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638 results for “oscillations”
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class III)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and <strong>high-order interactions</strong> (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (third), whose name explicitly indicates in which state variable occurs each coupling. In these case, Lineary-nonlinearx means that the linear coupling occurs in <strong>variable y</strong> (class II) whereas the non-linear coupling occurs in <strong>variable x</strong> (class III).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class II)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and<strong> high-order interaction</strong>s (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (fourth), whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlinearx means that the linear coupling occurs in variable <strong>y</strong> (class II) whereas the non-linear coupling occurs in variable <strong>y</strong> (class II).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</p>
Propagating Gottesman-Kitaev-Preskill states encoded in an optical oscillator
<p>Gottesman-Kitaev-Preskill (GKP) qubit in a single Bosonic harmonic oscillator is an efficient logical qubit for mitigating errors in a quantum computer. The entangling gates and syndrome measurements for quantum error correction only require noise-robust linear operations, a toolbox that is naturally available and scalable in optical system. To date, however, GKP qubits have been only demonstrated at mechanical and microwave frequency in a highly nonlinear stationary system. In this work, we realize a GKP state in propagating light at the telecommunication wavelength and demonstrate homodyne measurements on the GKP states without loss corrections. Our states do not only show nonclassicality and non-Gaussianity at room temperature and atmospheric pressure, but the propagating wave property also permits large-scale quantum computation with strong compatibility to telecommunication technology.</p>
Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture
<p>Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.</p>
Code from: Effect of fluid elasticity on the emergence of oscillations in an active elastic filament
<p>Many microorganisms propel through complex media by deformations of their flagella. The beat is thought to emerge from interactions between forces of the surrounding fluid, passive elastic response from deformations of the flagellum, and active forces from internal molecular motors. The beat varies in response to changes in the fluid rheology, including elasticity, but there is limited data on how systematic changes in elasticity alter the beat. This work analyzes a related problem with fixed-strength driving force: the emergence of the beating of an elastic planar filament driven by a follower force at the tip in a viscoelastic fluid. This analysis examines how the onset of oscillations depends on the strength of the force and viscoelastic parameters. Compared to a Newtonian fluid, it takes more force to induce instability in viscoelastic fluids, and the frequency of the oscillation is higher. The linear analysis predicts that the frequency increases with the fluid relaxation time. Using numerical simulations, the model predictions are compared with experimental data on frequency changes in bi-flagellated alga <em>Chlamydomonas reinhardtii</em>. The model shows the same trends in response to changes in both fluid viscosity and Deborah number and thus provides a possible mechanistic understanding of the experimental observations.</p>
Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"
<p>Data for Spaulding-Astudillo and Mitchell (2024a), "A simple model for the emergence of relaxation-oscillator convection"</p> <p>The main directories have a common nomenclature: e.g., minimal_1p1S_FSC_335K_N7200, where 1p1S indicates a solar constant 1.1 times the present day (1360), FSC indicates that it is a full-sky radiation run, 335 K is the surface temperature (fixed), and N7200 refers to the relaxation timescale of 7200 seconds for CAPE in the quasi-equilibrium closure to the convection scheme. </p> <p>In every directory, each .nc file contains 5 years of model output. There are 5 types of .nc files, which correspond to different output streams of ECHAM6. The main output stream is ..._echam.nc, which has daily-averaged output.</p> <p>The tendencies for gross deposition (gdep_ls) and gross condensation (gcnd_ls; both with units of kg/m2/s) from the large-scale scheme are in ..._g1am.nc. This is the mean-value stream, which records daily-averaged values. </p> <p>The tendencies for gross condensation in convective updrafts are stored in the variable "ddf13" in ..._debugs.nc (units of kg/m2/s). This is another output stream, which records hourly values usually for debugging purposes. The total pressure as a function of height (units of Pa) is stored in the variable "ddf8". Also, the implied surface heat source/sink in the surface energy budget that keeps the surface temperature fixed in time is tracked in the variable "zdf8" (units of W/m2). </p> <p>The experimental range of surface temperatures is 290-360 K, which remain fixed as a function of time in the simulations through the use of an artificial heat sink. The simulations are from the ECHAM6 climate model, which we ran in single-column mode. </p>
Data for "Association of western US compound hydrometeorological extremes with Madden-Julian oscillation and ENSO interaction"
<p>The files in this dataset are used to reproduce the figures in a paper entitled "Association of western US compound hydrometeorological extremes with Madden-Julian oscillation and ENSO interaction", submitted to <em>Communications Earth & Environment</em>.</p> <p>The data are in the NetCDF format including the climatological frequency of compound precipitation and temperature extremes over the western US and their changes associated with Madden-Julian oscillation and ENSO. Reanalysis ERA5 data contain the geopotential height, integrated vapor transport, and temperature advections. </p>
Theta oscillations coordinate grid-like representations between ventromedial prefrontal and entorhinal cortex
<p>This dataset contains iEEG neural recordings and behavior movement direction in a navigation task from human subjects undergoing inpatient monitoring for seizure localization. The experimental design, including an explanation of metrics of interest, is detailed in Chen et al (2018) <em>Current Biology</em> and Chen et al (2021) <em>Science Advances</em>. EXAMPLE electrode data from the vmPFC and EC ROIs are included. These data are used for grid-like modulation of theta power in vmPFC and EC.</p>
Demonstration videos of Erkki Kurenniemi's DICO - a digitally controlled oscillator for composer Osmo Lindeman
<p>The set contains demonstration videos Erkki Kurenniemi's DICO - a dgitally controlled oscillator shot at the UHMRL - University of Helsinki Music Research Laboratory and Electronic Music Studio on May 20, 2012 by Mikko Ojanen.</p> <p>DICO is a twelve-step sequencer with digital memory custom-designed by FInnish electronic musical instrument designer Erkki Kurenniemi for composer Osmo Lindeman in 1969.</p> <p>See also: DICO - digitally controlled oscillator (1969) by Erkki Kurenniemi: a demo by Mikko Ojanen. <a href="https://vimeo.com/80931815">https://vimeo.com/80931815</a></p> <p> </p> <p>For more information about the DICO see:</p> <p>Ojanen, Mikko. (2020). User Stories of Erkki Kurenniemi's Electronic Musical Instruments, 1961–1978 (20201127_01) [Zenodo]. <a href="https://doi.org/10.5281/zenodo.4018719">https://doi.org/10.5281/zenodo.4018719</a></p> <p>Suominen, Jari (2021). The user manual of DIR-DICO. [Link to the online resource to be added]</p> <p> </p>
Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators
<p>These are time-domain and frequency-domain data recorded from 4 realizations of a chaos-generating integrated circuit as a function of a control voltage and various input signals. They are provided to support replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of chaotic oscillators, distributed sensing and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data: Minati L, Tokgoz KK, Ito H, Distributed sensing via the ensemble spectra of uncoupled electronic chaotic oscillators, <em>Chaos Solitons & Fractals,</em> vol. 155, 111749, 2022, DOI 10.1016/j.chaos.2021.111749</p> <p>This work was partially supported by JSPS KAKENHI Grant Number 19H02191. Device realization was also supported by SCOPE (No. 0159-0013) from the Japan Ministry of Internal Affairs and Communications (MIC), with the assistance of the National Institute of Information and Communications Technology (NICT), and through the activities of VDEC, the University of Tokyo, in collaboration with Cadence Design Systems and Mentor Graphics.</p>
Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells
<p>Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.</p>
The model data of Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation
<p>This is the model data of "Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation". The data includes the last 20 years data of control run (CTRL), the 20 years data of TP–T experiment, and the wave activity flux difference between ensemble means of TP–T and CTRL. 2D is two dimensions. 3D is three dimensions.</p>
Beatings of ratchet current magneto-oscillations in GaN-based grating gate structures: Manifestation of spin-orbit band splitting
<p>OPEN DATA related to the research publication:</p> <p>Sai, P., Potashin, S. O., Szoła, M., Yavorskiy, D., Cywiński, G., Prystawko, P., ... & Kachorovskii, V. Y. (2021). <strong>Beatings of ratchet current magneto-oscillations in GaN-based grating gate structures: Manifestation of spin-orbit band splitting.</strong> <em>Physical Review B</em>, <em>104</em>(4), 045301 [arXiv:2102.12791].</p> <p><em>Abstract</em>: We report on the study of the magnetic ratchet effect in AlGaN/GaN heterostructures superimposed with a lateral superlattice formed by a dual-grating gate structure. We demonstrate that irradiation of the superlattice with a terahertz beam results in the <em>direct</em> ratchet current, which shows giant magneto-oscillations in the regime of Shubnikov–de Haas oscillations. The oscillations have the same period and are in phase with the resistivity oscillations. Remarkably, their amplitude is greatly enhanced as compared with the ratchet current at zero magnetic field, and the envelope of these oscillations exhibits large beatings as a function of the magnetic field. We demonstrate that the beatings are caused by the spin-orbit (SO) splitting of the conduction band. We develop a theory which gives a good qualitative explanation of all experimental observations and allows us to extract the SO splitting constant α<sub>SO</sub>=7.5±1.5 meVÅ. We also discuss how our results are modified by plasmonic effects and show that these effects become more pronounced with decreasing the period of the grating gate structures down to submicrons.</p>
A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures
<pre>This repository contains the data for "A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures". </pre>
Animation of Spin Up 3rd Eigenvector for 2-D Simple Harmonic Oscillator Potential (MP4)
<p>MP4 video animation of 3rd Eigenvector for a 2-D SHO potential. This animation accompanies the paper "Real Wave Quantum Mechanics: Solutions for 2-D Simple Harmonic Potential Well". The animation shows the repeated multiplication of the Eigenvector by the (complex) normalized eigenvalue. The magnitude of the FRs are unchanged but their phase drifts in the same direction as the FR spin.</p>
Animation of 13th Eigenstructure (Spin Down) for Simple Harmonic Oscillator Potential (MP4)
<p>MP4 video animation of 13th Eigenvector for a 2-D SHO potential. This animation accompanies the paper "Real Wave Quantum Mechanics: Solutions for 2-D Simple Harmonic Potential Well". The animation shows the repeated multiplication of the Eigenvector by the (complex) normalized eigenvalue. The magnitude of the FRs are unchanged but their phase drifts in the same direction as the FR spin.<br> </p>
Dual-comb thin-disk oscillator raw data
<p>Dual-comb thin-disk oscillator raw data:</p> <ul> <li>"DCS 625MSa.h5" Full spectrum, sampled at 625 MSa/s (dt=1.6ns)</li> <li>"DCS etalon 313msa.h5" Fabry-Pérot-etalon (fig. 7), sampled at 312.5 MSa/s (dt=3.2ns)</li> <li>"DCS acetylene 625msa.h5" Acetylene (fig. 8), sampled at 625 MSa/s (dt=1.6ns)</li> </ul> <p>Data import with Python:</p> <pre>import h5py import numpy as np trace = np.array(h5py.File(filename, "r")['Waveforms']['Channel 1']['Channel 1Data'].__array__())</pre> <p>A full description of data evaluation method can be found in the supplementary information.</p>
DATASET J. Stat. Mech. (2022) 053209: "Virtual double-well potential for an underdamped oscillator created by a feedback loop"
<p><strong>Matlab Figures: </strong></p> <p>Fig_i.fig corresponds to the source file used to plot the figures of the article</p> <p><strong>Simulation and Model codes: Matlab script</strong></p> <p>Simu_and_Model.m is the code used to:</p> <p>1) Simulate trajectories of the underdamped cantilever in the virtual double well potential with hysteresis at the comparator switches.</p> <p>2) Compute from the simulation the steady state temperature and the crossing rate.</p> <p>2) Compute and plot the model's prediction regarding the steady state temperature and the crossing rate. </p> <p><strong>static_hyst_XX.mat : Matlab dataset</strong></p> <p>It is experimental data of the underdamped cantilever in vacuum trapped in a double well potential with hysteresis at the switches. It is not the data used in the article. Indeed:</p> <p>1) The velocity distributions of the article were the ones of the analogical implementation of the feedback loop with hysteresis. Here we propose as an example a more recent dataset from the numerical implementation of the feedback loop with a chosen hysteresis.</p> <p>2) Besides the in this dataset the cantilever is evolving in vacuum so that the cooling effect observed is much more important for a given hysteresis.</p> <p>But as the data treatment to obtain the velocity distribution and the cooling observed are similar to the one detailed in the article, this dataset should be enough for any person interested.</p> <p>Each data file contains in particular: x the position in sigma units (z in the article), X_1 the wells centers position in sigma units (z_1 in the article), x0 the barrier position in sigma units (z_0 in the article), sigma_m the position variance calibrated before the experiment and sigma_protocol the position variance calibrated before every data file. </p> <p> </p> <p><strong>Analyse_velocity_distribution.m : Matlab Script</strong></p> <p>It is the code to treat datasets such as the one provided here "static_hyst". The code recover the data from the files and compute the velocity distributions for different distances between the wells. The velocity variance is deduced from the velocity distributions and matches the variance computed using the velocity power spectrum. The velocity and position spectrums are also displayed.</p> <p> </p>
Data set for the article "Self-oscillation and Synchronisation Transitions in Elasto-Active Structures"
<p>This is the data set for the article "Self-oscillation and Synchronisation Transitions in Elasto-Active Structures", </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2106.05721">https://doi.org/10.48550/arXiv.2106.05721</a> <p> </p> </td> </tr> </tbody> </table> <p>Is contains raw images and processed data from images used to describe the self-oscillations under study in this article.</p> <p>All zip files corresponds to the single chain experiment except "Double_chain_Experiment.zip".</p> <p> </p>
Distribution of blood flow oscillation across the Doppler shift evaluated by the proposed approach with local pressure test.
<p>A step-wise increase in local pressure is known to cause a gradual change in the parameters of capillary blood flow in the<br> upper layers of the skin, and can also affect the measurement results of various optical methods. The impact of the procedure has several subsequent effects, such as mechanical compression of vessels, and neurological and metabolic compensating mechanisms like pressure-induced vasodilation, which maintain the homeostasis of the skin during moderate levels of external pressure and tissue hypoxia. To examine how those effects are translated to the blood flow registering in different ranges of the Doppler spectra, we have developed a 3D-printed pressure distribution tool compatible with the developed sensor which was used, and equipped with a set of weights. </p> <p>During the main series of measurements, the weights were placed into the PDT in a step-wise manner to achieve the<br> following values of pressure applied: 10 mmHg, 30 mmHg, 90 mmHg, 150 mmHg, 210 mmHg. At the end of the procedure, the load was reduced back to 30 mmHg. Experiments were conducted with the participation of 7 healthy volunteers with 10 min LDF recording for each step. To estimate the prominence of the observed effects and substantiate the measuring routing, several preliminary experiments were also conducted where the set of values of pressure was applied with a step-wise increase and then decrease with about 2 min of LDF recordings for each step.</p> <p>Published in IEEE Transactions on Biomedical Engineering "Diagnosis of skin vascular complications revealed by time-frequency analysis and laser Doppler spectrum decomposition", Zherebtsov et al.</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.