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115 results for “data compression”
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>
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>
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>
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
Data for analysis in "Towards optimal cosmological parameter recovery from compressed bispectrum statistics"
<p>Measures of the three point function extracted from a suite of simulations using 4 different estimators: namely, the bispectrum, modal estimator, integrated bispectrum, line correlation function. Also supplied are power spectrum measures across the same simulations. <br> <br> The measures are done across 200 fiducial and 60 non-fiducial cosmology simulations, at 3 redshifts. Further details on what was done can be attained by reading the document, Overview.md/Overview.pdf, attached to the bundle. Even more details can be acquired by reading the paper this data was prepared for at https://arxiv.org/abs/1705.04392! </p>
Data Supplement for "Phase diagram of compressible and paired states in the quarter-filled Landau level"
<p><strong>Description:</strong> This dataset provides supplemental data for the paper <em>Arxiv 2408.08354</em>, specifically the Density Correlation Function (DCF) and Harmonic Coefficients <span><span>GkG_k</span><span><span><span><span>G</span><span><span><span><span><span><span>k</span></span></span><span></span></span></span></span></span></span></span></span> (Pol) for various fractional quantum Hall wave functions, as outlined in the paper.</p> <h3>Contents:</h3> <ul> <li><strong>Density Correlation Function (DCF)</strong>: Corresponds to Eq. (4) in the paper, capturing correlation behavior in fractional quantum Hall systems.</li> <li><strong>Harmonic Coefficients <span><span>Gk</span></span> (Pol)</strong>: Corresponds to Eq. (5), representing harmonics relevant to polarization effects.</li> </ul> <h3>File Structure:</h3> <p>The data files follow the structure:</p> <ul> <li><strong>DCF[WF_name, Ne, Nc]</strong> and <strong>Pol[WF_name, Ne, Nc]</strong>: <ul> <li><code>WF_name</code>: Identifier for the wave function (details below).</li> <li><code>Ne</code>: Number of particles.</li> <li><code>Nc</code>: Lowest Landau level projection cutoff used in Eq. (C5), if projection is needed.</li> </ul> </li> </ul> <h3>Data Format:</h3> <p>The data is formatted for Mathematica, with each entry stored as a comma-separated list enclosed in curly braces <code>{}</code>. Each function entry is structured as:</p> <ul> <li><strong>DCF[WF_name, Ne, Nc] = {value, ...}</strong> and <strong>Pol[WF_name, Ne, Nc] = {value, ...}</strong></li> </ul> <h3>Error Estimation:</h3> <p>For error assessment, each wave function's data is divided into 20 batches. Each batch is averaged separately:</p> <ul> <li><strong>DCFbatch[WF_name, Ne, Nc, id]</strong> and <strong>Polbatch[WF_name, Ne, Nc, id]</strong>: <ul> <li><code>id</code>: Batch number ranging from 1 to 20.</li> </ul> </li> </ul> <h3>Wave Function Naming Conventions:</h3> <p>The naming convention used in <strong>WF_name</strong> designates the state as follows:</p> <ul> <li> <p><strong>First Letter</strong>: Filling factor</p> <ul> <li><code>H</code>: Half-filled</li> <li><code>Q</code>: Quarter-filled</li> </ul> </li> <li> <p><strong>Second and Third Letters</strong>: Pairing channel (shift)</p> <ul> <li><code>AP</code>: Anti-Pfaffian (<span><span>l=−3</span></span>)</li> <li><code>PH</code>: PH-Pfaffian (<span><span>l=−1</span></span>)</li> <li><code>MR</code>: Moore-Read (<span><span>l=1</span></span>)</li> <li><code>FW</code>: f-wave (<span><span>l=3</span></span>)</li> <li><code>Q0</code>: CFL only (<span><span>l=0</span></span>)</li> </ul> </li> <li> <p><strong>Ending Type</strong>: Wave function type</p> <ul> <li><code>S</code>: Paired state with single-particle projection.</li> <li><code>CFL</code>: Composite Fermi liquid.</li> <li><code>E</code>: Exact wave function for Moore-Read.</li> </ul> </li> </ul> <p><strong>Examples:</strong></p> <ul> <li><strong>QAPS</strong>: Quarter-filled Anti-Pfaffian wave function.</li> <li><strong>HPHCFL</strong>: Half-filled Composite Fermi liquid at PH-Pfaffian shift.</li> <li><strong>QMRE</strong>: Quarter-filled Moore-Read exact wave function without expansion of pairing function.</li> </ul> <h3>Naming Exceptions:</h3> <ul> <li><code>La</code>: Laughlin state at <span><span>ν=1/3.</span></span></li> <li><code>QSU2</code>: SU(2)<span><span>2_2</span><span><span><span><span><span><span><span></span></span></span></span></span></span></span></span> wave function, Eq. (20) with + sign at <span><span>ν=1/4</span></span> (p=2).</li> <li><code>HASU2</code> and <code>QASU2</code>: SU(2)<span><span>2_</span><span><span><span><span><span><span><span><span><span>2</span></span></span><span></span></span></span></span></span></span></span></span> wave function, Eq. (20) with - sign at <span><span>ν=1/2</span></span> and <span><span>ν=1/4</span></span> (p=1 and 2).</li> </ul>
Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm. in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 1. Diplopanax cacaoides (ZENKER) comb. nov. a–d: [Holotype of Mastixia cantia E.REID et M.CHANDLER, V.22953]. a: Lateral view of longitudinally broken specimen, reflected light. b–d: Surface renderings from micro-CT data. b: Lateral view of longitudinal fracture surface. c: Same specimen rotated to show external surface. d: Enlargement of lower half from (a, b), reflected light. e, f: Specimen figured originally as a paratype of M. cantia, V.22954 (Reid and Chandler 1933: pl. 25, fig. 3), reflected light. e: Ventral view with much of the endocarp wall fallen away exposing smooth convex ventral surface of locule cast. f: Transversely fractured surface, showing thick wall of the endocarp, and dehiscence plane leading to the left limb of the locule. g: Transversely sectioned, laterally compressed specimen from Miocene of Wiesa, Germany for comparison, Senckenberg Museum, SM.B. 21034/I. h–j: Digital transverse sections from micro-CT data of the Holotype V.22953. h: Transverse fracture surface from (b), showing curved locule and zone of weakness defining the germination valve (arrow), reflected light. i: Same orientation with clear demarcation of the separation plane of the germination valve (arrow), digital section from micro-CT scan. j: Enlargement from (h). Scale bars 5 mm.
Crystal structures and hkl data associated with paper 'Metal-free negative linear compressibility (NLC) material - the cocrystal of 1,2-bis(4-pyridyl)ethane and fumaric acid'
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Data_MathyChekafCowan_JOC2018_Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression
<p>Original data files for the article Mathy, Fabien, Chekaf, Mustapha, & Cowan Nelson (2018). Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression. Journal of Cognition.</p> <p>Abstract : Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
Fig. 3 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 3. Resulting order after multiple sorting according to the columns typeStatus, country, state, city and locality, in that order. This sequence of column names corresponds to the variable Sorting Order in the Gridit software.
Fig. 2 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 2. Columns swapped according to the desired order for the information in the final text. The order of the column names (scientificName, typeStatus, sex, country, etc.) corresponds to the variable Display Order in the Gridit software.
Fig. 1 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 1. Spreadsheet data from some of the specimens of Distictus tibialis (Brullé, 1846) cited in Supeleto et al. (2019). Mandatory columns and column names in the Gridit software marked in red.
Fig. 5 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 5. Data from the spreadsheet in Fig. 4, excluding header and the column scientificName, copied and pasted into a text editor; resulting tabs (cells) replaced with comma. Sequences of "ib" and "?" in each row were grouped together with a preceding number (e.g., "11ib" in row 2) that indicates the total of subsequent repeats. Each row represents a unique collecting event. The final text generated from this file is shown in Table 2.
Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).
Effects of Charring on Squash (Cucurbita L ) Seed Morphology and Compression Strength: Implications for Paleoethnobotany Metric Data
<p>Metric data resulting from a series of charring experiments on seeds from three species of squash: <em>Cucurbita pepo</em>, <em>Cucurbita moschata</em>, and <em>Cucurbita maxima</em>.</p>
First results of online compression of HEP data using Baler
<p>Within the framework of the HiDA Trilateral Data Science Exchange Program, this internship project unveils preliminary findings on online compression using Baler. It involved the examination of various datasets of different sizes from the High Energy Physics (HEP) domain to evaluate compression performance. All datasets used are subsets of the jet data recorded by the CMS experiment at the LHC in 2012, released as open data under the Creative Commons CC0 waiver (see references). The data is modified (flattened, truncated, formatted, etc.) and packaged in a way that makes it easy for others to reproduce the results. The provided files in this page include comprehensive instructions for replicating the project's results, as well as datasets and outcomes. Below, you'll find a brief overview of the project's folder structure, categorized by the dataset utilized (small dataset/example CMS data/larger CMS data), the online/offline compression method, and resource utilization, particularly regarding GPU usage.</p> <p>Presentations summarizing this project's results can be found here: https://zenodo.org/record/8326707.</p> <p><strong>Project's folders:</strong></p> <ol> <li> <p><strong>Reproduction Instructions</strong>: This folder houses all files that offer detailed guidelines for replicating the project's presented results. These files serve as a reference for accessing relevant materials.</p> </li> <li> <p><strong>GPU with Example CMS Data.zip</strong>: This directory contains all files related to offline compression of the approximately 100MB example CMS dataset provided by Baler. GPU resources were employed in the model training process.</p> </li> <li> <p><strong>GPU with Larger CMS Data (1).zip</strong>: In this section, you'll find files associated with the compression of a larger CMS dataset, approximately 1.4GB in size. It includes results of offline compression and a split of the dataset into a 50/50 ratio for training and testing, with results provided for various epochs.</p> </li> <li> <p><strong>GPU with Larger CMS Data (2)</strong>: This folder holds the larger dataset, divided into two halves, with the first half's array values in one file and the second half's in another.</p> </li> <li> <p><strong>Offline/Online on Small Dataset</strong>: Here, you'll find files related to both offline and online compression of a small dataset, roughly 100KB in size, extracted from the example CMS dataset provided by Baler.</p> </li> <li> <p><strong>Modifications of Small Dataset</strong>: This section comprises variations of the small dataset, including both normalized and un-normalized datasets.</p> </li> <li> <p><strong>Materials</strong>: This folder includes fundamental papers and summaries to enhance your understanding of the project.</p> </li> <li> <p><strong>HiDA</strong>: Within this directory, you'll find a printed webpage from the HiDA program.</p> </li> </ol>
Data Accompanying "Fracturing process of concrete under uniaxial and triaxial compression: insights from in-situ x-ray mechanical tests"
<p>These are the datasets analysed in "Fracturing process of concrete under uniaxial and triaxial compression: insights from <em>in-situ</em> x-ray mechanical tests" by Stamati <em>et. al.</em> (submitted on June 2020 in Cement and Concrete Research)</p> <p>The datasets contain the reconstructed x-ray 3D images of selected tests and the corresponding DVC analysis.</p> <p>Note that details regarding the DVC analysis can be found in the paper.</p> <ul> <li>Each folder contains a separate test; uniaxial compression ("<em>C-02"</em>) and triaxial compression at 5MPa ("<em>TX5-01"</em>), 10MPa ("T<em>X10-01</em>") and 15MPa ("<em>TX15-01</em>") confining pressures.</li> <li>For each test, folder 01 contains the first scan, where 01 is the reference state for the DVC analyis. Folder 01 also contains the image of the labelled largest aggregates used for the discrete DVC. Folrders 02-XX contain the scans at intermediate loading steps and the corresponding DVC analysis; "<em>reg</em>" contains the registration result in downscaled 2-binning images. "<em>ddic</em>" contains the discrete DVC computation. "<em>ldic</em>" contains the local DVC computation, before and after the merge and filtering of the grid and discrete DVC fields. "<em>strains</em>" contains the strain fields coming from the corrected merged DVC field.</li> <li>Folder "<em>stressStrain</em>" contains the stress-strain curves measured during the tests after subtracting the displacement corresponding to the loading system.</li> <li>Folder "<em>doubleScanUniaxial</em>" contains the two reconstructed images of the "repeated scan" of the uniaxial tests, from which the DVC measurement uncertainties were evaluated.</li> </ul>
Quasi-static cyclic shear-compression tests on plastered rubble stone masonry walls - Experimental data
<p>This dataset contains quasi-static cyclic shear-compression tests on six large-scale rubble stone masonry walls plus two example point clouds created from two specimens.</p> <p> </p>
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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
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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.