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695 results for “topologies”
Replication data and theory code for: Observation of a Majorana zero mode in a topologically protected edge channel
<p>Replication Data for: Observation of a Majorana zero mode in a topologically protected edge channel</p>
Data for Nguyen Le at al. ""Topological phases of a dimerized Fermi-Hubbard model for semiconductor nano-lattices"
<p>Codes and simulation data used in Nguyen Le at al. "“Topological phases of a dimerized Fermi-Hubbard model for semiconductor nano-lattices."</p>
Data supporting the publication "Optimized Sandwich and Topological Structures for Enhanced Haptic Transparency"
<p>This dataset includes experimental modal responses of sandwich and topological structures, which are designed to enhance haptic transparency. </p>
TOPOLOGY OF MINKOWSKI METRIC
<p>"Topology of Minkowski Metric" investigates the mathematical structure and features of the Minkowski space, which is essential to the study of special relativity and theoretical physics.</p>
Electronic Structure and Topology in Gulf-edged Zigzag Graphene Nanoribbons
<h1>Electronic Structure, Topology and Spin-Polarization in Gulf-edged Zigzag Graphene Nanoribbons</h1> <p>This repository collects the necessary calculation files to reproduce the results shown in our manuscript (<a title="arXiv" href="https://arxiv.org/abs/2408.14839" target="_blank" rel="noopener">arXiv</a>). It includes the following parts:<br>* A) Structure files<br>* B) TB calculations<br>* C) DFT calculations<br>* D) GW calculations<br>* E) Parametrization of TB with Hubbard-U (TB+U)<br>* F) TB+U calculations<br>* G) ZGNR systems <br>* H) Calculations for different $U$ values</p> <h1>(A) Structure files</h1> <p>ZGNR-G structures, created with a C-C (C-H) bond length of 1.4 Ang (1.1 Ang) and bond angles of 120°. Structures are given with and without saturation of dangling bonds by hydrogen atoms. The unit cells are rectangular. The GNR is periodic in the x-direction, and a vacuum gap of 20 Ang between the carbon atoms is added in the y- and z-direction. We did not perform geometry optimization. Files are given in XYZ, XSF, and CIF format. The structural parameters are varied in the following range:<br>* $N$=4...11<br>* $a$=3...10<br>* $M$=2...9 (depending on $a$)<br>* $b$=0...a/2 (depending on $a$ and whether $N$ is odd or even)<br>* S and L inversion center</p> <p>The files are named in the following way: <br>* Carbon only (used in TB calculations): $N$-ZGNR-G$M$_$a$_$b$_<inversion center>.<file format><br>* Saturated systems (used in DFT calculations): $N$-ZGNR-G$M$_$a$_$b$_<inversion center>_saturated.<file format></p> <h1>(B) TB calculations</h1> <p>Minimal calculation files for the complete set of structures:<br>* Structure file in CIF format<br>* PythTB input file (onsite energy $\alpha$=0, 1st-NN hopping element $t_1$=-1)<br>* Calculation results in JSON format</p> <p>The data files contain the calculated band gaps and Z2 topological invariants, sorted into tables by structural parameters. A table is given for each combination of $N$, $M$, and inversion center. Each table varies the parameter $a$ in the rows (value of $a$ given first in each line) and the parameter $b$ in the columns (values not explicitly given, varied from 0 (0.5) to $a$/2 for even (odd) $N$). The band gaps are given in units of the 1st-NN hopping element $t_1$. The Z2 topological invariant is calculated using the Zak phase. A value is given for metallic systems even though the equations are not applicable to these systems. Additionally, the results are given as a simple list.</p> <h1>(C) DFT calculations</h1> <p>Calculation files for the subset of systems studied on the DFT/HSE06 level with a tight tier 1 basis and a k-grid of 18x3x3 using FHI-aims. ZGNR-G systems for this subset are selected to have a maximum of 100 carbon atoms in the primitive unit cell. Calculations are performed both without and with spin polarization. Spin-polarized systems are run with both an antiferromagnetic (AFM) and ferromagnetic (FM) initial guess (by placing an initial spin moment on the zigzag edge atoms), resulting in an AFM or FM magnetic state, respectively. </p> <p>For each calculation, the following files are stored:<br>* geometry.in: input geometry<br>* control.in: input file for FHI-aims<br>* aims.out: output file for FHI-aims <br>* band1001.out: band structure file for the first spin channel<br>* band2001.out: band structure file for the second spin channel (only for spin-polarized calculations)<br>* cube_001_spin_density.cube: converged spin density in CUBE format (compressed in ZIP format to save storage space after extracting calculation files)<br>* spin-polarization.png: plot of spin moments for carbon atoms (only for spin-polarized calculations)<br>* gap.dat: band gap, extracted from the band structure file<br>* spin_max.dat: maximum absolute spin moment, extracted from the Mulliken projection results<br>Additionally, for each structure, a plot comparing the band structure without spin polarization, the band structure of the AFM state, and the band structure of the FM state are stored. The DAT files are not stored for the FM state as those are never the magnetic ground state and thus were not further analyzed.</p> <p>In addition to the calculation files, the main results are collected in DAT files: the total energy (without spin polarization, AFM state, FM state), the band gap (without spin polarization and AFM state), and the maximum spin moment (only for the AFM state).</p> <h1>(D) GW calculations</h1> <p>Calculation files for the GW calculations, performed for 4-ZGNR, 5-ZGNR, and 6-ZGNR. They are calculated at the GW@PBE level and compared against calculations on the DFT/PBE and DFT/HSE06 level. Calculations are performed using FHI-aims with a tier 1 or tier 2 basis set and varying k-grids as visible from the file names. For each calculation, the input and output files are stored. They are sorted into subdirectories by their properties in the following order:<br>* Studied system,<br>* Method (DFT or GW),<br>* Functional, and<br>* Basis set and k-grid.</p> <h1>(E) Parametrization of TB with Hubbard-U (TB+U)</h1> <p>The parametrization of TB+U was done in two steps: (1) parametrization of the 1st NN hopping element $t_1$ and (2) the subsequent parametrization of the Hubbard-U, using the previously parametrized $t_1$.</p> <h2>(1) Parametrization of $t_1$</h2> <p>The parametrization of $t_1$ was done using the ZGNR-G systems available for DFT calculations. The TB and DFT calculations without spin polarization were used from steps B and C. Systems were excluded from the data set if the position of the DFT band gap was not reproduced in TB, leaving 372 ZGNR-G systems in the data set. The parametrization itself was done by linear regression of the DFT band gap in eV as a function of the TB band gap in units of $t_1$, resulting in y=3.328x-0.072 (R^2=0.951), giving $t_1$=3.328 eV. The TB and DFT band gaps are stored in the file "step1_parametrize_t1.dat"; the calculation files are taken directly from steps B and C.</p> <h2>(2) Parameterization of $U$</h2> <p>The parameterization of the Hubbard-U was done by first running TB+U calculations with different $U$ values. For this purpose, we varied $U$ from 0 to 5 in intervals of 0.2, using $t_1$=-1 to keep this step independent of the parametrization of $t_1$. The resulting band gaps are stored in DAT files in the subdirectory "calc_step2_variation_U" with a single file per ZGNR-G system. To save storage space, we did not upload further calculation files - the input files are equivalent to those uploaded in step F, just with different values for $t_1$ and $U$. </p> <p>Afterward, we used these results to obtain the optimal $U$ value for each system, focusing on systems that show a band gap opening in the AFM state on the DFT/HSE06 level. We performed the parametrization by identifying which value of $U$ in each system gives the best agreement of the TB+U band gap with the DFT band gap in the AFM state, using the calculations from step C and interpolating linearly between the $U$ values of the scan described above. We then ran a TB+U calculation with the obtained $U$ value to check the agreement with the DFT calculations. We generally obtained good agreement with a few exceptions that were filtered out: systems that resulted in a $U$ of zero and those without a band gap opening in the AFM state of the TB+U calculation. The results of the remaining 414 ZGNR-G systems are summarized in "step2_parametrize_U.dat". The final value of $U$ was obtained by averaging over those systems, yielding an average of 1.720 $t_1$, equivalent to 5.723 eV. The calculation files used for parametrization, including those filtered out, are stored in "calc_step2_TB+U_calculations". Plots comparing the band structures on DFT/HSE06 level, TB, and TB+U are in "plots_step2_fit_agreement".</p> <h1>(F) TB+U calculations</h1> <p>Calculation files for the complete set of structures:<br>* Structure file in XYZ format<br>* PythTB input file (onsite energy $\alpha$=0, 1st-NN hopping element $t_1$=-1, $U$=1.72 $t_1$)<br>* Calculation results in JSON format<br>* Plot of the band structure from TB vs. TB+U (AFM state)<br>* Plot of spin moments as an overlay over the atomic structure</p> <p>The data files contain the band gaps on TB and TB+U level ("results_band_gaps.dat"), the position of VBM and CBM on TB and TB+U level ("results_band_edge_positions.dat"), and spin momentum quantities ("results_spin_moments.dat"). Please note that, compared to the JSON files, a factor of 2 is applied to obtain the spin moment; this corrects the PythTB calculations, which multiply the final spin-polarization by a factor of 1/2 to account for electrons being particles with spin 1/2.</p> <h1>(G) ZGNR systems</h1> <p>ZGNR systems without gulf edges are included in the data set as a reference system. Structures are included in the subdirectory "structures" with widths $N$ from 2 to 50, analogously to part A. For all ZGNRs, DFT and TB+U calculations were performed. The provided files are equivalent to parts C and F. Additionally, for the DFT calculations with spin polarization, files for the maximum, minimum, and average (over the carbon atoms) spin moments are provided, distinguished by results from Mulliken and Hirshfeld analysis. The plots of the spin moments are also given for both the Mulliken and Hirshfeld analysis results.</p> <p>The data files contain the band gaps of the AFM state on DFT and TB+U level ("results_band_gaps_AFM_state.dat"), the total energy of the DFT calculations without and with spin-polarization in the AFM and FM state ("results_total_energies_DFT.dat"), as well as the maximum, minimum, and average (averaged over the C atoms) spin moment of the TB+U and DFT calculations, distinguished by Mulliken and Hirshfeld analysis ("results_spin-moments_maximum.dat," "results_spin-moments_minimum.dat," "results_spin-moments_average.dat"). Please note that, compared to the JSON files, a factor of 2 is applied to obtain the spin moment for the TB+U calculations.</p> <h1>(H) Calculations for different $U$ values</h1> <p>TB+U calculations similar to part F were performed for ZGNR and ZGNR-G systems. The main difference is that different values of $U$ were used: 1.20, 1.50, 1.72, and 2.00 in units of $t_1$. Please note that, compared to the JSON files, a factor of 2 is applied to obtain the spin moment for the TB+U calculations.</p>
Transport chirality generated by a tunable tilt of Weyl nodes in a van der Waals topological magnet
<p>The source ASCII data files for the article entitled "Transport chirality generated by a tunable tilt of Weyl<br>nodes in a van der Waals topological magnet".</p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Data for "Observation of microscopic confinement dynamics by a tunable topological θ-angle"
<p>This dataset is for the research article "Observation of microscopic confinement dynamics by a tunable topological θ-angle". A preprint version is available on arXiv with the identifier <a title="Observation of microscopic confinement dynamics by a tunable topological &theta;-angle" href="https://arxiv.org/abs/2306.11794" target="_blank" rel="noopener">arXiv:2306.11794</a>. </p> <p>Note that Figure 1 in the main text and Extended Data Figures S2 and S3 in the Methods section show the mapping relationship and initial state preparation, respectively, and do not contain original data. These details are stated in the corresponding files.</p>
Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor
<p>Molecular dynamics simulations of AF1c region of human glucocorticoid receptor and its phosphovariants as described in the below paper: </p> <p><strong>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</strong></p> <p>Vasily Akulov, Alba Jiménez Panizo, Eva Estébanez-Perpiñá, John van Noort, Alireza Mashaghi</p> <p> </p> <p>The data related to this project has been deposited in two repositories. This repository contains the first part of the data; the second part can be found at the DOI: 10.5281/zenodo.13822438</p>
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
<p>TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.</p> <p> </p> <p>If you use any of the data provided, please cite our paper:</p> <p> </p> <pre>@article{matzner2024topology, title={Topology Bench: systematic graph-based benchmarking for core optical networks}, author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina}, journal={Journal of Optical Communications and Networking}, volume={17}, number={1}, pages={7--27}, year={2024}, publisher={IEEE} }<br><br></pre>
Dataset for fracture topology in mafic formations
<p>This dataset is part of the GEOMIMIC project, funded by the European Union’s Horizon Europe Research and Innovation program, which aims to improve carbon storage and mineralization efficiency in fractured mafic reservoirs. It combines fracture property data from previous studies with new data gathered in this project, along with results from fracture network simulations exploring reactive fluid flow near injection wells in mafic formations.</p>
Figure data for the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology"
<p>This file contains the data shown in Fig. 1, Fig 3, Fig. 4, Fig. 5 and Fig. 6 of the manuscript "SI-traceable frequency dissemination at 1572.06 nm in a stabilized fiber network with ring topology". Additional information on the data and processing procedure are available from the author upon reasonable request.</p>
Bending strain in 3D topological semi-metals
<p>We present an experimental set-up for the controlled application of strain gradients by mechanical piezoactuation on 3D crystalline microcantilevers that were fabricated by focused ion beam machining. A simple sample design tailored for transport characterization under strain at cryogenic temperatures is proposed. The topological semi-metal Cd<sub>3</sub>As<sub>2</sub> serves as a test bed for the method, and we report extreme strain gradients of up to 1.3%μm<sup>−1</sup> at a surface strain value of≈0.65% at 4K. Interestingly, the unchanged quantum transport of the cantilever suggests that the bending cycle does not induce defects via plastic deformation. This approach is a first step towards realizing transport phenomena based on structural gradients, such as artificial gauge fields in topological materials.</p>
Aerostructural Topology Optimization using High Fidelity Modeling
<p>The files included define shape and topology optimization problems with fluid-structure interaction (modelled with SU2) that were presented in:</p> <p>P. Gomes and R. Palacios, “Aerostructural Topology Optimization using High Fidelity Modeling,” in AeroBest 2021, July 2021.</p>
Topological phonon-polariton funneling in mid-infrared metasurfaces
<p>Data set for the paper titled "Topological phonon-polariton funneling in mid-infrared metasurfaces".</p>
Structure-imposed electronic topology in cove-edged graphene nanoribbons
<p><strong>Abstract</strong></p> <p>In cove-edged zigzag graphene nanoribbons (ZGNR-C), one terminal group per length unit is removed on each zigzag edge, forming a regular pattern of coves which controls their electronic structure. Based on three structural parameters that unambiguously characterize the atomistic structure of ZGNR-C, we present a scheme that classifies their electronic state, i.e., if they are metallic, topological insulators or trivial semiconductors, for all possible widths <em>N</em>, unit lengths <em>a</em> and cove position offsets at both edges <em>b</em>, thus showing the direct structure-electronic structure relation. We further present an empirical formula to estimate the band gap of the semiconducting ribbons from <em>N</em>,<em>a</em>, and <em>b</em>. Finally, we identify all geometrically possible ribbon terminations and provide rules to construct ZGNR-C with well-defined electronic structure.</p> <p>DOI: 10.1103/PhysRevLett.129.216401</p> <p><strong>Content of repository</strong></p> <p>The repository contains the inputs and outputs of tight-binding (TB) calculations of ZGNR-C based on <a href="http://www.physics.rutgers.edu/pythtb/">PythTB</a>. For each analysed structure one subdirectory is created, labelled as "N-ZGNR-C_a_b_inv_cell<span class="math-tex">\(\alpha\)</span>_termination". This corresponds to a <em>N</em>-ZGNR-C(<em>a</em>,<em>b</em>) with inversion center at the unit cell boundary <em><strong>S</strong></em> or <em><strong>L</strong></em> ("inv"), unit cell angle <span class="math-tex">\(\alpha\)</span> ("cell<span class="math-tex">\(\alpha\)</span>": 60°, 90°, or 120°) and a given unit cell termination (armchair, zigzag or bearded). Each directory contains the atomic structure in xsf and cif format, the PythTB input file, the output as a json file, and the calculated band structure as image file. The json file contains the band structure information (path and eigenvalues), the raw Zak phase in units of <span class="math-tex">\(\pi\)</span> without modulo 2, and the final <span class="math-tex">\(\mathbb{Z}_2\)</span> invariant.</p> <p> </p>
Data for: Evaluating the impact of anatomical partitioning on summary topologies obtained with Bayesian phylogenetic analyses of morphological data
<p>Morphological data are a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task. Bayesian phylogenetic analyses require the use of evolutionary models, which have been intensively studied in the past few years, with significant improvements to our knowledge. Notwithstanding, a systematic evaluation of the performance of partitioned models for morphological data has never been performed. Here we evaluate the influence of partitioned models, defined by anatomical criteria, on the precision and accuracy of summary tree topologies considering the effects of model misspecification. We simulated datasets using partitioning schemes, trees, and other properties obtained from two empirical datasets, and conducted Bayesian phylogenetic analyses. Additionally, we reanalysed 32 empirical datasets for different groups of vertebrates, applying unpartitioned and partitioned models, and, as a focused study case, we reanalysed a dataset including living and fossil armadillos, testing alternative partitioning hypotheses based on functional and ontogenetic modules. We found that, in general, partitioning by anatomy has little influence on summary topologies analysed under alternative partitioning schemes with a varying number of partitions. Nevertheless, models with unlinked branch lengths, which account for heterotachy across partitions, improve topological precision at the cost of reducing accuracy. In some instances, more complex partitioning schemes, led to topological changes, as tested for armadillos, mostly associated with models with unlinked branch lengths. We compare our results with other empirical evaluations of morphological data and those from empirical and simulation studies of partitioning of molecular data, considering the adequacy of anatomical partitioning relative to alternative methods of partitioning morphological datasets.</p>
The topological nature of tag jumping in environmental DNA metabarcoding studies (sequencing raw data)
<p>Metabarcoding of environmental DNA constitutes a state-of-the-art tool for environmental studies. One fundamental principle implicit in most metabarcoding studies is that individual sample amplicons can still be identified after being pooled with others – based on their unique combinations of tags – during the so-called demultiplexing step that follows sequencing. Nevertheless, it has been recognized that tags can sometimes be changed (i.e. tag jumping), which ultimately leads to sample crosstalk. Here, using four DNA metabarcoding datasets derived from the analysis of soils and sediments, we show that tag jumping follows very specific and systematic patterns. Specifically, we find a strong correlation between the number of reads in blank samples and their topological position in the tag matrix (described by vertical and horizontal vectors). This observed spatial pattern of artefactual sequences could be explained by polymerase activity, which leads to the exchange of the 3' tag of single stranded tagged sequences through the formation of heteroduplexes with mixed barcodes. Importantly, tag jumping substantially distorted our datasets – despite our use of methods suggested to minimize this error. We developed a topologic model to estimate the noise based on the counts in our blanks, which suggested that 40-80% of the taxa in our soil and sedimentary samples were likely false positives introduced through tag jumping. We highlight that the amount of false positive detections caused by tag jumping strongly biased our community analyses. </p>
dataset for T. Shimaya and K. A. Takeuchi, Tilt-induced polar order and topological defects in growing bacterial populations. PNAS Nexus 2022
<p>dataset for T. Shimaya and K. A. Takeuchi, Tilt-induced polar order and topological defects in growing bacterial populations. PNAS Nexus 2022. DOI: 10.1093/pnasnexus/pgac269</p>
Weighted Link Schedules in 100-node Fixed Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a fixed-topology placed networks. This data set contains samples for 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</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
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.