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695 results for “topologies”
Dataset from 'Topological interfaces crossed by defects and textures of continuous and discrete point group symmetries in spin-2 Bose-Einstein condensates'
<p>Dataset associated with the publication 'Topological interfaces crossed by defects and textures of continuous and discrete<br>point group symmetries in spin-2 Bose-Einstein condensates' in Physical Review Research. <br>Source data for Figures 3-8 in the manuscript.</p>
Topological superconductivity in twisted bilayer WSe2: single band t-J model
<p>Dataset of results related with the theoretical analysis of topological unconventional superconducting state within the t-J model as applied to the description of the twisted bilayer WSe2. The code in c++ which was used to produce the data is also provided. This data set is a result of research which was founded by National Science Centre, Poland (NCN) according to decision 2021/42/E/ST3/00128. </p>
Data from: Topological nano-switches in higher-order topological insulators
<p>This repository provides data underlying the figures in the manuscript titled 'Topological nano-switches in higher-order topological insulators'.</p> <p>The data is given in CSV-format and the data files are named after their respective figures. The data corresponding to figure 2 is provided separately for the four field orientations in each panel of the figure, and contains the probability density on each site of the discretized system as a 2D array. Similarly, the data for figure C1 is provided separately. The other files contain the scattering matrix elements in columns labeled 'ij', with each row corresponding to an orientation of the magnetic field in the range [0, 2π] listed in the column labeled 'theta'. </p> <p>The data was generated using Kwant, a free (open source), powerful, and easy to use Python package for numerical calculations on tight-binding models. An example of how the studied systems were constructed is included in 'higher_order_ti_nano_switches.py', and contains the code to calculate the corresponding scattering matricies for a chosen set of parameters. The probability density included in the data related to figure 2 can similarly be extracted from the discretized system. See '<a href="https://kwant-project.org/">kwant-project.org</a>' for additional details.</p>
X-ray linear dichroic tomography of crystallographic and topological defects
<p>Open Data for "X-ray linear dichroic tomography of crystallographic and topological defects" published in <a href="https://www.nature.com/articles/s41586-024-08233-y">Nature <strong>636</strong>, 354 (2024) </a></p> <div> </div> <div>Full citation:</div> <div>A. Apseros, V. Scagnoli, M. Holler, M. Guizar-Sicairos, Z. Gao, C. Appel, L. J. Heyderman, C. Donnelly & J. Ihli </div> <div>X-ray linear dichroic tomography of crystallographic and topological defects.</div> <div><em>Nature <strong>636</strong>, 354</em> (2024).</div> <div>https://www.nature.com/articles/s41586-024-08233-y</div>
Topological Liquid Crystal Superstructures as Structured Light Lasers
<p>Data associated with the paper titled "Topological Liquid Crystal Superstructures as Structured Light Lasers".</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>
Diverse Topologies for Evaluation of Geometric Similarity Metrics
<p>A collection of 7 datasets with each set containing 3D shapes with varying topological complexity. The datasets can be used to compare different metrics of geometric dissimilarity. Two of the datasets have topologically complex shapes that resemble designs obtained from topology optimization, a widely used design optimization method for engineering structures.</p> <p>We used this dataset for a related journal article with the following abstract: "In the early stages of engineering design, multitudes of feasible designs can be generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Data mining such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representatives for review. Design similarity can be defined not only on a purely functional level but also based on geometric properties, such as size, shape, and topology. While metrics such as chamfer distance measure the geometrical differences intuitively, it is more useful for design exploration to use metrics based on <em>geometric features</em>, which are extracted from high-dimensional 3D geometric data using dimensionality reduction techniques. If the Euclidean distance in the <em>geometric features</em> is meaningful, the features can be combined with performance attributes resulting in an aggregate feature vector that can potentially be useful in design exploration based on both geometry and performance. We propose a novel approach to evaluate such derived metrics by measuring their similarity with the metrics commonly used in 3D object classification. Furthermore, we measure clustering accuracy, which is a state-of-the-art unsupervised approach to evaluate metrics. For this purpose, we use a labeled, synthetic dataset with topologically complex designs. From our results, we conclude that Pointcloud Autoencoder is promising in encoding geometric features and developing a comprehensive design exploration method."</p> <p>For each dataset, shapes/designs are saved as surface mesh files (extension: stl) and point cloud files (extension: ply) in the folders "stls" and "plys" respectively. A brief description of the 7 different datasets is in the following table. For each dataset, the designs are named using numbers starting from 0, e.g., “0.stl, 1.stl, …, 19.stl” in the folder for the surface mesh files. Some of the datasets are labeled, i.e., each design belongs to a class. In a labeled dataset, all classes have the same number of designs, and the designs are named in the order of their class. For example, a labeled dataset with 4 designs and 2 classes contains files whose names start with {0, 1, 2, 3} where the designs {0, 1} belong to class 1, and {2, 3} belong to class 2.</p> <table> <thead> <tr> <th scope="col">Dataset name</th> <th scope="col">Directory name</th> <th scope="col">Number of designs</th> <th scope="col">Number of classes</th> </tr> </thead> <tbody> <tr> <td>Beam-rotation</td> <td>"rotate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-elongation</td> <td>"elongate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-translation</td> <td>"move_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Three cube trusses</td> <td>"three_cube_truss"</td> <td>150</td> <td>6</td> </tr> <tr> <td>Single cube trusses</td> <td>"single_cube_truss"</td> <td>275</td> <td>11</td> </tr> <tr> <td>Random topologies</td> <td>"three_cube_truss_random"</td> <td>1000</td> <td>50</td> </tr> <tr> <td>Topologically optimized designs</td> <td>"cube_opt_shapes"</td> <td>1500</td> <td>None</td> </tr> </tbody> </table>
Topological states in superlattices of HgTe class of materials for engineering three-dimensional flat bands
<p>In search of materials with three-dimensional flat band dispersions, using ab-initio computations we investigate how topological phases evolve as a function of hydrostatic pressure and uniaxial strain in two types of superlattices: HgTe/CdTe and HgTe/HgSe. In short-period HgTe/CdTe superlattices, our analysis unveils the presence of isoenergetic nodal lines, which could host strain-induced three-dimensional flat bands at the Fermi level without requiring doping, when fabricated, for instance, as core-shell nanowires. In contrast, HgTe/HgSe short-period superlattices are found to harbor a rich phase diagram with a plethora of topological phases. Notably, the unstrained superlattice realizes an ideal Weyl semimetal with Weyl points situated at the Fermi level. A small-gap topological insulator with multiple band inversions can be obtained by tuning the volume: under compressive uniaxial strain, the material transitions sequentially into a Dirac semimetal to a nodal-line semimetal, and finally into a topological insulator with a single band inversion.</p> <p>The provided repository contains data to reproduce the figures of the corresponding article.</p>
TReNCo: Topologically associating domain (TAD) aware regulatory network construction (extended data)
<p>The enclosed files contain all of the extended data from: TReNCo: Topologically associating domain (TAD) aware regulatory network construction</p>
The implications of incongruence between gene tree and species tree topologies for divergence time estimation
<p>Phylogenetic analyses are increasingly being performed with datasets that incorporate hundreds of loci. Due to incomplete lineage sorting, hybridization, and horizontal gene transfer, the gene trees for these loci may often have topologies that differ from each other and from the species tree. The effect of these topological incongruences on divergence time estimation has not been fully investigated. Using a series of simulation experiments and empirical analyses, we demonstrate that when topological incongruence between gene trees and the species tree is not accounted for, the temporal duration of branches in regions of the species tree that are affected by incongruence is underestimated, whilst the duration of other branches is considerably overestimated. This effect becomes more pronounced with higher levels of topological incongruence. We show that this pattern results from erroneous estimation of the number of substitutions along branches in the species tree, although the effect is modulated by the assumptions inherent to divergence time estimation, such as those relating to the fossil record or among-branch-substitution-rate variation. By only analysing loci with gene trees that are topologically congruent with the species tree, or only taking into account the branches from each gene tree that are topologically congruent with species tree, we demonstrate that the effects of topological incongruence can be ameliorated. Nonetheless, even when topologically congruent gene trees or topologically congruent branches are selected, error in divergence time estimates remains. This stems from temporal incongruences between divergence times in species trees and divergence times in gene trees, and more importantly, the difficulty of incorporating necessary assumptions for divergence time estimation.</p>
Imaging topological defects in a non-collinear antiferromagnet
<p>Data related to the publication, arXiv 2202.02243.</p> <p> </p>
Decomposition, topology, properties, and graphs of woody crown networks of 15 tree species of Cerrado vegetation
<p>Data of decomposition, topology, properties, and the corresponding graphs of 15 adult tree species of Cerrado vegetation, <em>sensu stricto</em> physiognomy. The woody crown networks (WCN) representations in a bidimensional space were obtained by drawing followed the methodology described by Prado et al. (2020, Prado, C.H.B.A., Trovão, D.M.B.M., Souza, J.P.<strong>,</strong> 2020. A network model for determining the woody crown's decomposition, topology, and properties. Journal of Theoretical Biology, v. 499, p. 110318. https://doi.org/<a href="https://www.x-mol.com/paperRedirect/1258515479077781504">10.1016/j.jtbi.2020.110318</a>.). The branching regions were the nodes, and the woody crown segments connecting the nodes or merely emerging from them were the connectors. Those trees grew under natural conditions in a most common (<em>sensu stricto</em>) physiognomy of Cerrado vegetation, in a reservoir of 86 ha, located at 850 m above sea level in São Carlos city, São Paulo state, Brazil, at 21°58'- 22°00'S and 47°51'-47°52'W. Following the Köppen climatic classification, this region is between Aw and Cwa, a tropical climate with dry winter and wet summer. The rainy season occurs between October-March, and the dry season between April and September. </p>
Tuning topological spin textures in size-tailored chiral magnet insulator particles
<p>The file contains the raw data and code used for the paper entitled "Tuning topological spin textures in size-tailored chiral magnet insulator particles" by Priya R. Baral et al. to be published in Journal of Physical Chemistry C. </p> <p>Requests for further information can be directed to the corresponding author: Arnaud Magrez (arnaud.magrez 'at' epfl.ch) </p>
Topological Network of the Dutch Fairway Information System
<p>Topological fairway network derived from the <a href="https://www.vaarweginformatie.nl/">Dutch Fairway Information System</a>. The data is processed to be topological connected and usable for transport network analysis. </p> <p>Files</p> <p><code>network_digital_twin_v0.3.json</code> This is the json (for web) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p><code>network_digital_twin_v0.3.pickle</code> This is the pickled (for performance) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p><code>network_digital_twin_v0.3.zip</code> This is the shapefile (for gis) file for the Rhine corridor extending from Rotterdam (NLD) to Basel (Switzerland).</p> <p>Methodological information</p> <p>For details about the creation of the network see <a href="https://github.com/Deltares/digitaltwin-waterway/blob/master/notebooks/Build_FIS_network.ipynb">Build_FIS_network.ipynb</a>.</p> <p>Updates in version 0.3.0:</p> <p>- Added information on discharge dependent <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/velocities/read-velocities.ipynb">velocities</a> and <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/waterlevels/read_waterlevels.ipynb">waterlevels</a> in <code>river_waterlevel.geojson</code> and <code>river_velocity.geojson</code></p> <p>- Moved information on <a href="https://github.com/Deltares/digitaltwin-waterway/blob/feature/sailing/notebooks/fis-network/generate_bathymetry.ipynb">bathymetry</a> to separate file<code>edges_0.3_with_bathy.geojson</code></p> <p>- More structures added (fix in source dataset)</p> <p>- Edge and node id's are now always strings</p> <p>- Removed yaml file. It was not efficient enough and reading functionality is removed from networkx</p> <p> </p> <p>Updates in version 0.2.0:</p> <p>- added bathymetry info: mean, standard deviation , percentiles [0 (min), 5, 10, 50 (median), 90, 95, 100 (max)]</p> <p>- added new output formats: added shapely compatible geometry type</p> <p>Source data for the network is available at <a href="https://www.vaarweginformatie.nl/">https://www.vaarweginformatie.nl/</a>.</p> <p>Sharing and Access information</p> <p>CC BY-SA 4.0 license applies: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>.</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>
Data for the publication "Control of electronic topology in a strongly correlated electron system"
<p>Data sets of the figures in the publication "Control of electronic topology in a strongly correlated electron system"</p>
Vortex shedding topology for oscillating heavy spherical pendulums underwater
<p>Relevant data of vortex shedding topology for oscillating heavy spherical pendulums underwater to reproduce the major findings of the article "Dynamics of heavy subaqueous spherical pendulums" published in Journal of Fluid Mechanics. The article has been published as open access: <a href="https://doi.org/10.1017/jfm.2023.170">https://doi.org/10.1063/5.0086557</a></p>
Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"
<p>MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Source data files for manuscript "Closed Magnetic Topology in the Venusian Magnetotail and Ion Escape at Venus"
<p>The zip file contains source data files for all figures in the manuscript "Closed Magnetic Topology in the Venusian Magnetotail and Ion Escape at Venus" published in Nature Communications. DOI: 10.1038/s41467-024-50480-0.</p>
ScienceDex guides
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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.