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185 results for “Granular”
Permeability of granular mixtures under shear - videos for shear rate 213 s-1
<p>A granular column comprising Ballotini glass beads of diameters 250 μm, 125 μm, 90 μm and 63 μm is sheared at shear rate 213 s-1, while simultanouesly being fluidised with an increasing air flux rate. The data set includes videos of the granular column for each size fraction.</p>
Permeability of granular mixtures under shear - videos for zero shear rate
<p><strong>Videos of fluidisation experiments</strong></p> <p>A static granular column comprising Ballotini glass beads of diameters 250 μm, 125 μm, 90 μm and 63 μm is fluidised with an increasing air flux rate. The data set includes videos of the granular column for each size fraction.</p>
Permeability of granular mixtures under shear
<p><strong>Data set for fluidisation experiments</strong></p> <p>A granular column cmprising Ballotini glass beads of diameters 250 μm, 125 μm, 90 μm and 63 μm is sheared at a range of shear rates, γ˙ = 0, 16, 49, 115, 213 s−1, whilst simultaneously being fluidised with an increasing air flux rate. The data set includes the values of the pressure gradient across the granular column, Δp (Pa) and the corresponding air flux rate, Q (L min-1), for each size fraction and each shear rate.</p> <p> </p>
Raw data from "AC transport detection of magnetic transitions in small and granular samples"
<p>Raw data supporting the submitted manuscript "AC transport detection of magnetic transitions in small and granular samples". Additional information avaliable upon request.</p>
Supplemental Material for "Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults"
<p>Dataset and Constrained Parameters for "<strong><em>Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults</em></strong>" by Chang et al.</p> <p>This dataset comprises 64 rheological experiments, which have been encapsulated in four experimental groups: Full Height, Half Height, Half Height & Load, and Ethanol-Water. </p> <p>The experiments were conducted using a double-cylinder geometry on three-phase granular media to simulate the lateral faults of slow-moving landslides. The dataset includes Mechanical Data for time-evolution measurements of shear stress, fluid volume fraction under varying conditions, and Image Data for velocity fields derived from PIV (Particle Image Velocimetry) analysis.</p> <p>In addition, it provides constrained parameters, such as steady-state shear stress and the characteristics of the flow structure.</p> <p>Article information:</p> <div> <div>Chang, C., Ohno, K., Schulz, W. H., & Yamaguchi, T. (2025). Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults. <em>Geophysical Research Letters</em>, <em>52</em>(7), e2024GL113816. <a href="https://doi.org/10.1029/2024GL113816">https://doi.org/10.1029/2024GL113816</a></div> </div>
Thesis data: Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels
<p>Starting dataset used within the thesis; Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels.</p> <p>Results of manual annotation of and extract of nonPatchTaggedCommitLinks within the NVD CVE dataset.</p>
Experimental dataset on basal stresses and seismic signals generated by granular flows moving on a 3D-printed bumpy substrate
<p>This dataset provides the data supporting the experimental study of granular flows moving on a 3D-printed bumpy substrate considering the response of basal stresses and seismic signatures.</p> <p>S1_video clips_the side-view of the kinematic behaviors of the granular flows tracked by a high-speed camera.</p> <p>S2_data_stress and seismic signals measured at the instrumented plate</p> <p>S3_data_example of velocity fields downstream and normal to the base<br>S4_data_propagation features of the flow characterized by basal stresses and seismic signals<br>S5_data_variations of the depth-averaged velocity of the granular flows and corresponding nondimensionalized downslope velocity profiles<br>S6_data_profiles of the velocity of the granular flows normal to the base along with their depth-averaged velocities in the basal layers<br>S7_data_ nondimensionalized shear rates of the granular flows for various particle diameters <br>S8_data_depth-averaged shear rates and corresponding inertial numbers for granular flows with different particle sizes<br>S9_data_the mean normal stress and shear stress and stress fluctuations normal and tangential to the base<br>S10_data_characteristics of seismic signals in terms of peak amplitude, mean envelope, seismic deviation factor, and the signal mean frequency<br>S11_data_effective basal friction coefficient and equivalent friction coefficient as functions of particle diameter<br>S12_data_relationships between nondimensionalized normal stress fluctuations and nondimensionalized basal vertical velocity, inertial number, effective basal friction coefficient and equivalent friction coefficient of the flows.<br>S13_data_seismic deviation factor as functions of nondimensionalized normal stress fluctuations, inertial number, effective basal friction coefficient and equivalent friction coefficient</p> <p>S14_data_Comparison of effective basal friction coefficient μ_b and that scaled by the nondimensionalized basal vertical velocity.</p>
Tomography data for interparticle contact detection analysis in spheroidal granular packings
<p>This collection contains a series of synchrotron XCT scans on a hexagonal close-packed arrangement of soda-glass pellets. The field of view (FOV) diameter is 68.9 mm in diameter, approximately, and the nominal individual pellet diameter is 10 mm. The detector pixel size is 21 microns for all scans. The pellets were arranged in three horizontal lattices (layers). The middle and top lattices were separated by a layer of polyethylene film (cling film), while the bottom and middle layer were fully-contacting. Each file corresponds to a scan of either the bottom contacting or top non-contacting lattice pair. Thus, each filename includes a 'top' and 'bot' identifier. </p> <p>Acquisition parameters (number of projections, exposure time per projection, rotation range and sample position) were varied to achieve different image qualities and are included in 'README.txt'. All but scan A5 were local scans; scan A5 is a full-field scan acquired using the 'half-acquisition' method. </p> <p>Tomographic reconstruction was carried out using filtered back-projection in Savu. After reconstruction, a 3D median filter (kernel size = 2) and an anisotropic diffusion filter (diffusion threshold = 100; iterations = 2) were used to reduce noise.</p> <p>Data was acquired using Beamline I12-JEEP at Diamond Light Source (proposal NT26307-1).</p> <p>Please read README.txt</p> <p>Copyright 2021 Diamond Light Source Ltd. Licensed under the Apache License, Version 2.0.</p>
Appendix of "Impact of Change Granularity in Refactoring Detection"
<p>This is the dataset for ICPC 2022 Impact of Change Granularity in Refactoring Detection, which contains data about coarse-grained refactorings in 19 open source repositories.</p> <p>There are 19 csv files in this dataset. Each of the csv contains 8 rows:</p> <p>1. repository: repository name <br> 2. commit(s): commit SHA-1 hash<br> 3. detected_refactoring_type: refactoring type detected in that commit<br> 4. description: description for that refactoring<br> 5. leftSideLocations: refactoring start place<br> 6. rightSideLocations: refactoring end place<br> 7. is_effective: whether this refactoring is a coarse-grained refactoring (null for refactoring whose coarse-granularity is equal to 1)<br> 8. granularity: coarse-granularity of this refactoring</p> <p>Note that refactorings detected using RefactoringMiner(2.2) with invalid locations has been excluded.</p>
Granular piston-probing in microgravity: powder compression, from densification to jamming
<p>The datasets represents all data used in the article "Granular piston-probing in microgravity: powder compression, from densification to jamming", by Olfa D'Angelo, Anabelle Horb, Aidan Cowley, Matthias Sperl, and W. Till Kranz, published in npj Microgravity (2022).</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>
Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields
<p>Raw and processed data as well as a jupyter notebooks for the evaluation associated with the paper "Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields" (publicly available on arXiv: <a href="https://arxiv.org/abs/2403.10669">arXiv:2403.10669</a>) by N. Zapata et al. acquired and prepared at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.</p> <p>The notebooks show how to analyze the main results of the manuscript.</p> <p>For additional information please contact: nicolas.gonzalez2@kit.edu or ioan.pop@kit.edu.</p>
Discrete Element Model Simulations for a Granular Bed Undergoing Shear Deformation
<p>Discrete Element Model (DEM) Archive for,</p> <p>"Shear Variation at the Ice-Till Interface Changes the Spatial Distribution of Till Porosity and Meltwater Drainage"</p> <p>by,<br> Indraneel Kasmalkar, Anders Damsgaard, Liran Goren, Jenny Suckale</p> <p><br> This dataset contains the output files of the DEM simulations for understanding how the porosity of a granular bed evolves with an imposed laterally varying shear. The DEM simulations are performed using Sphere (<a href="https://src.adamsgaard.dk/sphere/">https://src.adamsgaard.dk/sphere/</a>), developed by Anders Damsgaard.</p> <p>The output files are in the 'output' folder.<br> The porosity values are curated and stored in 'porosities' folder as 4D numpy arrays.<br> The results are generated as .pdf files in the 'Images' folder.<br> The scripts are stored in the 'python' folder.</p> <p><br> The main file in the 'python' folder is MyVisualization.py.</p> <p>To run the script you will need Python 3.6 or newer, with the following packages:</p> <p>numpy, scipy, matplotlib, colorbrewer, math, pickle</p> <p>In MyVisualization.py, go to line<br> if __name__ == '__main__'</p> <p>Before the line, you can change the variable plot_type to get the plot you need.<br> The options for plot_type are listed in a comment right before the variable.<br> You can limit the plotting to either simple shear ('unif') or laterally varying shear ('hl') within each if condition thereafter</p>
Data from: Gardner-like crossover from variable to persistent force contacts in granular crystals
<p>We report experimental evidence of a Gardner-like crossover from variable to persistent force contacts in a two-dimensional, bidisperse granular crystal by analyzing the variability of both particle positions and force networks formed under uniaxial compression. Starting from densities just above the freezing transition, and for variable amounts of additional compression, we compare configurations to both their own initial state, and to an ensemble of equivalent, reinitialized states. This protocol shows that force contacts are largely undetermined when the density is below a Gardner-like crossover, after which they gradually transition to being persistent, being fully so only above the jamming point. We associate the disorder that underlies this effect to the size of the microscopic asperities of the photoelastic disks used, by analogy to other mechanisms that have been previously predicted theoretically.</p>
Controlling rheology via boundary conditions in dense granular flows
<p>Boundary shape, particularly roughness, strongly controls the amount of wall slip in dense granular flows. We aim to quantify and understand which aspects of a dense granular flow are controlled by the boundary conditions and to incorporate these observations into a cooperative nonlocal model characterizing slow granular flows. To examine the influence of boundary properties, we perform experiments on a quasi-2D annular shear cell with a rotating inner wall and a fixed outer wall; the latter is selected among 6 walls with various roughnesses, local concavity, and compliance. Measuring flow field and stress field throughout the material in experimental studies of granular materials is an ongoing challenge due to the complex nature of these materials. Here, we use innovative techniques to quantify stress field and flow field when different boundaries were used. We find that we can successfully capture the full flow profile using a single set of empirically determined model parameters, with only the wall slip velocity set by direct observation. Through the use of photoelastic particles, we observe how the internal stresses fluctuate more for rougher boundaries, corresponding to a lower wall slip, and connect this observation to the propagation of nonlocal effects originating from the wall. Our measurements indicate a universal relationship between dimensionless fluidity and velocity.</p>
Ultra-High Granularity Pixel Vertex Detector (PXD) signature Images
<p>Pixel Vertex Detector <strong>(PXD)</strong> is the innermost sub-detector of Belle II. The configuration of the PXD consists of 40 sensors within two detector layers. The inner layer has 16 sensors, and the outer layer comprises 24 sensors. Thus, each event includes 40 grey-scale images, each with a resolution of 250x768 pixels, resulting in more than <strong>7.5 million pixel channels per event</strong>. Each event in the dataset has the structure of <strong>[40,1,250,768]</strong> where the events are encoded by an event number distributed over 40 sub-directories corresponding to the 40 class labels.</p> <p>1.1.1/ --> sensor #1<br> ├── event_1<br> ├── event_2<br> ├── ...<br> 1.1.2/ --> sensor #2<br> ├── event_1<br> ├── event_2<br> ├── ...</p> <p>The samples are simulated synthetic background processes by GEANT4 via the <a href="https://link.springer.com/article/10.1007/s41781-018-0017-9">basf2</a> software. This data is used to train/test the <a href="https://arxiv.org/abs/2303.08046">IEA-GAN: Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning</a>.</p>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 1)
<p>The dataset consists of simulated events of electron-positron pairs (<em>e</em><sup>+</sup><em>e</em><sup>−</sup>) with flat transverse momentum <em>p</em><sub>T</sub> distribution <em>p</em><sub>T</sub> ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The <a href="https://cmsweb.cern.ch/das/request?input=dataset%3D%2FDoubleElectron_FlatPt-1To100%2FPhase2HLTTDRSummer20ReRECOMiniAOD-PU200_111X_mcRun4_realistic_T15_v1-v2%2FGEN-SIM-DIGI-RAW-MINIAOD&instance=prod/global">original dataset (CMS-internal)</a>.</p> <p>This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this <a href="https://twiki.cern.ch/twiki/bin/viewauth/CMS/HGCALTriggerPrimitivesSimulation">TWiki (CMS-internal)</a>.</p> <p>A description of each variable is below.</p> <table> <thead> <tr> <th>Variable</th> <th>Description</th> <th>Type</th> </tr> </thead> <tbody> <tr> <td><code>run</code></td> <td>Run number</td> <td><code>int</code></td> </tr> <tr> <td><code>event</code></td> <td>Event number</td> <td><code>int</code></td> </tr> <tr> <td><code>lumi</code></td> <td>Luminosity section</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_n</code></td> <td>Number of primary generated particles</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_PUNumInt</code></td> <td>Number of pileup interactions</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_TrueNumInt</code></td> <td>Number of true interactions</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_x</code></td> <td>Simulated primary vertex <em>x</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_y</code></td> <td>Simulated primary vertex <em>y</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_z</code></td> <td>Simulated primary vertex <em>z</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>gen_eta</code></td> <td>Primary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_phi</code></td> <td>Primary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_pt</code></td> <td>Primary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_energy</code></td> <td>Primary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_charge</code></td> <td>Initial generated particle charge</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_pdgid</code></td> <td>Primary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_status</code></td> <td>Primary generated particle generator status</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_daughters</code></td> <td>Primary generated particle daughters (empty)</td> <td><code>vector<vector<int>></code></td> </tr> <tr> <td><code>genpart_eta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_phi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pt</code></td> <td>Primary and secondary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_energy</code></td> <td>Primary and secondary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvz</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovy</code></td> <td>Primary and secondary generated particle original vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovz</code></td> <td>Primary and secondary generated particle original vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_mother</code></td> <td>Primary and secondary generated particle parent particle index (-1 indicates no parent)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_exphi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exeta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_fbrem</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pid</code></td> <td>Primary and secondary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_gen</code></td> <td>Index of associated primary generated particle</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_reachedEE</code></td> <td>Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_fromBeamPipe</code></td> <td>Deprecated variable, always true</td> <td><code>vector<bool></code></td> </tr> <tr> <td><code>genpart_posx</code></td> <td>Primary and secondary generated particle position <em>x</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posy</code></td> <td>Primary and secondary generated particle position <em>y</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posz</code></td> <td>Primary and secondary generated particle position <em>z</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>ts_n</code></td> <td>Number of trigger sums</td> <td><code>int</code></td> </tr> <tr> <td><code>ts_id</code></td> <td>Trigger sum ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_subdet</code></td> <td>Trigger sum subdetector</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_zside</code></td> <td>Trigger sum endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_layer</code></td> <td>Trigger sum layer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafer</code></td> <td>Trigger sum wafer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafertype</code></td> <td>Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_data</code></td> <td>Trigger sum ADC value</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_pt</code></td> <td>Trigger sum transverse momentum in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_mipPt</code></td> <td>Trigger sum energy in units of transverse MIP</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_energy</code></td> <td>Trigger sum energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_eta</code></td> <td>Trigger sum pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_phi</code></td> <td>Trigger sum azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_x</code></td> <td>Trigger sum <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_y</code></td> <td>Trigger sum <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_z</code></td> <td>Trigger sum <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_n</code></td> <td>Number of trigger cells</td> <td><code>int</code></td> </tr> <tr> <td><code>tc_id</code></td> <td>Trigger cell unique ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_subdet</code></td> <td>Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_zside</code></td> <td>Trigger cell endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_layer</code></td> <td>Trigger cell layer number</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferu</code></td> <td>Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferv</code></td> <td>Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_wafertype</code></td> <td>Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon)</td> <td> </td> </tr> <tr> <td><code>tc_cellu</code></td> <td>Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_cellv</code></td> <td>Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_data</code></td> <td>Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_uncompressedCharge</code></td> <td>Trigger cell ADC data at full precision before compression</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_compressedCharge</code></td> <td>Trigger cell ADC data compressed into 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_pt</code></td> <td>Trigger cell transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_mipPt</code></td> <td>Trigger cell energy in units of transverse MIPs</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_energy</code></td> <td>Trigger cell energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_simenergy</code></td> <td>Trigger cell energy from simulated particles in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_eta</code></td> <td>Trigger cell pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_phi</code></td> <td>Trigger cell azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_x</code></td> <td>Trigger cell <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_y</code></td> <td>Trigger cell <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_z</code></td> <td>Trigger cell <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_cluster_id</code></td> <td>ID of the 2D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_id</code></td> <td>ID of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_pt</code></td> <td>Transverse momentum <em>p</em><sub>T</sub> in GeV of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<float></code></td> </tr> </tbody> </table>
Scaled laboratory experiments of analogue magma intrusion in granular material: X-ray Computed Tomography imagery and displacement data
<p>This data set contains X-ray Computed Tomography (CT) images and surface displacement data of 15 scaled laboratory experiments of analogue magma intrusion in granular material. The experimental methodology and the experimental results were described in detail by Poppe et al. (2019). Displacement data of experiment SPCTIN14 was used by Poppe et al. (2023).<br> When using the experimental imagery or their derivatives please reference at a minimum Poppe et al. (2019) and this data set (Poppe et al., 2023, Zenodo data set).<br> The included explanatory notice reproduces the experimental method and presents the structure and file types contained in this data set.</p>
Effect of Cyclosporine Therapy on Gene Expression in Patients With Large Granular Lymphocyte Leukemia
ClinicalTrials.gov study NCT00363779. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Alemtuzumab (Campath) to Treat T-Large Granular Lymphocyte Leukemia
ClinicalTrials.gov study NCT00345345. IPD Sharing: Not stated. Countries: 1. Publications: 4.
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.