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307 results for “nonlinearity”
Data of publication: "Collective atom-cavity coupling and nonlinear dynamics with atoms with multilevel ground states"
<p>The uploaded files contain the raw data of the measurements and simulations presented in <a href="https://doi.org/10.1103/PhysRevA.107.023714">https://doi.org/10.1103/PhysRevA.107.023714</a></p>
Nonlinear THz Control of the Lead Halide Perovskite Lattice - Experimental data
<p>Experimental data for the paper "<strong>Nonlinear THz Control of the Lead Halide Perovskite Lattice</strong>", published with open-access in <em>Science Advances</em> under <a href="https://doi.org/10.1126/sciadv.adg3856">https://doi.org/10.1126/sciadv.adg3856</a></p> <p>The data was measured at the Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society in Berlin.</p> <p>Contents:</p> <ul> <li>THz E-field data from Fig. 1</li> <li>THz-induced Kerr effect time domain data, fluence dependence, azimuthal angle dependence, and corresponding THz fields for MAPbBr3 and CsPbBr3 at room temperature from Fig. 2.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystals and thin films for room temperature, 180K and 80K from Fig. 3.</li> <li>THz-induced Kerr effect experimental data and simulated Kerr signals from Fig. 4.</li> <li>THz-induced Kerr effect time domain data for MAPbBr3 single crystal at different THz fluences from Fig. 5a.</li> </ul> <p>Raw data and data of the Supplementary Materials (SM) will be provided upon request. Please contact Maximilian Frenzel (frenzel@fhi-berlin.mpg.de) and Sebastian F. Maehrlein (maehrlein@fhi-berlin.mpg.de) for such a request or for general questions.</p>
Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn2Au
<p>Data for the publication "<strong>Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn<sub>2</sub>Au"</strong>, published in <em>Nat Commun</em> <strong>14</strong>, 6038 (2023). (https://doi.org/10.1038/s41467-023-41569-z).</p> <p>A preprint (2023) can be found on arxiv (https://doi.org/10.48550/arXiv.2305.03368).</p> <p>The datasets are provided for Figures 2-4.</p> <p>Files are provided as comma-separated text files with column headers. The value delimiter is comma " , ". The decimal separator is period " . "</p>
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
Dataset related to the publication "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176
<p>This folder contains the raw data from which the graphs in paper "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176, have been obtained.</p>
Dataset related to the publication "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086
<p>This folder contains the raw data from which the graphs in paper "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086, have been obtained.</p>
Nonlinear mechanosensation in fiber networks
<p>Dataset corresponding to the underlying numerical and experimental data of the research article "Nonlinear mechanosensation in fiber networks". </p> <p>This repository contains four folders containing the data used to produced the figures shown in the article:<br>- EXPERIMENTS.zip : microrheology measurements in biopolymer networks<br>- MACRO.zip : macroscopic loading of disordered fiber networks (simulations results)<br>- MICRO.zip : local probing of disordered fiber networks (simulations results)<br>- Spatial-NL.zip : local probing of disordered fiber networks, with records of the networks spatial deformations (simulations results)<br>The README.txt document provides a detailed description of the content, including files naming convention and data description.</p> <div> <div> <div> <p>We would like to acknowledge that this project has received funding (E.B. and C.P.B.) from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 891217 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project ID 201269156 - SFB 1032 (Project B12). P.R. is supported by France 2030, the French National Research Agency (ANR-16-CONV-0001) and the Excellence Initiative of Aix-Marseille University - A*MIDEX. M.G. and H.Y. acknowledge support from NIH Grant No. 1R01G140108.</p> </div> </div> </div>
Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
<p>Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: <em>Ardisia escallonioides</em> (Pascarella & Horvitz, 1998), <em>Silene acaulis</em>, and <em>Bistorta vivipara</em> (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.</p>
Accounting for nonlinear responses to traits improves range shift predictions
<p>Accurately predicting species' range shifts in response to environmental change is paramount for understanding ecological processes and global change. In synthetic analyses, traits emerge as significant but weak predictors of species' range shifts across recent climate change. These studies assume linear responses to traits, while detailed empirical work often reveals trait responses that are unimodal and contain thresholds or other nonlinearities. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and therefore may be under-powering traits to predict range shifts. We evaluate the predictive performance of approaches that can capture nonlinear relationships (ridge-regularized linear regression, support vector regression with linear and nonlinear kernels, and random forests). We apply our models using six multi-decadal range shift datasets for plants, moths, marine fish, birds, and small mammals. We show that nonlinear approaches can perform better than least-squares linear modeling in reproducing historical range shifts. Consistent with expectations, we identify dispersal and climatic niche traits as primary determinants of distribution shifts. Traits identified as important predictors and the direction of trait effects are generally consistent across models but there are notable exceptions. Among important predictors, there are more consistent responses to climatic niches than dispersal ability. Modest improvements in predictability when accounting for nonlinearities and interactions and the overall low amount of variance accounted for by trait predictors suggest limits to trait-based statistical predictive frameworks.</p>
Time series generated by nonlinear Langevin equation
<p>Datasets used in papers:</p> <p>Telesca L. and Z. Czechowski, Fisher–Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Entropy 2023, 25, 1650.</p> <p>Czechowski Z. and L. Telesca, Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Chaos, Solitons and Fractals 183 (2024), 114927</p>
Datasets for ``Leading-order nonlinear gravitational waves from reheating magnetogeneses''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table II of the paper "Leading-order nonlinear gravitational waves from reheating magnetogeneses" by Yutong He, Axel Brandenburg, and Alberto Roper Pol. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
Reproducing nonlinear seismic response from in-situ soil dynamic parameters: application to the Delaney Park Downhole Array, Alaska
<p>The zip file named 'seismic data at DPDA.zip' contains all the seismic data used in our study.</p> <p>The DEEPSOIL file named 'ground response analysis at DPDA.dp' contains the numerical models used in our study.</p>
Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters
<p>Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well. </p>
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Image sensing with multilayer, nonlinear optical neural networks
<p>This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”.</p> <p>This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper:</p> <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>
Data from: Machine learning without a processor: Emergent learning in a nonlinear analog network
<p>The capabilities of digital artificial neural networks grow rapidly with their size, however the time and energy required to train them does as well. The tradeoff is far better for Brains, where the constituent parts (neurons) update their analog connections in ignorance of the actions of other neurons, eschewing centralized processing. Recently introduced analog electronic <em>contrastive local learning networks </em>(CLLNs) share this important decentralized property. However their capabilities were limited because existing implementations are linear. In this dataset we include experimental demonstrations of a nonlinear CLLN, establishing a new paradigm for scalable learning. Included here are data and scripts required to generate figures 2-6 of the manuscript titled "Machine learning without a processor: Emergent learning in a nonlinear analog network".</p>
Nonlinear method to assess autonomic modulation during controlled breathing: dataset
<p><strong>Please, cite this article if using dataset:</strong></p> <p><strong>A. Uryga, M. Najdek, M. Najda, C. Mataczyński and T. Buchner, "Nonlinear Method to Assess Autonomic Modulation During Controlled Breathing," <em>2024 13th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)</em>, ZARAGOZA, Spain, 2024, pp. 1-2, doi: 10.1109/ESGCO63003.2024.10766976.</strong></p> <p> </p> <p><strong>General information:</strong></p> <p>This database contains data from 34 young healthy volunteers (median age: 22 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2023 and January 2024.</p> <p>The study was approved by the bioethical committee (KB-179/2023/N).</p> <p>We would like to thank Prof. Magdalena Kasprowicz, the head of the Brain Physics group (https://www.brainlab.pwr.edu.pl/), for her help and support during the research.</p> <p>The study was support by National Science Centre, Poland (UMO-2022/47/D/ST7/00229).</p> <p><strong>Signal recordings description:</strong></p> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (CNAP, CNSystems Medizintechnik GmbH, Graz, Austria, in n = 19 subjects, and Finapres Nova, FMS Medical Systems, in n = 15 subjects). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration, and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA).</li> <li><strong>Protocol</strong>: After a resting epoch lasting at least 5 minutes, a controlled breathing session was initiated with three 5-minute recordings at respiratory rates of 6, 10, or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <p><strong>Data description:</strong></p> <ul> <li><strong>Metadata</strong>: Including device, gender (male M, female F), and age</li> <li><strong>Autonomic Nervous System parameters</strong>: Including <ul> <li>Phase-Rectified Signal Averaging (PRSA) - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart</li> <li>Entropy: Fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</li> <li>Joint Symbolical Analysis (JSA) - a method that identifies short-term repeated patterns in a signal (JSA_sym and JSA_diam)</li> </ul> </li> <li><strong>Physiological parameters</strong>: Including <ul> <li>Mean arterial blood pressure (ABP)</li> <li>Mean end-tidal carbon dioxide (EtCO2)</li> <li>Mean heart rate (HR)</li> </ul> </li> </ul>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</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.