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Dataset results
638 results for “oscillations”
North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms
<p>Datatsets associated with "North Atlantic Oscillation (NAO) climate index hidden in ocean generated secondary microseisms". The data include the daily seismic cross-correlograms for station pairs located on land and at the seafloor offshore Ireland, 3D models used for the numerical simulations and the associated synthetic seismic data.</p>
A Daily Azores-Iceland North Atlantic Oscillation Index back to 1850
<p>A Daily resolution North Atlantic Oscillation Index from (01/01/1850) to (31/12/2014). The index is derived from the difference in atmospheric sea-level pressure values between the Azores and Iceland. The article describing the NAO index is currently under revision at Geoscience Data Journal.</p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
<p><span>Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by <em>Laminaria</em> <em>hyperborea</em>). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (<em>Gadus</em> <em>morhua</em>) and kelp and European lobster (<em>Homarus</em> <em>gammarus</em>), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.</span></p>
Coherent Charge Oscillations in a Bilayer Graphene Double Quantum Dot
<p>This repository contains the experimental data and the scripts for evaluating the data of the publication "Coherent Charge Oscillations in a Bilayer Graphene Double Quantum Dot".</p>
Dataset for "Understanding moisture variations in Madden-Julian Oscillation in NCAR CAM5.3"
<p>Dataset for two simulations using CAM5.3. Data are named with "CASE.vari.daily.nc". CASE is the simulation name, vari means the variable name. All data are daily output from 1992 to 2001.</p><p>1.CTRL: CAM5.3 control run.</p><p>2. BOTC: the experiment with stratiform heating which includes an upper-level heating and a lower-level cooling. </p>
Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions
<p>Datafiles for figures in the paper:</p> <p><em>"Beating-free quantum oscillations in 2D electron gases with strong spin-orbit and Zeeman interactions"</em>, Phys. Rev. Research [<em>Accepted</em>]</p> <p>The datafiles relate to Figs. 2, 3, 10, 12, 14, 15, 16, and 17</p> <p>Figs. 1 and 13 contain schematics.</p> <p>Figs, 4, 6, and 8 are generated in a straightforwd manner using Eqs. (27), (34), and (40), respectively, using parameter values supplied in the figure captions.</p> <p>Fig. 5, is generated by Eq. (30) along with Eq. (29), using parameter values supplied in the figure captions.</p> <p>Fig. 7, is generated by Eq. (30) along with Eqs. (35) and (36), using parameter values supplied in the figure captions.</p> <p>Fig. 9, is generated by Eq. (30) along with Eqs. (41) and (42), using parameter values supplied in the figure captions.</p>
Data for Ultrahigh Quality Factor of a Levitated Nanomechanical Oscillator
<p>A levitated nanomechanical oscillator under ultra-high vacuum (UHV) is highly isolated from<br>its environment. It has been predicted that this isolation leads to very low mechanical dissipation<br>rates. However, a gap persists between predictions and experimental data. Here, we levitate a silica<br>nanoparticle in a linear Paul trap at room temperature, at pressures as low as 7 × 10−11 mbar. We<br>measure a dissipation rate of 2π ×69(22) nHz, corresponding to a quality factor exceeding 1010, more<br>than two orders of magnitude higher than previously shown. A study of the pressure dependence of<br>the particle’s damping and heating rates provides insight into the relevant dissipation mechanisms.</p>
Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"
<p>This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy". </p>
Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling
<p>Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling</p>
Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations
<p>Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphism in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in Catechol-O-methyltransferase (COMT) Val<sup>158</sup>Met and brain-derived neurotrophic factor (BDNF) Val<sup>66</sup>Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.</p>
Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans
<p>This dataset contains all data and codes that are used in the manuscript entitled "Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in <em>Caenorhabditis elegans</em>".</p> <p>The data include raw videos and intermediate data for optogenetic experiments of all strains, experimental conditions (illumination duration, illuminated region, fluid viscosity and date). Within the parent folder 'Videos', each subfolder represents data of a group of experiments using the same strain under the same condition, as indicated explicitly by the subfolder name. Within each subfolder, there are raw videos of freely moving worms perturbed by transient optogenetic perturbations and intermediate data which include locomotory information and the corresponding figure plots (kymographs) that were generated by analysing the raw videos with the image analysis software (also in the dataset)</p> <p>The codes include scripts for image data analysis and model simulations. The image data analysis codes include scripts specifically for generating phase portrait graphs, phase response curves, head oscillation stability plots, phase isochron map and vector field. The model simulation codes include scripts for model oscillators implementation, paramter estimation/optimization and simulations of optogenetic inhibition.</p>
Data from: Identification of neural oscillations and epileptiform changes in human brain organoids.
<p>Seurat object containing processed single-cell RNA sequencing data described in:</p> <p>Samarasinghe, R.A., Miranda, O.A., Buth, J.E. <em>et al.</em> Identification of neural oscillations and epileptiform changes in human brain organoids. <em>Nat Neurosci</em> <strong>24, </strong>1488–1500 (2021). <a href="https://doi.org/10.1038/s41593-021-00906-5">https://doi.org/10.1038/s41593-021-00906-5</a></p> <p>Additional raw and processed data can be accessed at the Gene Expression Omnibus under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165577">GSE165577</a>. </p> <p> </p>
Pressure oscillations data as storm precursors
<p>The folders contain the derived data product from REMS pressure that study the storm precursors.</p>
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
<p>Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling <em>Xenopus</em> egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.</p>
Cortical oscillations support sampling-based computations in spiking neural networks
<p>This archive contains the scripts for generating the data and figures and the data of the publication: "Cortical oscillations support sampling-based computations in spiking neural networks".</p> <p>The different parts of the material are grouped into separate archives to enable modular usage and can be individually downloaded as required:</p> <ul> <li>The archive spike-based-tempering_scripts.tgz contains all the scripts to reproduce the simulation data and figures.</li> <li>The file software.img contains the third-party software needed to execute the simulations of the current-based experiments.</li> <li>The archive current-based_experiments_data includes the scripts and the simulation data for the current-based experiments.</li> </ul>
Datasets produced by SCALE-GM in QBO-like oscillation experiments.
<p>Data produced by SCALE-GM and used in the manuscript submitted to the AMS journal Monthly Weather Review.</p> <p>Authors: <br> Hiroki Kashimura, Hisashi Yashiro, Seiya Nishizawa, Hirofumi Tomita, Kensuke Nakajima, Masaki Ishiwatari, Yoshiyuki O. Takahashi, and Yoshi-Yuki Hayashi<br> Corresponding Author:<br> Hiroki Kashimura (hiroki@gfd-dennou.org)<br> Manuscript Title:<br> Dependence of a QBO-like oscillation on time-step intervals in a non-hydrostatic general circulation model</p> <p>See README.txt for more details.</p> <p> </p>
Thin Filaments in an Average Magnetosphere: Pure Interchange vs. Ballooning Oscillations
<p>This paper describes magnetospheric waves of very long wavelength in thin magnetic filaments. We consider an average magnetospheric configuration with zero ionospheric conductance and calculate waves using two different formulations: classic interchange theory and ideal MHD. Classic interchange theory, which is developed in detail in this paper, is basically analytic and is relatively straightforward to determine computationally, but it can’t offer very high accuracy.</p> <p>The two formalisms agree well for the plasma sheet and also for the inner magnetosphere. The eigenfrequencies range over about a factor of seven, but the formulations generally agree with a root-mean-square difference between the logarithms of interchange and MHD frequencies to be $\sim 0.054$. The pressure perturbations in the classic interchange theory are assumed constant along each field line, but the MHD computed pressure perturbations along the field line vary in a range $\sim 30 \%$ in the plasma sheet but are larger in the inner magnetosphere. The parallel and perpendicular displacements, which are very different in the plasma sheet and inner magnetosphere, show good qualitative agreement between the two approaches. In the plasma sheet, the perpendicular displacements are strongly concentrated in the equatorial plane, whereas the parallel displacements are spread through most of the plasma sheet away from the equatorial plane; and can be regarded as buoyancy waves. In the inner magnetosphere, the displacements are more sinusoidal and are more like conventional slow modes. The different forms of the waves are best characterized by the flux tube entropy $PV^\gamma$.</p>
Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves
<p>Data set of manuscript entiteld Sigma Oscillations Protect or Reinstate Motor Memory Depending on their Temporal Coordination with Slow Waves</p> <p>EEG files : Brainvision</p> <p>*.dat; *.edf : eeg files of the experimental nap </p> <p>*.vhdr: corresponding header files</p> <p>*.vmrk: corresponding marker files time starting from recording sample</p> <p>*._Pauselog.txt: markers with condition in the stimulation computer time</p> <p>Sleep Scores: </p> <p>Scores by the sleep technician (one file per participants). </p> <p>Surveys:</p> <p>General survey (first screening) and complete survey (for inclusion). Code correspondance is provided in ID_correspondance.txt</p> <p>BehavData:</p> <p>For each participant, a text file with all the cues and responses logged for each tasks. *_raw.txt are present when an issue arised during experiment and a task needed to be re-started. </p> <p> </p>
A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots
<p>This dataset contains data and code to replicate main and supplemental figures for the related article published in Matter:</p> <p>Title: A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots</p> <p>DOI: 10.1016/j.matt.2022.06.002</p> <p>In the article we introduce a simple and compact soft valve with intentional hysteresis, analogous to an electronic relaxation oscillator. By integrating the valve with a soft actuator, we transform a continuous inflow to cyclic activation. Importantly, we show that our circuits can activate up to five actuators in various sequences, and that we can physically reprogram the activation order by varying the (initial) conditions in the fluidic circuit. Moreover, we show the feasibility of our approach under more realistic conditions by building a four-legged robot.</p> <p>This dataset contains measurement data and simulation files.</p> <p>The data are recorded (in human-readable format) from experiments on our fluidic circuits (e.g., pressure, flow data), and are accompanied by MATLAB scripts for data processing as well as generating figures.</p> <p>The simulation files are MATLAB and LTspice files for simulating our fluidic circuits making use of the analogy with electronic circuits. For more involved parameter sweeps we generate, run, and post-process LTspice input and result files using MATLAB. More details and instruction for use are provided in the included readme.txt files.</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.