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1,066 results for “bayesian”
Estimates of tidal-marsh bird densities using Bayesian networks
<p>This data set was generated by Wiest, WA, MD Correll, BG Marcot, BJ Olsen, CS Elphick, TP Hodgman, GR Gunterspergen, and WG Shriver. 2019. Estimates of tidal marsh bird densities using Bayesian networks. Journal of Wildlife Management 83:109-120 (paper available here <a href="https://doi.org/10.1002/jwmg.21567">https://doi.org/10.1002/jwmg.21567</a>) and was developed by the Saltmarsh Habitat and Avian Research Program (https://www.tidalmarshbirds.org/). The same data set has subsequently been used in other papers produced by our group.</p>
Data and code: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize
<p>Data and code supporting the research article: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize - M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T.K.D. Weber. This includes R code for the implementation of the SPASS phenology maize model; Jags in R to implement the Bayesian multi-level models .</p>
Bayesian Estimation on Microbial Electrochemical Time Profiles obtained by a High-Throughput Bioelectrochemical Device
<p>Bioelectrochemical systems (BESs) are attracting much attention, but the mechanisms are not yet fully clarified. One of the issues for the BESs research is the lack of a high-quality database that is developed through well-controlled experiments with a high-throughput data-collecting system. Here, we developed a new high-throughput potentiostat with 96 well plates with silk-screen-printed electrodes. With this potentiostat, we obtained 576 time-profiles of microbial current production for viewing the complex landscape of the parameters, namely, the redox mediator concentration and the electrode potential. This dataset contains the following materials:</p> <ul> <li>Current production profiles for <em>Shewanella </em>(Data with average of four data sets for impact of mediators.zip)</li> <li>Summary of the calculated values (statistics summary.csv)</li> <li>Python code for calculating the maximum slope (Slope EF.py)</li> <li>Python codes for Bayesian estimation (2D Bayesian Optimization mapping.py and 2D Bayesian Optimization_slice.py)</li> </ul>
Data from: Assessing bayesian phylogenetic information content of morphological data using knowledge from anatomy ontologies
<p>Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent 'parts', but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies—structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here we assess whether the proximity of ontology-annotated characters within an ontology predicts evolutionary patterns. To do so, we measure phylogenetic information across characters and evaluate if it is hierarchically structured by ontological knowledge—in much the same way as phylogeny structures across-species diversity. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to datasets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially structured by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that ontology does indeed structure phylogenetic information, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological datasets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may structure it: phylogeny, development, or convergence.</p>
Multiscale Bayesian Simulations Reveal Functional Chromatin Condensation of Gene Loci
<p>Chromatin, the complex assembly of DNA and associated proteins, plays a pivotal role in orchestrating various genomic functions. To aid our understanding of the principles underlying chromatin organization, we introduce Hi-C metainference, a Bayesian approach that integrates Hi-C contact frequencies into multiscale prior models of chromatin. This approach combines both bottom-up (the physics-based prior) and top-down (the data-driven posterior) strategies to characterize the 3D organization of a target genomic locus. We first demonstrate the capability of this method to accurately reconstruct the structural ensemble and the dynamics of a system from contact information. We then apply the approach to investigate the Sox2, Pou5f1, and Nanog loci of mouse embryonic stem cells using a bottom-up chromatin model at 1kb resolution. We observe that the studied loci are conformationally heterogeneous and organized as crumpled globules, favoring contacts between distant enhancers and promoters. Using nucleosome-resolution simulations, we then reveal how the Nanog gene is functionally organized across the multiple scales of chromatin. At the local level, we identify diverse tetranucleosome folding motifs with a characteristic distribution along the genome, predominantly open at cis-regulatory elements and compact in between. At the larger scale, we find that enhancer-promoter contacts are driven by the transient condensation of chromatin into compact domains stabilized by extensive inter-nucleosome interactions. Overall, this work highlights the condensed, but dynamic nature of chromatin in vivo, contributing to a deeper understanding of gene structure-function relationships.</p>
Data and supplementary information from: Sequential bayesian phylogenetic inference
<p>The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously certain results. Here, we formally develop and test the sequential approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior produced in the previous step. The sequential approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be more efficient computationally than the traditional joint approach when multiple models are being tested. We show that the sequential approach is identical in practice to the joint approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into time-calibrate phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of insect divergence times estimation using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Gamma+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different-node calibration strategies and show that both, the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies, impact divergence time estimates. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approach for divergence time estimation, including branch lengths estimation from other software.</p>
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
Improved estimation of the prevalence of bovine cysticercosis and the diagnostic test characteristics in the absence of a reference standard using Bayesian Latent Class models, the example of Jimma and Ambo Abattoirs, Ethiopia
<p>Bovine cysticercosis is an infection of cattle musculature with the cestode parasite of humans known as Taenia saginata. This bovine cysticercosis data was collected from two Ambattoirs in Ethiopia namely Ambo and Jimma. Dissection of the predilection site, Ag-ELISA, and meat inspection were the diagnostic methods employed. Cysticerci collected during dissection of the predilection site were also confirmed using multiplex PCR. </p>
Bayesian analysis of (3+1)D relativistic nuclear dynamics with the RHIC beam energy scan data
<p>This dataset contains the MCMC chain (LHD+HPP) without any constraints on the parameters for the (3+1)D Bayesian inference study for the RHIC beam energy scan program.<br>We also provide the nine trained emulator objects, which were generated with the code available at <a title="GPBayesTools-HIC: v1.1.0" href="https://doi.org/10.5281/zenodo.12807892" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12807892</a>.<br>The training data is given in pickle format as dictionaries for the training points. The first 1000 points in the files correspond to the Latin Hypercube design points and the last 100 points are points sampled from the posterior distribution.</p>
Unified EoS within Nambu–Jona-Lasinio description of hadronic matter from a Bayesian approach
<p># Introduction to the Unified Equations of State Dataset for Neutron Star Properties</p> <p>This dataset comprises three unified equations of state (EOS) for neutron star properties, denoted as eNJL1, eNJL2, and eNJL3, developed based on a microscopic nuclear matter formalism with explicit chiral symmetry. This formalism is grounded in the Nambu Jona-Lasinio (NJL) model and has been specifically designed to accurately describe nuclear matter by introducing four-point and eight-point interactions. These interactions are crucial for reproducing nuclear matter properties at saturation density.</p> <p>### Background and Methodology</p> <p>To ensure the robustness and accuracy of the model parameters, a Bayesian inference approach was utilized. This method involved imposing constraints from experimental and {\it ab-initio} nuclear matter calculations, as well as observational data from neutron stars. Key properties such as the effective nucleon mass, radius, and maximum mass of neutron stars were meticulously matched against these constraints. The resulting model parameters provide a comprehensive and reliable description of nuclear matter and neutron star properties.</p> <p>### Key Findings</p> <p>- **Effective Nucleon Mass:** The model predicts an effective nucleon mass range between 0.75 and 0.8 times the nucleon mass.<br>- **Neutron Star Radius:** At a 90% confidence level, the radius of a 1.4 $M_\odot$ neutron star varies between 11.48 km and 13.20 km.<br>- **Maximum Mass and Radius:** The model predicts neutron stars with masses up to approximately 2.2 $M_\odot$, with the radius of a 2 $M_\odot$ star being above 10.5 km.<br>- **Speed of Sound:** The speed of sound in the center of massive stars increases monotonically with density, reaching values between $\sqrt{0.7}c$ and $\sqrt{0.8}c$.</p> <p>### High-Density Constraints and Observational Alignment</p> <p>The high-density perturbative QCD (pQCD) results impose significant constraints on the EOS, invalidating those predicting larger maximum masses and radii. Interestingly, the EOS that satisfy pQCD constraints also align closely with recent mass-radius measurements, particularly those of PSR J0437-4715. This alignment provides an additional layer of validation against other relativistic mean field model results.</p> <p>### Dataset Overview</p> <p>This dataset includes three distinct EOS models (eNJL1, eNJL2, and eNJL3), each offering a unified approach for calculating neutron star properties. These models are detailed in the associated article, where the implications of the findings and the methodology are thoroughly discussed.</p> <p>Researchers and astrophysicists can utilize this dataset to further explore and model the properties of neutron stars, contributing to a deeper understanding of these fascinating celestial objects. The provided EOS models are essential tools for simulations and theoretical studies aimed at deciphering the complex nature of neutron stars and nuclear matter under extreme conditions.</p>
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
CLDF dataset derived from Oskolskaya et al.'s "A Bayesian approach to the classification of Tungusic languages" from 2021
<p>Cite the source of the dataset as:</p> <blockquote> <p>Oskolskaya, S., Koile, E., & Robbeets, M. (2021). A Bayesian approach to the classification of Tungusic languages. Diachronica, 39(1), 128–158. https://doi.org/10.1075/dia.20010.osk</p> </blockquote>
Data for 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited'
<p>Data and results for Chazot et al. (2018) 'Priors and Posteriors in Bayesian Timing of Divergence Analyses: the Age of Butterflies Revisited.'</p> <p>S1. List of taxa and Genbank accession codes.</p> <p>S2. Molecular matrix for the core analysis (S2a), the reduced dataset (S2b) and the dataset with a mitochondrial fragment (S2c).</p> <p>S3. RAxML topology (S3a) and time-calibrated tree (S3b) obtained from the core analysis. In S3a, numbers at the nodes indicate rapid-bootstrap support values. In S3b, node ages are the median of node age posterior distributions.</p> <p>S4. Tree obtained when using only deep-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S5. Tree obtained when using only shallow-level fossil calibrations. Node ages are the median of node age posterior distributions.</p> <p>S6. Tree obtained from the reduced dataset. Node ages are the median of node age posterior distributions.</p> <p>S7. Tree obtained when using exponential fossil calibration priors. Node ages are the median of node age posterior distributions.</p> <p>S8. Tree obtained when adding a mitochondrial gene fragment. Node ages are the median of node age posterior distributions.</p> <p>S9. Tree obtained when using fossil information only modeled using lognormal priors. Node ages are the median of node age posterior distributions.</p> <p>S10. Tree obtained when using the host-plant ages obtained from Foster et al. (2017). In S10a node ages are the median of node age posterior distributions, while in S10b the node ages are the mode the mode of the kernel density estimate of the posterior distribution.</p> <p>S11. Tree obtained when using a Yule prior instead of Birth-Death tree prior. Node ages are the median of node age posterior distributions.</p>
Bayesian consensus phylogram from the article "Systematics of Huicundomantis, a new subgenus of Pristimantis (Anura, Strabomantidae) with extraordinary cryptic diversity and eleven new species"
<p>Bayesian consensus phylogram depicting relationships within <em>Pristimantis</em> (<em>Huicundomantis</em>). Museum catalog numbers are shown before the species name. Posterior probabilities resulting from Bayesian Markov chain Monte Carlo searches appear next to the branches.</p>
Bayesian Inference of Atomistic Structure in Functional Materials
<pre>Configurational 5D DFT dataset of C60/TiO2 material accompanying the manuscript "Bayesian Inference of Atomistic Structure in Functional Materials" by M. Todorovic, M. U. Gutmann, J. Corander and P. Rinke.</pre>
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
GCTB SBayesR shrunk sparse linkage disequilibrium matrices for HM3 variants, summary statistics and predictors generated from "Improved polygenic prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al. 2019.
<p>GCTB LD matrices and results for HapMap 3 variants and 2.8M variants, which were used for</p> <p>simulation, cross-validation and across biobank analyses in the manuscript "Improved polygenic</p> <p>prediction by Bayesian multiple regression on summary statistics" by Lloyd-Jones, Zeng et al.</p> <p>2019.</p> <p>Unzip and see README for further details.</p>
Supplementary of Bayesian Evaluation of Incomplete Fission Yields
<p>This part employ a code to read parameters of the trained Bayesian neural network which are stored in the log file, and output the averaged function values and Confidence Interval (CI) of fission yields. The instructions are given in the attached BNN-fission.PDF file. </p> <p> </p>
Integrated Probabilistic Annotation (IPA): A Bayesian-based annotation method for metabolomic profiles integrating biochemical connections, isotope patterns and adduct relationships - Supplementary Data
<ol> <li>Supplementary_data_1: data and code for standards analysis and database update</li> <li>Supplementary_data_2.zip: data and code used for the generation of the synthetic experiment</li> <li>Supplementary_data_3.zip: data, code, and results of the <em>E. coli</em> dataset analysis</li> <li>Supplementary_data_4.zip: data, code, and results of the beer dataset analysis</li> <li>Supplementary_data_5.zip: data, code, and results of the comparison with xMSannotator</li> </ol>
CLDF dataset derived from Walker and Ribeiro's "Bayesian Phylogeography of the Arawak expansion" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Robert S. Walker and Lincoln A. Ribeiro. 2011. Bayesian phylogeography of the Arawak expansion in lowland South America. *Proceedings of the Royal Society B: Biological Sciences*. Volume 278, Number 1718, 2562--2567. DOI: 10.1098/rspb.2010.2579</p> </blockquote>
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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)
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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.