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4 results for “non‐stationary model”
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
Data from the article "The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"
<p>Data from the article:</p> <p>"The mitochondrial phylogeny of land plants shows support for Setaphyta under non-stationary substitution models"</p> <p>Filipe de Sousa, Peter Civáň, João Brazão, Peter G. Foster, Cymon J. Cox</p> <p> </p> <p>These data are divided in four folders:</p> <p>* 1_36_gene_nt_alignments_&_trees - contains 36 single gene nucleotide alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 2_36_gene_aa_alignments_&_trees - contains 36 single gene amino acid alignments and the corresponding trees inferred from a MCMC analysis on the program p4</p> <p>* 3_concatenated_alignments_&_trees - contains the nucleotide, codon-degenerate and amino acid alignments of 36 concatenated genes and the corresponding trees inferred from MCMC analyses on the programs p4 and phylobayes with composition homogeneous, tree-heterogeneous and site-heterogeneous models; trees correspond to figures S1-S7 on the online supplemental file.</p> <p>* 4_concatenated_ML_trees - contains the ML trees from the analyses of the concatenated datasets (nucleotide, codon degenerate and amino acid).</p> <p> </p> <p> </p> <p> </p>
Data for Standard Codon Substitution Models Overestimate Purifying Selection for Non-Stationary Data
<p>Codon-aligned, filtered alignments for Kaehler et al. (2016) (https://peerj.com/preprints/2218/). Please refer to preprint for preparation details.</p> <p>Data obtained from Ensembl (http://www.ensembl.org/) and antbase (http://antbase.org).</p> <p> </p> <p> </p>
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.
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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.