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695 results for “topologies”
FIGURE 2. Strict consensus tree from the unweighted analysis. The same topology was obtained from 12 in Phylogenetic relationships of the comb-footed spider subfamily Spintharinae (Araneae, Araneoidea, Theridiidae), with generic diagnoses and a key to the genera
FIGURE 2. Strict consensus tree from the unweighted analysis. The same topology was obtained from 12 (PAUP, branch collapsing rule 3) and two (TNT, branch collapsing rule 1) equally most parsimonious trees (spintharine synapomorphies with character numbers above and character states below).
Learning topological states from randomized measurements using variational tensor network tomography
<p>Dataset for paper <strong>Learning topological states from randomized measurements using variational tensor network tomography.</strong><br>The numerical code can be found at the repo: https://github.com/teng10/tn-shadow-qst</p>
Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes
<p>Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes</p>
The limits of the constant-rate birth-death prior for phylogenetic tree topology inference
<p>Birth-death models are stochastic processes describing speciation and extinction through time and across taxa, and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under constant-rate birth-death (crBD) tend to differ from empirical trees, for example with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between crBD and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which crBD differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used crBD priors with those that used other non-BD Bayesian and non-Bayesian methods, we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using crBD in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios, but also highlights that empirically observed phylogenetic imbalance is highly improbable under crBD, leading to systematic bias in data sets with limited information content.</p>
Edge Mode Percolation and Equilibration in the Topological Insulator Cadmium Arsenide
Open the record for dataset details and reuse information.
Anomalous Nernst effect in the topological and magnetic material MnBi4Te7 (Data)
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Figure 11. Topology obtained using a in A new primitive alligatorine from the Eocene of Thailand: relevance of Asiatic members to the radiation of the group
Figure 11. Topology obtained using a strict consensus with the matrix of Salisbury et al. (2006). Krabisuchus siamogallicus is highlighted with a box.
Figure 12. Topology obtained using a in A new primitive alligatorine from the Eocene of Thailand: relevance of Asiatic members to the radiation of the group
Figure 12. Topology obtained using a strict consensus with the matrix of Brochu (1999, 2004). Krabisuchus siamogallicus is highlighted in grey and is placed in the sister clade to the genus Alligator. Abbreviations in boxes indicate the geographical origin of taxa or clades: As, Asia; Eu, Europe; Na, North America.
FIGURE 5. Alternative topologies among 11 in Māwhitiwhiti Aotearoa: Phylogeny and synonymy of the silent alpine grasshopper radiation of New Zealand (Orthoptera: Acrididae)
FIGURE 5. Alternative topologies among 11 representative species of Aotearoa New Zealand grasshopper used to test compatibility of existing taxonomic treatment. A) unconstrained ML phylogeny of 15 protein coding genes, and B) the same data with congeneric species constrained to monophyly is a significantly less-likely tree.
Supporting Data and Code for "Topological Order from Measurements and Feed-Forward on a Trapped Ion Quantum Computer"
<p>Supporting Data and Code for the paper "Topological Order from Measurements and Feed-Forward on a Trapped Ion Quantum Computer". </p> <p>Navigate to wen16/ then pip install -r requirements.txt<br>To generate all the plots/data run plots_and_data.ipynb</p>
Dataset for 'Mapping twist-tuned multi-band topology in bilayer WSe$_2$'
<p>Datasets and code for the manuscript 'Mapping twist-tuned multi-band topology in bilayer WSe2.' See 'README.txt' for further details. </p>
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O'Connor, Chiappe & Bell (2011) and divergence times are based on a 'literal' interpretation of the fossil record from Brockelhurst et al. (2012). Taxa abbreviations: Nth, Neornithes; Orph, Ornithuromorpha; Orn, Ornithothoraces; Orth, Ornithurae; Pyg, Pygostylia.
Simulation data for Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation
<p>Polymer configuration data from simulations of loop-extrusion on a replicating bacterial chromosome, used for the manuscript "Loop-extruders alter bacterial chromosome topology to direct entropic forces for segregation". New version includes data for more specific off-loading, as well as fewer SMCs.</p>
Dataset: Polarization-driven band topology evolution in twisted MoTe2 and WSe2
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IPDPS24: Graph Analytics on Jellyfish Topology
<p> </p> <p>The Artifact Description document (Artifact_description.pdf) in this repository contains the necessary information about our open-source GitHub-hosted code artifacts pertaining to our "IPDPS24" paper titled "Graph Analytics on Jellyfish Topology". It outlines how to build and run the applications to reproduce our results, as well as to conduct additional experiments using our implementations.</p> <p><strong>Repository Description:</strong></p> <p>This repository contains a collection of routing data organized into various subfolders.</p> <p><strong>Folder Structure:</strong></p> <p><strong><em>Routing Data Subfolders:</em></strong></p> <p>Each subfolder follows the naming convention:</p> <p>routing_data_(application_name)_(topology_name)_and_(equivalent_jellyfish)<br>These subfolders contain routing results for a specific application on a given topology and its equivalent Jellyfish topology, across different input graphs.</p> <p><strong><em>File Naming Convention:</em></strong></p> <p>Files within these subfolders are named according to:</p> <p>(input_graph_name)_(topology_name)_(topology_configurations)_(routing_algorithm_name)<br>For example: orkut_dfly_17_9_par</p> <p>input_graph_name: orkut<br>topology_name: Dragonfly (dfly)<br>topology_configurations: Configuration details of the topology (e.g., number of switches per group, number of groups).<br>routing_algorithm_name: Progressive adaptive Routing(PAR) - Name of the routing algorithm used. </p> <p><strong><em>Scaling Subfolders:</em></strong></p> <p>strong_scaling: Contains data for strong scaling.<br>weak_scaling: Contains data for weak scaling.</p> <p><strong><em>Graph-specific Data Subfolders:</em></strong></p> <p>bfs_rgg_data: Contains data for random regular graphs on Graph500 BFS application.<br>minivite_rgg_data: Contains data for random regular graphs on Minivite (graph clustering) application.</p> <p><strong><em>File Contents:</em></strong></p> <p>Each file within the subfolders contains information such as stalls, hops, MPI time, estimated runtime, etc., corresponding to the routing results for the specified configurations.</p>
The topology of spatial networks affects stability in experimental metacommunities
<p><span>Understanding the drivers of community stability has been a central goal in ecology. Traditionally the emphasis has been placed on studying the effects of biotic interactions on community variability, and less is understood about how the spatial configuration of habitats promotes or hinders metacommunity stability. To test the effects of contrasting spatial configurations on metacommunity stability, I designed metacommunities with patches connected as random or scale-free networks. In these microcosms, two prey and one protist predator dispersed, and I evaluated community persistence, tracked biomass variations, and measured synchrony between local communities and their neighbors. After 30 generations, scale-free metacommunities had lower global biomass variability and higher persistence, suggesting higher stability. At the local scale, patches in scale-free metacommunities showed a positive relationship between variability and patch connectivity, indicating higher stability in isolated communities. No clear relationship was observed in random networks. These results suggest the increased heterogeneity in connectivity of scale-free networks favors the prevalence of isolated patches in the metacommunity, which likely act as refugia against competition—the most dominant interaction in this system—resulting in higher global stability. These results highlight the importance of accounting for network topology in the study of spatial dynamics.</span></p>
Morph_CNeT: A new GIS tool to extract morphometric attributes characterising channel network topology of Indian catchments
<p><span>Morph_CNeT” (Morphometric Channel Network Extraction Tool) can facilitate extraction of the topology based new morphometric attributes by processing DEM datasets within GIS framework. Morph_CNeT tool is used to create a repository named as Morph_CNeT-India of topological catchment attributes for 1749 gauging stations maintained by the Central Water Commission (CWC) across 22 River basin Systems of India.</span></p>
Figure 5. Topology obtained under K in Two new species of the genus Quindina Roewer, 1914 (Opiliones: Nomoclastidae) from Colombia: phylogenetic relationships and notes on their nest architecture
Figure 5. Topology obtained under K = 7; only the family Nomoclastidae is displayed. Numbers above branches indicate Bootstrap support with absolute frequency (left) and Group present/Contradicted values (right), square brackets indicate negative differences. Navajo rugs: NE, Nelsen strict consensus under equal weighting; K, implied weighting (K 3, 4, 5, 6, 7, 8, 9, 10, 15). Next to each species are depicted the codes for the character #96 (Nest architecture) and a photograph for each state in the box on the right. Nest type 0 photo by Rosanette Quesada.
Figure 6. Topology obtained under K in Two new species of the genus Quindina Roewer, 1914 (Opiliones: Nomoclastidae) from Colombia: phylogenetic relationships and notes on their nest architecture
Figure 6. Topology obtained under K = 7, with unambiguous character optimisations shown in each branch. Empty and filled hashmarks represent homoplasious and non-homoplasious transformations, respectively, with characters on top and states below. Only the genera Callcosma and Quindina are displayed.
TEFNET24: Reference Packet Optical Network Topology for Edge to Core Transport [Invited]
<p>In this paper, we introduce TEFNET24, a reference multi-layer hierarchical network topology that spans from access to core networks, specifically designed to meet the demands of beyond-5G and and prepared for next-generation 6G communication systems. This topology, inspired by the actual network deployments of Telefónica in medium-sized countries in Europe and America, integrates both IP and optical (DWDM) layers to provide a comprehensive framework for network design, optimization, and analysis. Our primary contribution is the development of an open-source benchmarking network, accessible to both researchers and industry professionals. This resource aims to facilitate the study and advancement of integrated IP and optical networks, allowing researchers to address key challenges such as traffic aggregation, latency reduction, cost efficiency, and support for advanced applications. We provide guidelines for utilizing this benchmark network, enabling users to evaluate and enhance their solutions for AI-driven network management, ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication. By sharing this detailed and practical benchmarking network, we seek to foster innovation and collaboration within the optical network community, driving forward the capabilities and performance of future communication networks. </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.