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1,170 results for “Compression”
White Pine Girdling and Compression Experiment at Harvard Forest 2017-2019
Wood formation is a crucial process for carbon sequestration on land, yet how variations in phloem-transported carbon affect wood formation, respiration and nonstructural carbon pools remains poorly understood. To better understand the role of carbon supply on allocation to wood formation, we constrained phloem transport using girdling and compression around the stem of 40 mature white pines to monitor the effects of contrasting carbon supply (enriched above and reduced below the manipulations) on local wood formation and respiration, as well as on nonstructural carbon pools in needles, stems, and roots. This data set contains all data measured during the experiment. This includes wood anatomy, xylogenesis, stem respiration, nonstructural carbon measurements in coarse roots, stems and needles, as well as pre-dawn water potential measurements and pressure measurements from underneath the phloem compression collars. Furthermore, we provide allometric measurements for all trees. The code to process these data and reproduce our results is available in hf348-14-density-anomaly-analysis-code.zip. For more details pertaining to the methods see Rademacher et al. (2021).
Impacts of Phloem Chilling and Compression on Mature White Pine at Harvard Forest 2018
Wood formation is a crucial process for carbon sequestration on land, yet how variations in phloem-transported carbon and temperature affect wood formation, respiration and nonstructural carbon pools remains poorly understood. To better understand the role of carbon supply on allocation to wood formation, we constrained phloem transport using compression and chilling around the stem of 15 mature white pines to monitor the effects of contrasting carbon supply (enriched above and reduced below the manipulations) on local wood formation and respiration, as well as on nonstructural carbon pools in stems and roots. This data set contains all data measured during the experiment. This includes wood anatomical, xylogenetic, dendrochronological, stem CO2 efflux, and nonstructural carbon measurements in coarse roots and stems, as well as pre-dawn water potential measurements of needles and branches. Furthermore, we provide basic allometric measurements for all trees. The code to process these data and reproduce our results is available at https://github.com/TTRademacher/Exp2018Analysis. For more details pertaining to the methods see Rademacher et al. (2021) and contact the investigator.
Datasets to "Compressible test-field method and its application to shear dynamos"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Compressible test-field method and its application to shear dynamos" by M. J. Kapyla, M. Rheinhardt, & A. Brandenburg (Astrophys. J., in press, arXiv:2106.01107). If anything turns out to be incomplete, please email maarit.kapyla@aalto.fi or brandenb@nordita.org. </pre>
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"
<p>The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression".<br>Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs).</p> <p>For more details, please refer to the full paper.</p>
Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis
<p>This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.</p> <p>The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:<br> * id: unique identifier within each partition.<br> * tilted_label: image-level label corresponding to the tilted rectangle.<br> * nontilted_label: image-level label corresponding to the non-tilted rectangle.<br> * tilted_size: longest size of the tilted rectangle.<br> * nontilted_size: longest size of the non-tilted rectangle.<br> * kind: either "tile" or "mask" image type.</p> <p>The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".</p> <p>The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.</p>
Terrasar measurement data of "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing"
<p>This data set was used to test of the method described in "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing". It consists of the related Terrasar data and a MATLAB file to import the data into MATLAB.</p>
Shock Ramp Compressions Measurements of Iron on the Sandia National Laboratories' Z-Machine
<p>This data contains 1) the apparent velocity data from Velocity Interferometer System for Any Reflector (VISAR) data analyzed using the PointVISAR program for experiments Z3155 and Z3339 and 2) the equation of state results from analyzing the velocity data using a backward integration -- forward Lagrangian analysis.<br> These experiments were performed on the Sandia National Laboratories' Z-Machine, where the iron samples were dynamically compressed via shocked compression to approximately 275 Gpa and further ramp compression to approximately 400 GPa. This covers pressure-temperature regions near the melt line as well as the interior conditions of terrestrial planets.<br> The Z3155 data include four samples, each with two VISAR traces, and the Z3339 data include six samples, each with two or three VISAR traces.<br> The apparent velocity can be corrected to true velocity using the latest lithium fluoride window correction for a 532 nm wavelength.<br> PointVISAR is available as part of the Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox.<br> Details of the backward integration -- forward Lagrangian anaylsis that was used can be found in the related publication.</p> <p>Example data file interpretation: "Z3155_north_panel_bot_sample_01.txt" is the first VISAR trace from the bottom sample of the north panel on experiment Z3155.<br> "Z3155_EoS_combined.txt" is the sample-averaged Equation of State result from experiment Z3155.</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13961 O</p> <p> </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>
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>
Compressed COBS indexes (XZ) - HQ 661k - Part 1
<p><strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – <a href="https://doi.org/10.5281/zenodo.6845083">part1</a>, <a href="https://doi.org/10.5281/zenodo.6849657">part2</a></p> <p>Part 1 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>.</p> <p><strong>Citation:</strong></p> <blockquote> <p>K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996"><strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong></a>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong><a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a></strong></p> </blockquote>
Compressed COBS indexes (XZ) - HQ 661k - Part 2
<p><strong>Compressed COBS indexes (XZ) - HQ 661k - v1</strong> – <a href="https://doi.org/10.5281/zenodo.6845083">part1</a>, <a href="https://doi.org/10.5281/zenodo.6849657">part2</a></p> <p>Part 2 of the compressed COBS indexes built from the high-quality assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>.</p> <p><strong>Citation:</strong></p> <blockquote> <p>K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996"><strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong></a>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong><a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a></strong></p> </blockquote>
Oscillatory compression with different frequencies of 2D, dense, soft particle suspensions
<p>This repository contains 5 datasets of cyclically compressed hydrogel packings inside microfluidic channels, with different oscillation frequencies, observed using a microscope. This repository contains the raw data (images) as well as analyzed data of the particles tracked over time. The data format closely resembles information you might obtain from 2D DEM simulations, and could, therefore, be used to calibrate DEM simulations of the compaction of soft particles.</p> <p>The "Readme.md" file contains more in-depth information about the experimental setup, experiments and data structure.</p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Compression tests and direct shear test of two types of railway ballast
<p>This data set contains measurement data from uniaxial compression tests and direct shear tests<br> conducted on two types of railway ballast.<br> For a detailed description of the experiments see:</p> <p>B. Suhr, S. Marschnig and K. Six:<br> "Comparison of two different types of railway ballast in compression and direct shear tests:<br> experimental results and DEM model validation"<br> Granular Matter (2018)<br> Doi: 10.1007/s10035-018-0843-9</p> <p>For the uniaxial compression tests, measured normal forces and vertical paths are provided.<br> The direct shear tests are conducted directly afterwards, i.e. the information for<br> one compression and one shear test are contained in only one file.<br> For the shear tests shear paths, shear forces and vertical path are provided.</p> <p>At first the uniaxial compression test is carried out. At the end of this test, the normal load is equal to zero.<br> For the following direct shear test, the normal load is constant (according to the load specified in the file name)<br> and not recorded in the file.<br> The direct shear test starts, when the measured shear path is greater than zero.</p> <p>Check the README.txt file for more information. </p>
EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) compressed for NumPy
<p>A compressed NumPy version of the <a href="https://www.ngdc.noaa.gov/geomag/emag2.html">EMAG2 (v3)</a> global Earth Magnetic anomaly grid compiled from satellite, ship, and airborne magnetic measurements. The original CSV data was imported, transformed, and saved to a compressed NumPy archive as follows:</p> <pre><code class="language-python">import numpy as np mag_data = np.loadtxt('EMAG2_V3_20170530.csv', delimiter=',', usecols=(2,3,4,5,7)) lon_mask = mag_data[:,0] > 180.0 mag_data[lon_mask,0] -= 360.0 np.savez_compressed('EMAG2_V3_20170530.npz', data=mag_data.astype(np.float32))</code></pre> <p>The NumPy archive (contained in this repository) can be efficiently loaded in Python workflows. It contains the following columns:</p> <ol> <li>Longitude - geographic longitudinal coordinates in decimal degrees (WGS84)</li> <li>Latitude - geographic latitudinal coordinates in decimal degrees (WGS84)</li> <li>SeaLevel - magnetic anomaly value at sea level (nT)</li> <li>UpCont - magnetic anomaly value at continuous 4km altitude (nT)</li> <li>Error - Error estimate (nT)</li> </ol> <p>Code 888 is assigned in certain cells on grid edges where the data source is ambiguous and assigned an error of -888 nT.<br> Code 999 is assigned in cells where no data is reported with the anomaly value assigned 99999 nT and an error of -999 nT.</p> <p><strong>Reference</strong></p> <p>Brian Meyer, Richard Saltus, Arnaud Chulliat (2017): EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model. doi:10.7289/V5H70CVX</p>
Proof-of-Concept Measurement for "Radar Band Fusion Using Frame-Based Compressed Sensing"
<p>This data set was created for a proof-of-concept test of the method described in "Radar Band Fusion Using Frame-Based Compressed Sensing". It consists of a measurment against a metal plate.</p> <p> </p>
Mechanical Stretch Inhibition Sensitizes Proprioceptors to Compressive Stresses
<p>A repetitive gait cycle is an archetypical component within the behavioural repertoire of many if not all<br> animals including humans. It originates from mechanical feedback within proprioceptors to adjust the<br> motorprogram during locomotion and thus leads to a periodic orbit in a low dimensional space. Here,<br> we investigate the mechanics, molecules and neurons responsible for proprioception in Caenorhabditis<br> (C.) elegans to gain insight into how mechanosensation shapes the orbital trajectory to a well-defined<br> limit cycle. We used genome editing, force spectroscopy and multiscale modeling and found that<br> alternating tension and compression with the spectrin network of a single proprioceptor encodes<br> body posture and informs TRP-4/NOMPC and TWK-16/TREK2 homologs of mechanosensitive<br> ion channels during locomotion. In contrast to a widely accepted model of proprioceptive ‘stretch’<br> reception, we found that proprioceptors activated locally under compressive stresses in vivo and in<br> vitro, and propose that this property leads to compartmentalized activity within long axons delimited<br> by curvature-dependent mechanical stresses.</p>
Development and analysis of entropy stable no-slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows
<p>The database used in the submission of "Development and analysis of entropy stable no-slip wall boundary conditions implementation of the Eulerian model for viscous and heat conducting compressible flows."</p> <p>Abstract: Nonlinear entropy stability analysis is used to derive entropy stable no-slip wall boundary conditions for the Eulerian model proposed by Svärd ( <em>Physica A: Statistical Mechanics and its Applications, 2018 </em>). and its spatial discretization based on entropy stable collocated discontinuous Galerkin operators with the summation-by-parts property for unstructured grids. A set of viscous test cases of increasing complexity are simulated using both the Eulerian and the classic compressible Navier–Stokes models. The numerical results obtained with the two models are compared, and differences and similarities are then highlighted.</p>
Compressed COBS indexes (XZ) - Full 661k - Part 2
<p><strong>Compressed COBS indexes (XZ) - Full 661k - v0.0.2</strong> – <a href="https://doi.org/10.5281/zenodo.7313926">part1</a>, <a href="https://doi.org/10.5281/zenodo.7313942">part2</a>, <a href="https://doi.org/10.5281/zenodo.7315499">part3</a></p> <p>Part 2 of the compressed COBS indexes built from all assemblies of the 661k dataset. More information can be found on <a href="https://brinda.eu/mof/">https://brinda.eu/mof/</a>.</p> <p><strong>Citation:</strong></p> <blockquote> <p>K. Břinda, L. Lima, S. Pignotti, N. Quinones-Olvera, K. Salikhov, R. Chikhi, G. Kucherov, Z. Iqbal, and M. Baym, <a href="https://doi.org/10.1101/2023.04.15.536996"><strong>Efficient and robust search of microbial genomes via phylogenetic compression</strong></a>, <em>bioRxiv</em> 2023.04.15.536996, 2023. <strong><a href="https://doi.org/10.1101/2023.04.15.536996">https://doi.org/10.1101/2023.04.15.536996</a></strong></p> </blockquote>
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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)
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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.