Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
638
datasets available to search
ShareScore release 0.9.0
Dataset results
638 results for “Oscillation”
Cortical oscillations and interareal synchronization as a preparatory activity for postural response
<p>The EEG data of each participant is composed by three subset: Condition1(Perturbation), Condition2(No Perturbation), and Time. The analysis was performed by MATLAB software (.mat). </p>
Data for: Climatic oscillation promoted diversification of spinous assassin bugs during Pleistocene glaciation
<p>Insect speciation is among the most fascinating topics in evolutionary biology; however, its underlying mechanisms remain unclear. Allopatric speciation represents one of the major types of speciation and is believed to have frequently occurred during glaciation periods, when climatic oscillation may have caused suitable habitats to be fragmented repeatedly, creating geographical isolation among populations. However, supporting evidence for allopatric speciation of insects in East Asia during the Pleistocene glaciation remains lacking. We aim to investigate the effect of climatic oscillation during the Pleistocene glaciation on the diversification pattern and evolutionary history of hemipteran insects and to test the hypothesis of Pleistocene species stability using spinous assassin bugs <em>Sclomina</em> (Hemiptera: <span>Reduviidae</span>), a small genus widely distributed in southern China but was lately found to have cryptic species diversity. Here, using the whole mitochondrial genome (mitogenome) and nuclear ribosomal RNA genes, we investigated both interspecific and intraspecific diversification patterns of spinous assassin bugs. Approximate Bayesian computation, ecological niche modeling and demographic history analyses were also applied to understand the diversification process and driven factors. Our data suggest that the five species of <em>Sclomina</em> are highly diverged, despite three of them currently being cryptic. Speciation occurred during Pleistocene when suitable distribution areas were possibly fragmentated. Six phylogeographic groups in the type species <em>S. erinacea</em> were identified, among which two groups underwent expansion during early Last Glacial Period and after Last Glacier Maximum. Our analyses suggest that this genus may have experienced climate-driven habitat fragmentation and post-glacial expansion in the Pleistocene, promoting allopatric speciation and intraspecific diversification. Our results reveal underestimated species diversity in a small insect group and illustrate a remarkable example of allopatric speciation of insects in East Asia promoted by Pleistocene climatic oscillations. These findings provide important insights into the speciation processes and aid the conservation of insect species diversity.</p>
Data release for "Measurements of neutrino oscillation parameters from the T2K experiment using 3.6E21 protons on target"
<p>This archive contains the electronic version in ROOT format of the measurements of oscillation parameters in the paper "Measurements of neutrino oscillation parameters using 3.6 \times 10^{21} protons on target with the T2K experiment". Its arxiv identifier is <a href="https://arxiv.org/abs/2303.03222">arXiv:2303.03222 [hep-ex]</a>, and Published in <a href="https://doi.org/10.1140/epjc/s10052-023-11819-x"><em>Eur. Phys. J. C</em> <strong>83</strong>, 782 (2023)</a>.</p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>Both Bayesian and frequentist results are provided, with details of each analysis provided in the paper. All published oscillation parameters are provided, with 2D confidence/credible regions and 1D DeltaChi^2 and posterior probability density distributions. The Bayesian and frequentist results are separated in two different files ("Bayesian_DataRelase.root" and "Frequentist_DataRelease.root"), and an a tag in the TGraph and histogram names also allow to differentiate them: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. For the 1D distributions, the posteriors are Bayeisan results and the DeltaChi^2 are frequentist results.</p> <p>Results for each mass hierarchy hypothesis are provided, denoted "NH" for normal hierarchy and "IH" for inverted hierarchy. The Bayesian file also includes the results marginalised over the mass hierarchy, denoted by the tag "both" in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted hierarchy: the Bayesian results are in term of #Deltam^{2}_{32} for both normal (NH) and inverted (IH) hierarchies, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NH, and |#Deltam^{2}_{31}| for the IH.</p> <p>When employed, the constraint on theta13 from reactor experiment results corresponds to the value in the PDG 2019 summary table: sin^2(theta_13)=(2.18+-0.07) x 10^{-2}. This is commonly referred to as "the reactor constraint".<br>Results marked "woRC" are without this reactor constraint, and "wRC" are with the reactor constraint.</p> <p>A glossary is provided at the end of this readme.</p> <p>Two example ROOT macros ("Bayesian_example.cpp" and "Frequentist_example.cpp") showcase how to extract information from the data release. These produce pdf files of the results that can be directly compared to the "*ref.pdf" files for validation.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass hierarchy tag.<br>For the frequentist results, there's an additional "FC" tag, marking if critical DeltaChi^2 values have been computed with Feldman-Cousins ("FC") or using Wilks' theorem (constant DeltaChi^2).</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).<br>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_bestfit</p> <p>The best fit markers and contour lines are computed for each MH *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass hierarchy considered. There is only one exception, some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MH to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMH" in its name to distinguish it from the others.</p> <p>For larger confidence/credible exclusion regions (e.g. 99.7%) and when the Bayesian analysis shows the result for dm2 for both hierarchies, the regions may be split in to discontinuous regions. They are named "_0" and "_1", and the value on the y-axis denotes dm^{2}_{23}, from which the hierarchy can be deduced. The examples show examples of how this can be acheived.</p> <p>**************************************<br>*** 1D plots<br>**************************************<br>Objects of the form<br>h1D_var<chi2,posterior>_<wRC,woRC>_<NH,IH><br>are TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var".</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass hierarchy:<br>- 1D DeltaChi^2 plots use a global minimum over both hierarchies<br>- Each 1D posterior probability plot integrates to unity *individually*</p> <p>**************************************<br>***** Additional notes for frequentist results<br>**************************************<br>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and not the Feldman-Cousins method. They therefore have only approximate coverage.<br>For the 2D distributions, only {sin^2(theta_23), deltaCP} with reactor constraint were computed using the Feldman-Cousins method, and are expected to have proper coverage. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.<br>Additionally, those extra regions using Feldman-Cousins method are provided with two conventions regarding the best fit used to evaluate them. The TGraphs with an extra tag "globalMH" use a best fit over both MH hypothesis. The ones without this extra tag use the best fit obtained in each MH to compute the confidence regions for this MH.</p> <p>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP (with reactor constraint "wRC" case only):<br>grCritical_{variable}chi2_wRC_{MH}_conf{CL}<br> variable: th23, dCP<br> MH: NH, IH<br> CL: 68, 90, 955, 997</p> <p>To obtain the FC-corrected confidence interval in those 2 cases for a given confidence level, take the intersection of grCritical with the corresponding 1D histogram. This is shown in the example macros.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the choice of hierarchy is given by the sign:<br> dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br> dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>For the Jarlskog invariant, the prior on deltaCP is either flat in deltaCP, or flat in sindeltaCP ("flatsindcp")</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties, e.g. in "h1D_dCPposterior_wRC_IH" around delta CP=-1.47.</p> <p>Plots with "_bestfit" appended signify the point in the space with the highest posterior density, and is not necessarily the global minimum of the test-statistic.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce asymmetric credible intervals. The root macro "Bayesian_example.cpp" shows a method to do this.</p> <p>**************************************<br>***** Glossary<br>**************************************</p> <p>"RC" - Reaction Constraint from PDG 2019 sin^2(theta_13)=(2.18+-0.07) x 10^{-2}.<br>"wRC" - With Reactor Constraint<br>"woRC" - Without Reactor Constraint<br>"FC" - Feldman-Cousins<br>"NH" - Normal Hierarchy<br>"IH" - Inverted Hierarchy<br>"both" - Marginalised over normal and inverted hierarchy<br>"cred" - Credible interval<br>"conf" - Confidence interval<br> "68" - 68% (1 sigma)<br> "90" - 90%<br> "955" - 95.5% (2 sigma)<br> "997" - 99.7% (3 sigma)<br>"chi2" - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed with Feldman-Cousins</p> <p>"th13" - sin^2(theta_13)<br>"th23" - sin^2(theta_23)<br>"dCP" - delta CP<br>"dm2" - Delta m^2_{23} (NH), |Delta m^2_{13} (IH)| for confidence intervals; used in frequentist analysis.<br>"dm32" - Delta m^{2_{23} regardless of hierarchy; in the Bayesian analysis Delta m^2_{23} is always plotted.<br>"jarlskog" - Jarlskog invariant, only in Bayesian analysis<br>"flatsindcp" - Flat in sin delta CP</p>
Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability
<p>Pulse Profile Modeling of Thermonuclear Burst Oscillations I: The Effect of Neglecting Variability</p>
Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations
<pre>We discuss the stability of the neutrino oscillation parameters at low energy scale including self-complementarity (SC) relations among mixing angles under radiative corrections with the variation of SUSY breaking scale ($m_s$) in both normal and inverted hierarchical cases. We observe that the neutrino oscillation parameters including the SC relation maintains stability at the electroweak scale within $1\sigma$ range of the latest global fit data. NH case maintains more stability than IH case. All the numerical values related to the absolute neutrino masses viz., $\Sigma |m_i|$, $m_{\beta}$ and $m_{ \beta \beta}$ are found to lie below the observational upper bound.</pre>
Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations
<pre>We discuss the stability of the neutrino oscillation parameters at low energy scale including self-complementarity (SC) relations among mixing angles under radiative corrections with the variation of SUSY breaking scale ($m_s$) in both normal and inverted hierarchical cases. We observe that the neutrino oscillation parameters including the SC relation maintains stability at the electroweak scale within $1\sigma$ range of the latest global fit data. NH case maintains more stability than IH case. All the numerical values related to the absolute neutrino masses viz., $\Sigma |m_i|$, $m_{\beta}$ and $m_{ \beta \beta}$ are found to lie below the observational upper bound.</pre> <pre> </pre> <pre> </pre>
Stability of neutrino oscillation parameters at low energy scale with the variations of SUSY breaking scale under Renormalisation Group Equations
<pre>We discuss the stability of the neutrino oscillation parameters at low energy scale including self-complementarity (SC) relations among mixing angles under radiative corrections with the variation of SUSY breaking scale ($m_s$) in both normal and inverted hierarchical cases. We observe that the neutrino oscillation parameters including the SC relation maintains stability at the electroweak scale within $1\sigma$ range of the latest global fit data. NH case maintains more stability than IH case. All the numerical values related to the absolute neutrino masses viz., $\Sigma |m_i|$, $m_{\beta}$ and $m_{ \beta \beta}$ are found to lie below the observational upper bound.</pre>
Dataset Hadler et al., 2023: Gamma-Oscillation Plasticity Is Mediated by Parvalbumin Interneurons
<p>Dataset accompanying Hadler et al., 2023. Contains three files sufficient to reproduce the findings of the study:</p> <p>Gamma-Plasticity_Data-Dictionary.xlsx: A dictionary file supplementing the corresponding data file "Gamma-Plasticity_Data.xlsx". Explanations for column headers and variables are provided for the respective spreadsheets.</p> <p>Gamma-Plasticity_Data.xlsx: Sheet-by-sheet presentation of the descriptive data presented in each figure (including supplementary figures) of the manuscript.</p> <p>Gamma-Plasticity_Statistics-Report.xlsx: Sheet-by-sheet reporting of the statistical evaluation presented in each figure (including supplementary figures) of the manuscript. Re-edited from previous version due to errors in the Q-Q-plots in sheet Fig. S4.</p>
Dataset for: "Skilful predictions of the Summer North Atlantic Oscillation"
<p>Met Office DePreSys3 climate prediction data used in the generation of the figures in the paper "Skilful predictions of the Summer North Atlantic Oscillation" submitted to Communications Earth & Environment.</p>
Effect of Modulating Gamma Oscillations Using tACS
ClinicalTrials.gov study NCT03412604. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Causal Role of Top-Down Theta Oscillations in Prioritization
ClinicalTrials.gov study NCT06252532. IPD Sharing: YES. Countries: 1. Publications: 2.
Chest Wall Oscillation for Asthma and COPD Exacerbations Trial (COAT)
ClinicalTrials.gov study NCT00181285. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Infant Forced Oscillations Technique (iFOT)
ClinicalTrials.gov study NCT04697251. IPD Sharing: NO. Countries: 1. Publications: 4.
Evaluation of High-Frequency Chest Wall Oscillation
ClinicalTrials.gov study NCT00717873. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Study to Evaluate the Efficacy of Intranasal Kinetic Oscillation Stimulation in the Preventive Treatment of Chronic Migraine
ClinicalTrials.gov study NCT03400059. IPD Sharing: NO. Countries: 2. Publications: 1.
A therapeutic small molecule enhances γ-oscillations and improves cognition/memory in Alzheimer’s disease model mice
Open the record for dataset details and reuse information.
Data from: Nonlinearities between inhibition and T-type calcium channel activity bidirectionally regulate thalamic oscillations
Open the record for dataset details and reuse information.
Data from: Ultra-slow oscillations in fMRI and resting-state connectivity: Neuronal and vascular contributions and technical confounds
Open the record for dataset details and reuse information.
Coupling and de-coupling of the El Niño Southern Oscillation to the supply of larval fishes to benthic populations in the Hawaiian Islands
Open the record for dataset details and reuse information.
Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.