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695 results for “topologies”
FIGURE 3. Phylogenetic tree topology for the 16S in Description of a new flat gecko (Squamata: Gekkonidae: Afroedura) from Mount Gorongosa, Mozambique
FIGURE 3. Phylogenetic tree topology for the 16S gene (identical for Bayes and ML) using Afroedura hawequensis as outgroup. Support values for posterior probabilities and bootstraps are indicated by circles (PP:>0.95 / ML:>70%): full black circles indicated support by both methods and full open circles indicate no strong support by either method.
FIGURE 6. Topology obtained under k in Revision and phylogenetic analysis of the orb-weaving spider genus Glenognatha Simon, 1887 (Araneae, Tetragnathidae)
FIGURE 6. Topology obtained under k = 7. Filled and open circles represent non-homoplasious and homoplasious transformations, respectively. Character numbers are placed over the branches and the states are shown below the branches. Letters denote clade names.
Revealing the three-dimensional arrangement of polar topology in nanoparticles
<h1>Supplementary Data Codes</h1> <div>The data and source codes for the paper [<a>C.J., J.L., H.J., J.O., H.B., K.-J.G., J.S., S.-Y.C., S.P., L.B., and Y.Y., "Revealing the three-dimensional arrangement of polar topology in nanoparticles", <em>Nat. Commun. </em><strong>15</strong><em>.</em> 3887, (2024)]</a> are posted below.</div> <div> </div> <h1>Overview</h1> <div> <p>In the early 2000s, low dimensional ferroelectric systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and elastic distortion energies. However, the measurement and classification of topological textures for general ferroelectric nanostructures have remained elusive, as it requires mapping the local polarization at the atomic scale in three dimensions. Here we unveil topological polar structures in ferroelectric BaTiO3 nanoparticles via atomic electron tomography, which enables us to reconstruct the full three-dimensional arrangement of cation atoms at an individual atom level. Our three-dimensional polarization maps reveal clear topological orderings, along with evidence of size-dependent topological transitions from a single vortex structure to multiple vortices, consistent with theoretical predictions. The discovery of the predicted topological polar ordering in nanoscale ferroelectrics, independent of epitaxial strain, widens the research perspective and offers potential for practical applications utilizing contact-free switchable toroidal moments.</p> </div> <h1>System Requirements</h1> <div>1. All software dependencies and operating systems (including version numbers)</div> <div>Software: MATLAB R2021a – academic use</div> <div>Operating systems: CentOS Linux 7</div> <div> </div> <div>2. Versions the software has been tested on</div> <div>MATLAB R2021a – academic use</div> <div> </div> <div>3. Any required non-standard hardware</div> <div>There are no non-standard hardware</div> <div> </div> <h1>Repositary Contents</h1> <h3>1. Raw Experiment Data</h3> <div>Folder : 1_Raw_data</div> <div> </div> <div>This folder contains raw experimental projections before denoising as well as their corresponding angles for Particle 1 and Particle 2.</div> <div> </div> <div>File name</div> <div>: ImageData.mat : raw experimental projections</div> <div>: Angles.mat : corresponding angles</div> <div> </div> <h3>2. Experiment Data</h3> <div>Folder: 2_Measured_data</div> <div> </div> <div>This folder contains experimental projections after denoising and alignment as well as their corresponding angles for Particle 1 and Particle 2.</div> <div> </div> <div>File name</div> <div>: projections.mat : experimental projections after denoising and alignment</div> <div>: zeroprojections.mat : zero-degree projections at the beginning, in the middle, and at the end of the experiment </div> <div>: angles.mat : corresponding angles</div> <div> </div> <h3>3. Reconstructed 3D Volume</h3> <div>Folder: 3_Final_reconstruction_volume</div> <div> </div> <div>This folder contains the 3D volume (before and after orientation) of the BaTiO3 nanoparticles (Particle 1 and Particle 2) reconstructed from GENFIRE algorithm.</div> <div>The original GENFIRE URL is below.</div> <div>(https://www.physics.ucla.edu/research/imaging/dataSoftware.html)</div> <div> </div> <div>File name</div> <div>: volume_Particle1.mat : Reconstructed 3D volume of Particle 1</div> <div>: volume_Particle2.mat : Reconstructed 3D volume of Particle 2</div> <div>: reorientedvol_Particle1.mat : Reoriented volume along crystallographic direction of Particle 1</div> <div>: reorientedvol_Particle2.mat : Reoriented volume along crystallographic direction of Particle 2</div> <div>: Up_Particle1.mat : Rotation matrix for rotating along crystallographic direction of Particle 1</div> <div>: Up_Particle2.mat : Rotation matrix for rotating along crystallographic direction of Particle 2</div> <div> </div> <h3>4. Experimental Atomic Structure</h3> <div>Folder : 4_Final_atomic_structures</div> <div> </div> <div>This folder contains the final 3D atomic model and chemical species (i.e. type 1 for Ti atom, type 2 for Ba atom) of the BaTiO3 nanoparticles.</div> <div> </div> <div>File name</div> <div>: Final_atomic_model_Particle1.mat : Final 3D atomic model and chemical species of Particle 1 (after orientation along crystallographic direction)</div> <div>: Final_atomic_model_Particle2.mat : Final 3D atomic model and chemical species of Particle 2 (after orientation along crystallographic direction)</div> <div> </div> <h3>5. Data analysis</h3> <div>Folder : 5_Data_analysis</div> <div>This folder contains the analysis of creating Ti atomic displacement field and its topological analysis.</div> <div>1. Run the code Main_1_Ti_atomic_displacement_field.m to compute Ti atomic displacement fields for all Ti atoms surrounded by 8 nearest neighbor Ba atoms based on bcc fitting and calculate tetragonality map (c/a ratio) by using local tetragonal fitting.</div> <div>2. Run the code Main_2_Obtain_Topological_Charge.m to conduct topological analysis (i.e. helicity, winding number, pontryagin charge density, skyrmion number, monopole analysis and hopf invariants.</div> <div> </div> <div>If you use any of the above data or source codes in your publications and/or presentations, our paper should be properly cited: <a>C.J., J.L., H.J., J.O., H.B., K.-J.G., J.S., S.-Y.C., S.P., L.B., and Y.Y., "Revealing the three-dimensional arrangement of polar topology in nanoparticles", <em>Nat. Commun. </em><strong>15</strong><em>.</em> 3887, (2024)</a><br><br>If you have any questions regarding the above data or source codes, please contact Yongsoo Yang, Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea. Email: <a href="mailto:yongsoo.yang@kaist.ac.kr">yongsoo.yang@kaist.ac.kr</a></div>
Emergent flat band and topological Kondo semimetal driven by orbital-selective correlations
Open the record for dataset details and reuse information.
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACoA)
<p>This repository contains the dataset for the Missing ACoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
<p>Dataset of the synthetic networks used in the paper "Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance".</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database:Missing PCoA)
<p>This repository contains the dataset for the Missing PCoA described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Input files, parameter files, topology files for pure gas, gas-water, and lipid nanobubble systems(parameters); scripts for generating lipid nanobubbles (AA-LNB. py).
<p>Input files, parameter files, topology files for pure gas, gas-water, and lipid nanobubble systems(parameters); scripts for generating lipid nanobubbles (AA-LNB. py).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing ACA A1)
<p>This repository contains the dataset for the Missing ACA A1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoAs)
<p>This repository contains the dataset for the Missing PCoAs described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Data for "Chiral topological light for detection of robust enantiosensitive observables"
<p><strong>Data for "Chiral topological light for detection of robust enantiosensitive observables"</strong></p> <p>This repository contains the data files and Jupyter notebook to reproduce the numerical results from the paper</p> <p><em>"Chiral topological light for detection of enantiosensitive observables"</em>, N. Mayer, D. Ayuso, P. Decleva, M. Khokhlova, E. Pisanty, M. Ivanov and O. Smirnova, Nature Photonics (2024), <a href="https://doi.org/10.1038/s41566-024-01499-8">doi:10.1038/s41566-024-01499-8</a>, <a href="https://arxiv.org/abs/2303.10932">arXiv:2303.10932</a></p> <p>as well as the results contained in its Supplementary Files. </p> <p>The Python code used to calculate the data files is available from the authors upon reasonable request. Please refer to the Methods section of the paper for a detailed description of the numerical approach behind the numerical simulations.</p> <p>Note that the figures in the paper are assembled in a separate software from the plots reproduced here, and that we include here only the results for the theoretical simulations of the molecular response.</p> <p>The repository contains the following files:</p> <ul> <li><em>atomic_units.py</em>: Python file containing useful definitions of quantities in atomic units.</li> <li><em>readme.txt</em>: file containing a detailed description of the files contained in files.zip</li> <li><em>Figures_CTL.ipynb</em>: Jupyter notebook to load the files in files.zip and plot the images of the paper.</li> <li><em>files.zip</em>: zip file containing the data files.</li> </ul> <p>The copyright of this collection rests with the authors (2024). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>) license. For any academic use that results in a publication, please cite the main paper in addition to this deposit.</p>
Non-Hermitian topology of transport in Chern insulators
<p>This repository contains our simulation data and codes used to produce the figures of the manuscript/supplementary material.</p>
Lymph node STARmap/RIBOmap dataset of Chemical and topological design of multi-capped mRNA and capped circular RNA
<p>Here is the STARmap/RIBOmap dataset of Lymph node section included in "<strong>Chemical and topological design of multi-capped mRNA and capped circular RNA</strong>" from Chen et al. </p>
RAW SILAC mass spectrometry data of Chemical and topological design of multi-capped mRNA and capped circular RNA
<p>Here is the RAW SILAC mass spectrometry data included in "<strong>Chemical and topological design of multi-capped mRNA and capped circular RNA</strong>" from Chen et al. Please find the "README.txt" file for more details. </p>
Data for "Axion insulator state in hundred-nanometer-thick magnetic topological insulator sandwich heterostructures"
<p>All data in the main text for "Axion insulator state in hundred-nanometer-thick magnetic topological insulator sandwich heterostructures"</p>
Impact of Neural Cyberattacks on a Realistic Neuronal Topology from the Primary Visual Cortex of Mice
<p>Dataset associated to the paper "Impact of Neural Cyberattacks on a Realistic Neuronal Topology from the Primary Visual Cortex of Mice"</p>
FIGURE 2. Neighbour-joining tree topology using Kimura 2 in Redescription of Chimaera ogilbyi (Chimaeriformes; Chimaeridae) from the Indo-Australian region
FIGURE 2. Neighbour-joining tree topology using Kimura 2 Parameter distance based on nucleotide sequence divergence in aligned nucleotide NADH2 sequences for Chimaera ogilbyi and closely related species. Outgroup is represented by Rhinochimaera atlantica and Harriotta raleighana. Sequence labels are based on operational taxonomic units defined in paper. GenBank accession numbers follow sequence labels. Scale bar represents 2% K2P distance.
Reproducing topological properties with quasi-Majorana states
<p>Dataset and code for the publication "Reproducing topological properties with quasi-Majorana states".</p>
Separating the roles of magnetic topology and neutral trapping in modifying the detachment threshold for TCV
<p>Dataset for the plasma physics paper "Separating the roles of magnetic topology and neutral<br> trapping in modifying the detachment threshold for TCV". </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.