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26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “non-stationary”
Data from: Novel Fourier quadrature transforms and analytic signal representations for nonlinear and non-stationary time series analysis
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Data from: A new method of Bayesian causal inference in non-stationary environments
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To Design an Experimental Intervention, 62 Youth Volleyball Players Were Divided Into an Experimental Group and a Control Group and Underwent 10 Weeks of Non-stationary Strength Training Versus Tradit
ClinicalTrials.gov study NCT06831721. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Inferring dynamic regulatory programs in non-stationary expression time courses with applications to early human neural development
GEO Series GSE127064. Homo sapiens. 59 samples. Type: Expression profiling by high throughput sequencing.
Modeling of non-stationary autoregressive alpha-stable processe
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer–Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.
Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus
In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatio-temporally dependent and the mixing system may be non-stationary. Here, we propose a DCA algorithm, that combines concepts of particle filters and Markov Chain Monte Carlo (MCMC) methods in order to separate non-stationary mixtures of spatially dependent Gaussian sources.
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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.