div> <div><span>-</span><span> ManuscriptCorrelation.py with all the correlations derived in this work for an</span></div> <div><span> effective implementation in any solver, and a plotting function to have visual</span></div> <div><span> representation of the correlations.</span></div> <div><span>-</span><span> generalmain.py (calls Getter and Plotter)</span></div> <br> <div><span>The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.</span></div> <div><span>First dimension : Reynolds number</span></div> <div><span>Second dimension : Orientation angle</span></div> <div><span>Third dimension : Mach number</span></div> <div><span>The user just needs to give the absolute path to the folder ResultsCoefficients/.</span></div> <br> <div><span>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.</span></div> </div>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</p>
Transparent Exopolymer Particles (TEP), Coomassie Stainable Particles (CSP) and accompanying variables in seawater of the NW Mediterranean
<p>Concentrations of Transparent Exopolymer Particles (TEP) , Coomassie Stainable Particles (CSP), nitrate, silicate, phosphate, chlorophyll a (chla), particulate organic carbon (POC) and nitrogen (PON), seawater temperature, salinity, water transparency, solar radiation, flow cytometry-determined abundances of Prochlorococcus, Synechococcus, picoeukaryotes, nanoeukaryotes, prokaryotic heterotrophs (PHA), and microscopy-detrrmined abundances of dinoflagellates, diatoms, coccolithophores and other microalgae in the NW Mediterranean.</p> <p>Sampling sites are the Blanes Bay Microbial Observatory (monthly sampling between 22/06/2015 and 10/10/2017) and the L'Estartit Oceanographic Station (monthly sampling between 25/06/2015 and 11/10/2017) in the coastal NW Mediterranean, plus the MIFASOL cruise (October 2015) onboard the RV Garcia del Cid in the NW Mediterranean.</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Simulated X-ray micro-computed tomography based particle tracking velocimetry dataset for validation purposes
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Validation dataset for micro-computed tomography based particle tracking velocimetry: a simulated micro-CT based velocimetry experiment with associated ground-truth particle trajectories</p> <p>- The ground truth trajectories were based on randomly dropping virtual particles in the pore space, and tracking their movement through a CFD-based velocity field (see below). The positions were calculated for the time corresponding to each radiograph of a micro-CT experiment. The folder "GroundTruthData" contains the locations of all particles at the central time of each micro-CT scan, as well as their radii. Check the associated readme file to read the data file.</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 7 time steps (70 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains an image of the pore space without particles, matching with the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 6 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based X-ray particle tracking velocimetry dataset in a porous glass filter
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a glass filter (ROBU P0; sample size 4 mm diameter by 1 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 59 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</p>
X-ray micro-computed tomography based particle tracking velocimetry dataset in a sandpack
<p>Authors: Tom Bultreys, Stefanie Van Offenwert, Wannes Goethals, Matthieu N. Boone, Jan Aelterman and Veerle Cnudde; Ghent University (Belgium)<br> Date: 8th February 2022<br> For any usage, please cite the accompanying publication: T. Bultreys, S. Van Offenwert, W. Goethals, M. N. Boone, J. Aelterman and V. Cnudde, "X-ray Tomographic Micro-Particle Velocimetry in Porous Media", Physics of Fluids, 34, 042008 (2022).<br> https://doi.org/10.1063/5.0088000<br> -----------------------------</p> <p>Dataset of a micro-computed tomography based particle tracking velocimetry experiment performed on a sand pack (grainsize 500-710 µm; sample size 4 mm diameter by 2 cm).</p> <p>- The main data is contained in the directory "TimeFrames", containing the reconstructed 3D images at 79 time steps (35 seconds interval), with a voxel size of 11.8 µm, in 3D .tif format. This can be opened in for example Fiji/ImageJ.</p> <p>- The directory "clearFrame" contains a high-quality pre-scan taken before the main experiment, which was registered and resampled to the time frame images, in the same format and with the same voxel size as the time frame images.</p> <p>- The directory "SegmentedImage" contains two binary 3D images (same format as images before) which was created by segmenting the clearFrame image. There are two versions: the original segmentation, and a version where pores were eroded. The eroded segmentation was used to mask the pore space during particle detection (this avoids spurious detections near pore walls, caused by minor mis-alignments of the clearImage).</p> <p>- The original segmentation was used as input to simulate the velocity fields in the directory "simulatedVelocityFields", which contains 3D .tif images that represent the three components of the velocity vector field (the X-direction was the axis of the sample, equaling the flow direction). There is also an input text file and an output text file. The simulation was performed with the code from single-phase OpenFOAM implementation from Ali Raeini and others at Imperial College London: http://www.imperial.ac.uk/earth-science/research/research-groups/perm/research/pore-scale-modelling/</p> <p>- The trackingOutput folder contains the experimentally determined velocity points (.csv, only particles that could be tracked at least 20 time frames) and the experimentally determined velocity magnitude field (.tif, voxel size 23.6 µm)</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.