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695 results for “topologies”
Supplementary material for the paper: "Mesh-based topology, shape and sizing optimization of ribbed plates"
<p>Source code and results, accompanying the paper "Mesh-based topology, shape and sizing optimization of ribbed plates", published in Structural and Multidisciplinary Optimization, 2024. </p>
ISP topology and traffic dataset
<div>This dataset contains the topology and some interface-level traffic information for two European National Research and Education Networks:</div> <div> <ul> <li>Switch LAN (CH)</li> <li>SURF (NL)</li> </ul> <p>The data has been provided by the operators of these networks and published with their consent. Please refer to the README for more details.</p> <p>We would love to see this dataset extended by and for the community. If you have similar data to contribute, please do not hesitate to <a href="mailto:jacobr@ethz.ch" target="_blank" rel="noopener">reach out to Romain</a>.</p> </div>
Fig. 1. Species trees and alternative constraint convergence topologies. H0 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 1. Species trees and alternative constraint convergence topologies. H0 is the well-accepted species tree. H1, H2, and H3 are three alternate echolocator-converged topologies. H1-control, H2-control and H3-control refer to the constraint convergent topologies of representative non-echolocators (cow, hedgehog, and non-echolocating bats).
Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet"
<p>Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet" by I. Belopolski et al., Science 365, 1278-1281 (2019).</p>
ROC_all SINTEF workflow results with Atopo topology
<p>This dataset has the input data + result dataset that was the product of using the <strong>DrugLogics</strong> computational pipeline with the <strong>rbbt</strong> workflow system to predict synergistic drug combinations across 8 cell lines that were also tested in SINTEF. <strong>An automated generated Signor-based topology</strong> was used and the logical models were trained to a <strong>steady state activity profile </strong>that was derived using the <strong>PARADIGM tool</strong> and input from the<strong> CCLE</strong>.</p>
ROC_all SINTEF workflow results with CASCADE topology
<p>This dataset has the input data + result dataset that was the product of using the <strong>DrugLogics</strong> computational pipeline with the <strong>rbbt</strong> workflow system to predict synergistic drug combinations across 8 cell lines that were also tested in SINTEF. The <strong>CASCADE topology</strong> was used and the logical models were trained to a <strong>steady state activity profile </strong>that was derived using the <strong>PARADIGM tool</strong> and input from the<strong> CCLE</strong>.</p>
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs. in The sauropodomorph biostratigraphy of the Elliot Formation of southern Africa: Tracking the evolution of Sauropodomorpha across the Triassic-Jurassic boundary
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs.
Research data supporting the publication "Hierarchy of Topological Transitions in a Network Liquid"
<p>This dataset contains the data used to produce each of the figures in the manuscript "Hierarchy of Topological Transitions in a Network Liquid" published in PNAS. The dataset includes configuration files for the dendrimer system generated via NPT Monte Carlo simulations and the topological analysis of the networks formed by the dendrimer particles.</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>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</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 second part of the data; the first part can be found at DOI: 10.5281/zenodo.13820169</p>
Localized Topological States beyond Fano Resonances via Counter-Propagating Wave Mode Conversion in Piezoelectric Microelectromechanical Devices
<p>Main numerical and experimental data (Matlab .fig files) of the paper <em>Localized Topological States beyond Fano Resonances via Counter-Propagating Wave Mode Conversion in Piezoelectric Microelectromechanical Devices.</em></p>
Data for "Characterizing the chemical potential disorder in the topological insulator (Bi1−xSbx)2Te3 thin films"
<p>Here, the data underlying all figures of the paper "Characterizing the chemical potential disorder in the topological insulator (Bi$_{1−x}$Sb$_x$)$_2$Te$_3$ thin films" is given in .csv or .txt file format.<br>Furthermore, raw data files are provided.<br>The STM/STS data was recorded and stored with the Nanonis SPM Control Software Version Generic 5 in the Nanonis file format (.sxm) for scan images, binary file format (.3ds) for STSgrids and ASCII file format for simple point STS.</p>
Datasets for Manuscript: Neural-Network-Assisted Detection of Superconducting Topological Semimetals
<p>This file contains datasets for an original machine-learning-approach that we developed for the identification of superconducting topological semimetals.</p>
Superior robustness of anomalous nonreciprocal topological edge states
<p>Figure data, raw measured data, and codes for the paper "Superior robustness of anomalous nonreciprocal topological edge states", Nature 2021, by Z. Zhang et al.</p>
Fig. 44. Tree topology taken from figure 43 in Freshwater Stingrays Of The Green River Formation Of Wyoming (Early Eocene), With The Description Of A New Genus And Species And An Analysis Of Its Phylogenetic Relationships (Chondrichthyes: Myliobatiformes)
Fig. 44. Tree topology taken from figure 43 (strict consensus tree) with ambiguous characters mapped (numbered as in text and matrix in table 5, unambiguous characters in fig. 43). Some of the characters are ambiguous because they are scored as uncertain in Hexatrygon (characters 4 and 21) and in Myliobatis (character 16); these are displayed conservatively on the tree (i.e., it is not simply assumed that they will be found in these taxa). Other characters have more than one equally parsimonious optimization (characters 3, 20, 32, 43), whereas others are scored as uncertain in the Green River stingrays (characters 12, 22, and 44). The optimization chosen in both of these cases is accelerated transformation, favoring reversals over independent gains. Characters denoted with an asterisk (*) are part of multistate transformation series that have unambiguous character states in figure 43.
Fig. 60. Unambiguous character changes mapped onto topology from figure 57A. Each circle represents a in Morphology And Relationships Of Apternodus And Other Extinct, Zalambdodont, Placental Mammals
Fig. 60. Unambiguous character changes mapped onto topology from figure 57A. Each circle represents a character state change, with the character number above and state below (corresponding to the list given in the text and in table 5). Changes represented by solid circles show less homoplasy than those represented by open circles.
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
<p>A pre-trained model of the paper "Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections," CoRL 2020.</p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Data for "Realizing topologically ordered states on a quantum processor"
<p>Data and code used in "Realizing topologically ordered states on a quantum processor," Satzinger et al. (2021), preprint at https://arxiv.org/abs/2104.01180</p>
Circulant topologies dataset
<p>Circulant topologies with different generators count (2-10).</p> <p>This is a dataset consisting of signatures of optimal circulant topologies for various parameters. Circulants are regular topologies based on the Cayley graphs of a cyclic group. This type of topologies can be described as C(n; s_1,…,s_k), where n – number of nodes, k – number of generators, and k<n, k = 2..10.</p> <p>CSV files format: nodes count; graph signature; Diameter; average distance; generating time; connections count.</p> <p><strong>For citations:</strong></p> <blockquote> <p>Romanov, A. The Dataset for Optimal Circulant Topologies. <em>Big Data Cogn. Comput.</em> <strong>2023</strong>, <em>7</em>, 80. https://doi.org/10.3390/bdcc7020080</p> </blockquote>
Excited-state photoemission band mapping data of the topological insulator Bi2Te2Se
<p>The dataset contains excited-state photoemission band mapping data of the topological insulator Bi2Te2Se.</p> <p><strong>Provenance</strong>; The measurements were carried out at the Fritz Haber Insititute of the Max Planck Society, Germany. The Bi2Te2Se sample was grown at Aarhus University, Denmark. The photoelectrons were detected using SPECS METIS 1000 3D detector with extreme UV pulses centered at 21.7 eV as probe and a pump light pulses centered at 800 nm, which populates part of the excited electronic states (i.e. first conduction band).</p> <p><strong>Content</strong>: The dataset comes from (1) binning of the single-electron event data and (2) preprocessing through symmetrization and MCLAHE algorithm (https://ieeexplore.ieee.org/document/8895993) for contrast adjustment</p> <p><strong>Usage</strong>: The dataset is the prerequisite for band structure reconstruction, which may be used for reproducing the results in (https://arxiv.org/abs/2005.10210). For reconstruction, see the source code and examples on GitHub (https://github.com/mpes-kit/fuller).</p>
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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.