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235 results for “Lattices”
Spatial Tournament Data on a Periodic Lattice Tournament Size 50 - MSc Dissertation
<p>A data set that contains the results of 1000 spatial games, for the iterated prisoner's dilemma. The spatial topology used is a periodic lattice network, and the tournament size set is 50.</p>
Behavior of Telecommunication Lattice Towers to Thunderstorm Winds
<h1>Dataset Description</h1> <p>This work aims at closing the knowledge gap between the wind field monitoring of real structures and wind tunnel testing by simulating real atmospheric boundary layer (ABL) and thunderstorm events in the Wind Energy, Environment, Engineering (WindEEE) research facility. The real events were acquired by a wind and structural monitoring system installed on a 50 m telecommunication lattice tower located in Sânnicolau Mare, Romania. The study reproduces complex downburst wind systems, in a controlled laboratory environment, like those observed in the field monitoring. The wind-induced response of two typical telecommunication lattice towers of different heights, i.e. 50 m and 90 m is investigated by means of both aerodynamic and aeroelastic tests. The acquired data will allow to compare and calibrate wind tunnel test results with field monitoring structural data measured during intense ABL and thunderstorm winds by the Sânnicolau Mare monitoring system. This extends the wind field and aerodynamic database which can be further utilized for codification purposes and for validating numerical and analytical models. The proposed work aims to advance code-based design of telecom lattice towers to thunderstorm winds.</p> <p>This work involved three areas of testing – wind field characterization to determine the best settings to match full scale / realistic wind loads, aeroelastic tests of both a 90m and 50m full towers (1:100 scale) using strain gauges as well as force balances, and a 1:10 sectional model of the top of the 50m tower to study the aerodynamics of the tower both with and without ancillaries added</p> <p> </p> <h2>S0. Documentation</h2> <p>Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.</p> <p> </p> <h2>S1. Wind Profile Stand</h2> <p>A vertical stand of 11 TFI cobra probes measured wind field data at heights of 50, 100, 150, 200, 300, 400, 500, 600, 700, 800, and 900 mm from the ground surface. For each experiment described below, the Cobra Probe stand was located in select locations to capture the near-surface flow.</p> <h3>E1. ABL Profile Development</h3> <p>The 60-fan wall located on one side of the hexagonal shaped WindEEE test chamber was used to generate the various ABL flows for this experiment. Each fan on this wall is individually controlled allowing a versatile range of ABL flow conditions. In addition, the ABL flow turbulence and boundary layer gradient were fine-tuned using roughness elements and spires.</p> <h3>E2. Downburst Profile Development</h3> <p>An impinging-jet style downburst is generated at the WindEEE dome through the release of pressure from a plenum above the testing chamber. The plenum is pressurized with six large fans for an adjustable amount of time or until a certain pressure is achieved. The built pressure then releases through a bell mouth with variable orifice sizes, D, to achieve a rapid downdraft of air. Given WindEEE’s unique 3-D test chamber, measurements were taken at various angles, theta, and radius, r, from the centre of the bell mouth. Commonly, these measurement locations are indicated by a non-dimensional parameter, r/D, and the angle, ϑ (theta).</p> <h3>E3. Combined Downburst and ABL Profile Development</h3> <p>With the unique capability of the WindEEE test chamber, profile measurements were taken while operating various combinations of the ABL and downburst-like flow configurations. The natural occurrence of a downburst in a storm acted as a driver for this segment of profile development.</p> <h3>E4. Downburst with Radial Trip Profile Development</h3> <p>For this test, wooden trips about 15cm tall were evenly placed all around the edge of the turntable. The downburst-like flow was generated similar to the downburst profile development section.</p> <p> </p> <h2>S2. 1-100 Scaled 50m Lattice Tower Model (T50)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <h3>E4. Downburst with Radial Trip Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 4.</p> <p> </p> <h2>S3. 1-100 Scaled 90m Lattice Tower Model (T90)</h2> <p>This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below.</p> <h3>E1. ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1.</p> <h3>E2. Downburst Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2.</p> <h3>E3. Combined Downburst and ABL Wind Load</h3> <p>The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.</p> <p> </p> <h2>S4. Aerodynamic lattice tower sectional model</h2> <p>This specimen included a 1 m tall section of the top of the 50 m tower at a scale of 1:10. It is constructed of brass, steel, 3D printed nylon, PolyJet 3-D printed material, and steel screws. The antennas, railing and central ladder are all removable. The model was mounted on a rig made up of a 12.7 cm diameter steel pipe and a wooden base plate. The rig stands 60cm tall so that the model is above the sheared surface flow. The base and top plates of the rig were 90cm in diameter. The experiments were performed with three different configurations of the model.</p> <h3>E1. Aerodynamics of the Bare structure without Top Plate</h3> <p>During this test, the model was measured as a bare structure (no antennas, ladders, or other components). The model was tested under ABL flow to outline the aerodynamic effects of the baseline model.</p> <h3>E2. Aerodynamics of the Structure with Top Plate</h3> <p>During this test, a top plate hovered over the model for the entirety of the test program. This plate encourages 2-D flow properties in ABL flow to mimic aerodynamic properties seen in horizontal testing in traditional wind tunnels.</p> <h3>E3. Aerodynamics of the Structure with Ancillary Components</h3> <p>During this test, ancillary components including ladders, railing, and antenna were attached to the model. These items act to increase the frontal area of the model which are expected to change the aerodynamic properties of the model.</p> <p> </p> <p><strong>Note:</strong> Given the number of data files captured in this program, the files required to be uploaded in compressed '.zip' folders.</p>
Pneumatic elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability
<p>Supplementary codes and data: Elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability</p>
Figures 64–72. Lattice microstructure comparison among Alayotityus sierramaestrae Armas, 1973 in A new monotypic genus and species from China Langxie feti gen et sp. n. (Scorpiones: Buthidae)
Figures 64–72. Lattice microstructure comparison among Alayotityus sierramaestrae Armas, 1973 (female, 64; photograph by G. Lowe), Langxie feti gen. et sp. n. (female, paratype, 65), Janalychas tricarinatus (Simon, 1884) (female, 66), Lychas scutilus C. L. Koch, 1845 (juvenile, 67), Lychas mucronatus (Fabricius, 1798) (female, 68, and male, 69), Tityus footei Chamberlin, 1916 (male, 70), Tityus stigmurus (Thorell, 1876) (female, 71) and Tityus smithii Pocock, 1893 (female, 72).
Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory---Data Release
<p>This release contains the analysis workflow used to prepare the publication <a href="https://arxiv.org/abs/2311.14663" target="_blank" rel="noopener">Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory</a>.</p> <p>A Python code for performing the analysis and generating the plots and tables is <a href="https://doi.org/10.5281/zenodo.10929539" target="_blank" rel="noopener">uploaded to Zenodo</a>. See the README therein for details on running the code.</p> <p>For details on the data formats, see the relevant README.md files.</p> <h2>Content of directories and files:</h2> <ul> <li>README.md: This contains general information on the content of the release.</li> <li>raw_data.zip: This compressed file contains all the raw data utilized in the research outlined in arXiv:2311.14663. These data were crucial in generating the results showcased in the paper.</li> <li><span>data.h5: An HDF5 file housing the correlators derived from the raw data through the processing code, <code>generate/transform_h5.py</code>.</span></li> <li><span>metadata.zip: This archive furnishes essential metadata such as ensemble information, fitting intervals, and smearing parameters crucial for extracting masses.</span></li> <li><span>F_meson.csv: Presents the fundamental meson masses extracted via the <code>analysis/analysis_F.py</code> script.</span></li> <li><span>AS_meson.csv: Presents the antisymmetric meson masses extracted via the <code>analysis/analysis_AS.py</code> script.</span></li> <li><span>CB_mass.csv: Presents the chimera baryon masses extracted via the <code>analysis/analysis_CB.py</code> script.</span></li> <li><span>FIT_mass.csv: Offers the AIC scan results conducted through the <code>analysis/analysis_AIC.py</code> script.</span></li> <li><span>FIT_cross_fixAS.csv and FIT_cross_fixF.csv: These files provide cross-check results computed by the</span> <span><code>analysis/analysis_cross.py</code> script, specifically for fixing antisymmetric and fundamental masses, respectively.</span></li> </ul>
Bogoliubov Excitations Driven by Thermal Lattice Phonons in a Quantum Fluid of Light
<p>This dataset corresponds to the all experimental data plotted in the article<br>"Bogoliubov Excitations Driven by Thermal Lattice Phonons in a Quantum Fluid of Light", Irénée Frérot, Amit Vashisht, Martina Morassi, Aristide Lemaître, Sylvain Ravets, Jacqueline Bloch, Anna Minguzzi, and Maxime Richard, Phys. Rev. X <strong>13</strong>, 041058, Published 26 December 2023.</p> <p>the data are provided as '.mat' Matlab data file. Each file is named according the figure and panel it referes to. For instance 'Fnj.mat' referes to the experimental data plotted in the panel j of Fig.n. The extension 'SI' refers to Figures in the supplemental information of the article.</p> <p>The files are structured into self explanatory fields: Fnj.x is the x axis data vector, and Fnj.dy are the error bars of the corresponding data points Fnj.y. Reasonable requests for additional explanations can be addressed to the article corresponding author. </p>
Polygons with small denominator containing a small number of lattice points
<p>The denominator of a rational polytope \(P\) is an integer \(r\) such that the dilated polytope \(rP\) has lattice point vertices. The size of a polytope is the number of lattice points it contains. This dataset contains polygons with denominator 2 and 3 with small size, classified using a growing algorithm as described in [HHK24].</p> <p>The data consists of files "denom_r_size_k_polygons.txt" which record the denominator \(r\) size \(k\) polygons \(P\). Each entry consists of the vertices and volume of \(rP\), the Ehrhart \(\delta\)-vector/\(h^*\)-vector of \(P\), and an ID number, which is unique among polygons of given size and denominator. Entries are ordered by their ID number. There are 50,564 entries in total.</p> <p><strong>Example entry:</strong></p> <p>ID=1<br>Vertices=[[ 1, 0 ], [ 0, 1 ], [ 3, 5 ]]<br>Volume=7<br>DeltaVec=[ 1, 0, 3, 7, 3, 0 ]</p> <p>If you make use of this data, please cite [HHK24] and the DOI for this data:</p> <p>doi:10.5281/zenodo.14230584</p> <p><strong>References:</strong></p> <p>[HHK24] Girtrude Hamm, Johannes Hofscheier, Alexander Kasprzyk, Classification and Ehrhart Theory of Denominator 2 Polygons. (preprint) arxiv:2411.19183</p>
Datasets for napari-lattice: Lattice Lightsheet Analysis
<p>Sample data acquired using the Zeiss lattice lightsheet microscope. The images are of human red blood cells acquired in the 488 channel. This dataset is associated with the software napari-lattice, which is a plugin for napari, an n-dimensional viewer in Python.</p> <p>Dimensions are in the format (Time, Channel, Z, Y, X)</p> <ul> <li>RBC_full_one_timepoint.czi: (1, 1, 834, 300, 2048)</li> <li>RBC_full_time_series.czi: (3, 1, 834, 300, 2048)</li> <li>RBC_medium_LLSZ.czi: (5, 1, 834, 297, 279)</li> <li>RBC_tiny.czi: (1, 1, 834, 118, 209)</li> </ul> <p>Voxel sizes in z, y and x respectively: ( 0.3, 0.1449922, 0.1449922)</p> <p>We have updated the dataset with pointspread functions (PSFs) for each channel. These are Zeiss simulated ones. There is a czi version and a tiff version. </p> <p>If you would like more PSFs, visit this comprehensive repo from EPFL: https://zenodo.org/records/14505724</p> <p> </p> <p>napari-lattice can process lattice lightsheet datasets from Zeiss Lattice lightsheet 7. We have updated the interface and API. Visit:</p> <p>https://github.com/BioimageAnalysisCoreWEHI/napari_lattice</p> <p> </p>
Data supplement for "Topological magnon band structure of emergent Landau levels in a skyrmion lattice"
<p>Collection of the data sets for our paper, <a href="https://doi.org/10.1126/science.abe4441"><em>Topological magnon band structure of emergent Landau levels in a skyrmion lattice</em></a>. (The source code supplement can be found <a href="https://doi.org/10.5281/zenodo.5718363">here</a>.)</p> <p> </p> <p><strong>Contents</strong></p> <table> <caption>Data files used for the paper's figures.</caption> <thead> <tr> <th scope="col">Scan</th> <th scope="col">Figure</th> <th scope="col">File(s)</th> </tr> </thead> <tbody> <tr> <td>(i)</td> <td>2</td> <td>ill_thales/exp_4-01-1621/rawdata/025280<br> ill_thales/exp_4-01-1621/rawdata/025281</td> </tr> <tr> <td>(ii)</td> <td>S17</td> <td>ill_thales/exp_INTER-436/rawdata/022169</td> </tr> <tr> <td>(iii)</td> <td>2</td> <td>ill_thales/exp_4-01-1597/rawdata/023454</td> </tr> <tr> <td>(iv)</td> <td>3</td> <td>mlz_reseda/*</td> </tr> <tr> <td>(v)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020778<br> ill_thales/exp_INTER-413/rawdata/020779</td> </tr> <tr> <td>(vi)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020777</td> </tr> <tr> <td>(vii)</td> <td>S16</td> <td>ill_thales/exp_INTER-436/rawdata/022168</td> </tr> <tr> <td>(viii)</td> <td>S16</td> <td>ill_thales/exp_INTER-413/rawdata/020793</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023488</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023489</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023453</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023553<br> ill_thales/exp_4-01-1597/rawdata/023559</td> </tr> <tr> <td> </td> <td>S12</td> <td>ill_thales/exp_INTER-436/rawdata/022213<br> ill_thales/exp_INTER-436/rawdata/022216<br> ill_thales/exp_INTER-436/rawdata/022217</td> </tr> </tbody> </table> <p> </p> <table> <caption>Overview of experimental data sets.</caption> <thead> <tr> <th scope="col">Instrument</th> <th scope="col">Proposal</th> <th scope="col">Directory</th> </tr> </thead> <tbody> <tr> <td><a href="http://doi.org/10.1080/10448632.2015.1057050">THALES (ILL)</a></td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-413">INTER-413</a></td> <td>ill_thales/exp_INTER-413/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-436">INTER-436</a></td> <td>ill_thales/exp_INTER-436/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1597">4-01-1597</a></td> <td>ill_thales/exp_4-01-1597/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-477">INTER-477</a></td> <td>ill_thales/exp_INTER-477/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1621">4-01-1621</a></td> <td>ill_thales/exp_4-01-1621/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2011.01.173">LET (RAL)</a></td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1620412">RB1620412</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1720033">RB1720033</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td><a href="https://www.psi.ch/en/sinq/tasp">TASP (PSI)</a></td> <td>20181324 (part 1)</td> <td>psi_tasp/exp_20181324_1/</td> </tr> <tr> <td> </td> <td>20181324 (part 2)</td> <td>psi_tasp/exp_20181324_2/</td> </tr> <tr> <td> </td> <td>20151888</td> <td>psi_tasp/exp_20151888/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2017.09.063">MIRA (MLZ)</a></td> <td>13511</td> <td>mlz_mira/exp_13511</td> </tr> <tr> <td> </td> <td>15633</td> <td>mlz_mira/exp_15633</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2019.05.056">RESEDA (MLZ)</a></td> <td>P00745-01</td> <td>mlz_reseda/</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank E. Villard and P. Chevalier for technical support and J. Locatelli for IT support during the <em>THALES</em> experiments; and J. Frank for technical support during the <em>MIRA</em> experiments. We thank J. K. Jochum for support with the <em>RESEDA</em> experiment. We thank M. Kugler for his early experiments on skyrmion dynamics in MnSi.</p> <p> </p> <p>► Please see the <strong>readme.txt</strong> file in the archive for details.</p> <p> </p>
Non-embeddability of standard contexts of lattices of integer partitions
<p>We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset provides evidence that the standard context of the partition lattice for <span>\(n = 9\)</span> cannot be embedded into the one for <span>\(n = 10\)</span>. Further context and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).</p>
Symmetric embeddings between standard contexts of lattices of integer partitions
<p>We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset contains all 29 symmetric context embeddings of the standard context of the lattice of partitions of the integer 8 into the standard context of the lattice of 10. Further information and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).</p>
MATLAB code for 'Bounds on quantum evolution complexity via lattice cryptography'
<p>We provide the MATLAB code and data to reproduce the numerical results presented in the paper https://arxiv.org/abs/2202.13924.</p>
Supplementary Data: Mapping of local lattice parameter ratios by projective Kikuchi pattern matching
<p>This is the experimental dataset which was analyzed in:</p> <p>"Mapping of local lattice parameter ratios by projective Kikuchi pattern matching"<br> Aimo Winkelmann, Gert Nolze, Grzegorz Cios, and Tomasz Tokarski<br> Phys. Rev. Materials <strong>2</strong> (2018) 123803<br> https://doi.org/10.1103/PhysRevMaterials.2.123803</p> <p>We describe a lattice-based crystallographic approximation for the analysis of distorted crystal structures via electron backscatter diffraction (EBSD) in the scanning electron microscope. EBSD patterns are closely linked to local lattice parameter ratios via Kikuchi bands that indicate geometrical lattice plane projections. Based on the transformation properties of points and lines in the real projective plane, we can obtain continuous estimations of the local lattice distortion based on projectively transformed Kikuchi diffraction simulations for a reference structure. By quantitative image matching to a projective transformation model of the lattice distortion in the full solid angle of possible scattering directions, we enforce a crystallographically consistent approximation in the fitting procedure of distorted simulations to the experimentally observed diffraction patterns. As an application example, we map the locally varying tetragonality in martensite grains of steel.</p>
Liquid film rupture beyond the thin-film equation: a multi-component lattice Boltzmann study - Dataset
<p>Dataset for the material presented in the article "Liquid film rupture beyond the thin-film equation: a multi-component lattice Boltzmann study"</p>
Ehrhart series coefficients for random lattice polytopes
<p><strong>Ehrhart series coefficients for random lattice polytopes</strong></p> <p>A dataset of Ehrhart data for 2918 randomly generated lattice polytopes, in dimensions 2 to 8.</p> <p>The polytopes used to generate this data were produced by the following algorithm:</p> <ol> <li>Fix <span class="math-tex">\(d\)</span> a positive integer in <span class="math-tex">\(\{2,\ldots,8\}\)</span>.</li> <li>Choose <span class="math-tex">\(d + k\)</span> lattice points <span class="math-tex">\(\{v_1,\ldots,v_{d+k}\}\)</span> uniformly at random in a box <span class="math-tex">\([-5,5]^d\)</span>, where <span class="math-tex">\(k\)</span> is chosen uniformly at random in <span class="math-tex">\(\{1,\ldots,5\}\)</span>.</li> <li>Set <span class="math-tex">\(P := \mathrm{conv}\{v_1,\ldots,v_{d+k}\}\)</span>. If <span class="math-tex">\(\mathrm{dim}(P)\neq d\)</span> then return to step 2.</li> </ol> <p>The final dataset has duplicate records removed. The data is distributed by dimension <span class="math-tex">\(d\)</span> as follows:</p> <table> <tbody> <tr> <th scope="row">d</th> <td>2</td> <td>3</td> <td>4</td> <td>5</td> <td>6</td> <td>7</td> <td>8</td> </tr> <tr> <th scope="row">#</th> <td>431</td> <td>787</td> <td>812</td> <td>399</td> <td>181</td> <td>195</td> <td>113</td> </tr> </tbody> </table> <p>For details, see the paper:</p> <p> <em>Machine Learning the Dimension of a Polytope</em>, Tom Coates, Johannes Hofscheier, and Alexander M. Kasprzyk, 2022.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p> doi:10.5281/zenodo.6614821</p> <p><strong>dimension.txt.gz</strong><br> The file "dimension.txt.gz" is a gzip-compressed plain text file containing key:value records with keys and values as described below, where each record is separated by a blank line. There are 2918 records in the file.</p> <p><strong>Example record</strong><br> ULID: 1FTU9VGPXXU82CTDGD6WYMBF9<br> Dimension: 3<br> Volume: 342<br> EhrhartDelta: [1,70,223,48]<br> Ehrhart: [1,74,513,...]<br> LogEhrhart: [0.000000000000000000000000000000,4.30406509320416975378532779249,6.24027584517076953419476314266,...]</p> <p>(The values for Ehrhart and LogEhrhart in the example have been truncated.)</p> <p>For each polytope <span class="math-tex">\(P\)</span> of dimension <span class="math-tex">\(d\)</span> we record the following keys and values in the dataset:</p> <p>ULID: A randomly generated string identifier for this record.<br> Dimension: A positive integer. The dimension <span class="math-tex">\(2 \leq d \leq 8\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> Volume: A positive integer. The lattice-normalised volume <span class="math-tex">\(\mathrm{Vol}(P)\)</span> of the polytope <span class="math-tex">\(P\)</span>.<br> EhrhartDelta: A sequence <span class="math-tex">\([1,a_1,a_2,\ldots,a_d]\)</span> of integers of length <span class="math-tex">\(d + 1\)</span>. This is the Ehrhart <span class="math-tex">\(\delta\)</span>-vector (or <span class="math-tex">\(h^*\)</span>-vector) of <span class="math-tex">\(P\)</span>. The Ehrhart series <span class="math-tex">\(\mathrm{Ehr}(P)\)</span> of <span class="math-tex">\(P\)</span> is given by the power-series expansion of <span class="math-tex">\((1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\)</span>. In particular, <span class="math-tex">\(\mathrm{Vol}(P) = 1 + a_1 + a_2 + \ldots + a_d\)</span>.<br> Ehrhart: A sequence <span class="math-tex">\([1,c_1,c_2,\ldots,c_{1100}]\)</span> of positive integers. The value <span class="math-tex">\(c_i\)</span> is equal to the number of lattice points in the <span class="math-tex">\(i\)</span>-th dilation of <span class="math-tex">\(P\)</span>, that is, <span class="math-tex">\(c_i = \#(iP \cap \mathbb{Z}^d)\)</span>. Equivalently, <span class="math-tex">\(c_i\)</span> is the coefficient of <span class="math-tex">\(t^i\)</span> in <span class="math-tex">\(\mathrm{Ehr}(P) = 1 + c_1t + c_2t^2 + \ldots = (1 + a_1t + a_2t^2 + \ldots + a_dt^d) / (1 - t)^{d+1}\)</span>.<br> LogEhrhart: A sequence <span class="math-tex">\([0,y_1,y_2,\ldots,y_{1100}]\)</span> of non-negative floating point numbers. Here <span class="math-tex">\(y_i := \log c_i\)</span></p>
Data for the article "Topological lattices realized in superconducting circuit optomechanics"
<p>Here you will find all the raw data and data processing scripts for the plots presented in "Topological lattices realized in superconducting circuit optomechanics".</p>
Figure data to "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice"
<p>This collection contains the data of the figures shown in the publication "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice" as txt files.</p> <p>The CST datasets "Fig1_Gap_CST.txt", "Fig1_Energy_CST.txt" and "Fig3_CST.txt" are already published in https://doi.org/10.5281/zenodo.7528316 and included here for the sake of completeness.</p>
Dataset: Lattice Semiconductor Corporation (LSCC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
The influence of lattice termination on the edge states of the quantum spin Hall insulator monolayer 1T'-WTe_2
<p>We study the influence of sample termination on the electronic properties of the novel quantum spin Hall insulator monolayer 1T'-WTe<sub>2</sub>. For this purpose, we construct an accurate, minimal 4-orbital tight-binding model with spin-orbit coupling by employing a combination of density-functional theory calculations, symmetry considerations, and fitting to experimental data. Based on this model, we compute energy bands and 2-terminal conductance spectra for various ribbon geometries with different terminations, with and without magnetic field.</p> <p>The provided repository contains all code and data to reproduce the results of this study.</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)
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.