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695 results for “topologies”
Topological kinetic crossover in a nanomagnet array
<p>Common explanations of equilibrium thermodynamics depend critically on ergodicity, i.e., the ability to explore all available states, but ergodic kinetics can be constrained by a system's topology. We report an experimental study of a model nanomagnetic array, in which such constraints visibly affect the behavior. In this system, magnetic excitations are connected in thermally active one-dimensional strings whose motion can be imaged in real-time. At high temperatures, the kinetics include the merging, breaking, and reconnecting of strings, resulting in the system transitioning between topologically distinct configurations. Below a crossover temperature, however, the string motion is dominated by simple changes in length and shape. In this low-temperature regime, the system is energetically stable because of the inability to explore the full set of possible topological configurations. This kinetic crossover suggests a generalizable conception of topologically broken ergodicity and limited equilibration.</p>
Replication Package for: Mapping Firms' Locations in Technological Space: A Topological Analysis of Patent Statistics
<p>This replication package contains the data and the code to generate the paper’s main results, as well as the Online Appendix, for “Mapping Firms’ Locations in Technological Space: A Topological Analysis of Patent Statistics” by Emerson G. Escolar, Yasuaki Hiraoka, Mitsuru Igami, and Yasin Ozcan (published in <em>Research Policy</em>, volume 52, issue 8, October 2023; full text available online at https://doi.org/10.1016/j.respol.2023.104821).</p>
Freeform topology optimized, dispersive nanostructures for field confinement
<p>Provides the 3D topology optimized nanostructures as STL files, as well as the Lumerical files used to validate their performance presented in the article "Time-Domain Topology Optimization of Arbitrary Dispersive Materials for Broadband 3D Nanophotonics Inverse Design" (https://doi.org/10.1021/acsphotonics.3c00572).</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 highlight that empirically observed phylogenetic imbalance is highly improbable under crBD, leading to systematic bias in data sets with limited information content.</p>
Quantized thermal and spin transports of dirty planar topological superconductors
<p>The module contains the code and data corresponding to the paper arXiv:2308.16908, titled 'Quantized thermal and spin transports of dirty planar topological superconductors'.</p>
Dissecting glial scar formation by spatial point pattern and topological data analysis
<p>These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis". </p> <p>Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline.</p> <p><strong>AUTHORS</strong><br> Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman</p> <p><strong>KEYWORDS</strong><br> Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia, </p> <p><br> <strong>1. STUDY DESCRIPTION </strong> <br> This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals. </p> <p><strong>2. EXPERIMENTAL CONDITIONS</strong><br> Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1 <br> <br> <strong>3. FILE DESCRIPTION</strong></p> <p><strong>- GT5X_Gfap_Iba1_NeuN.rar: </strong>Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters.</p> <p>Objective: Fluar 5x/0.25 M27<br> Scaling per pixel: 1.300 x 1.300 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 3 s<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 4 s<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 1 s<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 50 ms</p> <p>We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim.</p> <p><strong>- GT10X_Gfap_Iba1_NeuN.rar:</strong> Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters.</p> <p>Objective: ECM paln-NeoFluar 10x/0.30 M27<br> Scaling per pixel: 0.45 x 0.45 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 200 ms<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 250 ms<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 100 ms<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 10 ms</p> <p><br> We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.<br> <br> For 5x and 10x images, the following naming strings apply:</p> <p>GT5x: Research project identifier indicating the magnification<br> M01(n): Animal ID<br> 5D(n): Days post-ischemia. 0D refers to healthy (naive) animals. <br> Scene1(n): Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior.</p> <p><strong>- PointPatterns_10x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_10x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- PointPatterns_5x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_5x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- QupathProjects_5x.rar: </strong>QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository. </p> <p><strong>- QupathProjects_10x.rar:</strong> QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders. </p>
The limits of the constant-rate birth-death prior for phylogenetic tree topology inference
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Data from: Gene network topology drives the mutational landscape of gene expression
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Data from: Topological transitions of the generalized Pancharatnam-Berry phase
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Broadband localization of light at the termination of a topological photonic waveguide
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Phylogenomics of bivalvia using ultraconserved elements (UCEs) reveal new topologies for Pteriomorphia and Imparidentia
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Anatomical partitioning has little influence in topologies from Bayesian phylogenetic analyses of morphological data
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Interactions outside local patches contribute to the compound topology of plant-pollinator networks in fragmented dune slacks
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Data from: Emergent electrostatics in planar XY spin models: The bridge connecting topological order with broken U(1) symmetry
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Topological kinetic crossover in a nanomagnet array
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The implications of incongruence between gene tree and species tree topologies for divergence time estimation
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ALD growth of ultra-thin Co layers on the topological insulator Sb2Te3 (data)
<p>This dataset contains the raw data connected to all figures included in the "<em>ALD growth of ultra-thin Co layers on the topological insulator Sb<sub>2</sub>Te<sub>3</sub></em>" by E. Lango et at., Nano Research 13, 570 (2020): <a href="https://link.springer.com/article/10.1007/s12274-020-2657-4">https://link.springer.com/article/10.1007/s12274-020-2657-4</a></p>
Data set accompanying: Topology of the Warm plasma dispersion relation at the second Harmonic Electron Cyclotron Resonance Layer
<p>The Warm Plasma Dispersion Relation, for waves in the electron cyclotron resonance range of frequencies, can be cast into the form of a bi-quadratic equation for $N_\perp$, where the coefficients are a function of $N_\perp^2$ and an iterative procedure is required to obtain a solution. However, this iterative procedure is not well understood and fails to converge towards a solution at the second harmonic resonance layer. In particular at higher densities where the wave can couple to an electron Bernstein wave.<br> This paper focuses on a solution to the poor convergence of the iterative method, enabling determination of the topology of the dispersion relation around the second harmonic using a fully relativistic code for oblique waves.<br> A feed-forward controller is proposed with the ability to adjust the rotation of a step of $N_\perp^2$ within the complex plane, while also limiting the step-size.<br> It is shown that implementation of the controller stabilizes unstable solutions, while improving overall robustness of the iteration. This allows the evaluation of the coupling between the fast extraordinary mode and electron Bernstein waves at the second harmonic electron cyclotron resonance layer, for non-perpendicularly propagating waves.</p>
Link-operator parameterized boolean models from scale-free topologies
<p>See analysis on this dataset here: <a href="https://druglogics.github.io/brf-bias/random-scale-free-networks-analysis.html">https://druglogics.github.io/brf-bias/random-scale-free-networks-analysis.html</a></p>
Nanomechanical topological insulators with an auxiliary orbital degree of freedom - source data
<p>This data set contains the source data and the matlab scripts applied to obtain the results published in "Nanomechanical topological insulators with an auxiliary orbital degree of freedom". For details of the experimental procedure please refer to the main text of the article, as well as the supplementary information. </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.