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1,066 results for “bayesian”
Fig. 2. Bayesian tree reconstructed from a in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 2. Bayesian tree reconstructed from a combined dataset of Cytb + ND2 + FGB + IRBP (3342 bp). The values on the branches are bayesian posterior probabilities for the partitioned analysis (see text for models) and bootstrap proportions obtained from ML analysis (model: GTR + I + G).
Fig. 5. Bayesian skyline plots for three Idarnes species. X in Community Structure and Undescribed Species Diversity in Non-Pollinating Fig Wasps Associated with the Strangler Fig Ficus petiolaris
Fig. 5. Bayesian skyline plots for three Idarnes species. X-axes are in units of mutations per site, while y-axes are in units of effective population size scaled by mutation rate. LO1 shows sharp growth in population size, whereas SO1 and SO2 show a similar pattern of consistent population size through time with minimal growth. LO2 was not included as it contains two cryptic species reducing sample sizes too low for analysis.
Fig. 5. Bayesian 50 in Two atypical new species of the genus Sectonema Thorne, 1930 (Nematoda, Dorylaimida, Aporcelaimidae) from Vietnam
Fig. 5. Bayesian 50% majority rule consensus trees as inferred from D2–D3 expansion segments of 28S rRNA gene sequence alignments under the GTR + I + G model. Posterior probabilities are given for appropriate clades. Newly obtained sequences are indicated by bold letters.
Bayesian evaluation of the mass calibration example from EA 4/02
<p>The example describes how the measurement uncertainty of the calibration of a 10 kg weight can be performed using Bayes’ rule and a measurement model. The purpose of the example is to demonstrate how Bayes’ rule can be implemented in the context of the Guide to the expression of Uncertainty in Measurement and the uncertainty can be propagated. Special attention is paid to the assignment of probability density functions of type A and type B evaluations of standard uncertainty.</p> <p>The files contained in the dataset are:</p> <ul> <li>Readme.txt: instructions on how to install and run the project</li> <li>Mass_example_EA_4_02.pdf: Report "Bayesian evaluation of the mass calibration example from EA 4/02"</li> <li>Compendium.bib: bibliography file</li> <li>Mass_example_EA_4_02_Article.Rproj: RStudio project file</li> <li>Mass_example_EA_4_02.Rnw: source file to generate Mass_example_EA_4_02.tex in RStudio</li> <li>Mass_example_EA_4_02.bbl: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.log: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.synctex.gz: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.tex: LaTeX file to be compiled in order to produce Mass_example_EA_4_02.pdf</li> <li>Mass_example_EA_4_02-concordance.tex: file generated by RStudio when compiling Mass_example_EA_4_02.Rnw.</li> </ul>
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 taxa using five genes (ArgK, CAD, LWRh, Top1, Wg) in a MrBayes analysis. Above node numbers indicate posterior probability. Data were partitioned by PartitionFinder v.1.1.1 and analyzed using a best fit model for each gene and codon position, with 10 million generations and a burn-in of 25 %. Area enclosed by dashed lines is enlarged on Fig. 2.
Figure 1. Bayesian phylogenetic tree inferred from the 640 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 1. Bayesian phylogenetic tree inferred from the 640-bp of cytochrome c oxidase subunit I gene under GTR + I + G model of sequence evolution. The two new species are highlighted in light grey (Cratera ochra sp. nov.) and dark grey (Obama maculipunctata sp. nov.). Values indicate support for each node according to the maximum posterior probabilities>70% and bootstrap support values> 70%, respectively.
Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism
<p>This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)</p>
Flux estimates for the Fermi map (Bayesian GCNN vs. NPTFit)
<p>Prediction of the Bayesian graph-convolutional neural network (GCNN) for the Fermi photon-count map. The shaded regions show the predictive (aleatoric and epistemic summed in quadrature) 1σ uncertainty. The markers with error bars (68% credible intervals) indicate the Non-Poissonian template fit (NPTFit) estimates for comparison. The NN predictions for the GCE flux in the Fermi map are similar in magnitude to those of NPTFit, but the GCE is almost entirely attributed to the smooth dark matter template.</p> <p>To view the plot, open it with a browser such as Google Chrome or Firefox. Templates can be (de-)activated by clicking on the colored rectangles. For zooming and panning, click on the buttons in the lower left corner.</p>
Anatomical partitioning has little influence in topologies from Bayesian phylogenetic analyses of morphological data
<p>Morphological data is a fundamental source of evidence to reconstruct the Tree of Life, and Bayesian phylogenetic methods are increasingly being used for this task, along with, or instead of, traditional parsimony approaches. Bayesian phylogenetic analyses require the use of proper evolutionary models and their performance have been intensively studied in the past few years, with significant improvements to our knowledge regarding their performance. Notwithstanding, it was only recently that partitioned models for morphology received attention in studies of empirical data, but a systematic evaluation of its performances using simulations was never performed. Here we evaluate the influence of partitioned models defined by anatomical criterion in the precision and accuracy of consensus tree topologies, evaluating the possible negative effects of under and overpartitioning. For that, we analysed datasets simulated using parameters and properties of two empirical datasets, using Bayesian phylogenetic analyses in MrBayes. Additionally, we reanalysed 32 empirical datasets for diverse groups of vertebrates, applying unpartitioned and partitioned models. We found that in general, partitioning by anatomy has little to no influences in the performance of Bayesian phylogenetic methods in respect to the metrics studied here, with analyses under alternative partitioning schemes presenting very similar tree precision and accuracy. We discuss the possible reasons for the disagreement between the results obtained here and previous studies for empirical morphological data, and with empirical and simulation studies of molecular data, discussing the adequacy of anatomical partitioning relative to alternative methods to partition morphological datasets and how morphological and molecular partitioning are related.</p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
Data from: Full Bayesian comparative phylogeography from genomic data
A challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian approach, ecoevolity, which takes full advantage of information in genomic data. By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estimating the number and timing of divergence events across pairs of populations than existing approximate-likelihood methods. Our full Bayesian approach also requires several orders of magnitude less computational time than existing ABC approaches. We find that despite assuming unlinked characters (e.g., unlinked single-nucleotide polymorphisms), the new method performs better if this assumption is violated in order to retain the constant characters of whole linked loci. In fact, retaining constant characters allows the new method to robustly estimate the correct number of divergence events with high posterior probability in the face of character-acquisition biases, which commonly plague loci assembled from reduced-representation genomic libraries. We apply our method to genomic data from four pairs of insular populations of Gekko lizards from the Philippines that are not expected to have co-diverged. Despite all four pairs diverging very recently, our method strongly supports that they diverged independently, and these results are robust to very disparate prior assumptions.
Gilby et al PLoS ONE Bayesian Belief Network input data
<p>Gilby et al PLoS ONE Bayesian Belief Network input data. Data used to educate relationships between nodes in Bayesian Belief Network for coral reef condition relative to management intervenations on coral reefs in Moreton Bay, Queensland, Australia.</p>
Repository of posterior distributions from Bayesian benchmark dose analysis
<p>This repository contains posterior distributions for all model parameters obtained from analysis of continuous dose-response studies.</p>
CLDF dataset derived from Kitchen et al.'s "Bayesian phylogenetic analysis of Semitic languages" from 2009
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Andrew Kitchen, Christopher Ehret, Shiferaw Assefa, Connie J. Mulligan. Proc. R. Soc. B 2009 -; DOI: 10.1098/rspb.2009.0408. Published 29 April 2009</p> </blockquote>
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I in Designation of a neotype for Myxicola infundibulum (Montagu, 1808) (Annelida: Sabellidae) and a new species from the UK
Fig. 11. Bayesian inference trees. A. 16S rRNA dataset. B. Cytochrome oxidase I gene dataset. The first value at each node represents maximum likelihood bootstrap support, the second the Bayesian posterior probabilities and the third the maximum parsimony bootstrap support.
Fig. 2. Bayesian consensus tree generated from partial 28S in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data
Fig. 2. Bayesian consensus tree generated from partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Values shown at each node refer to Bayesian (BI) posterior probabilities/maximum likelihood (ML) percentages of the bootstrap values with 100 replicates. Bootstrap values lower than 50 are given as dashes (-).
Code and data for Bayesian joint species distribution model selection for community-level prediction
<p>Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.</p>
Data for: Reintroduced Oriental stork bayesian hierarchical model data
<p>Long-lived territorial bird populations often consist of a few territorial breeding adults and many non-breeding individuals. Some populations are threatened by anthropogenic activities, because of human conflicts for high-quality breeding habitat. Therefore, habitat restoration projects have been widely implemented to improve avian population status. In conjunction with habitat restoration, conservation translocations have been increasingly implemented. Adequate non-breeder survival can be a key factor in the success of these attempts because non-breeding birds may represent reservoirs for the replacement of breeders. The maintenance of breeding pair numbers is also influenced by the transition rate of non-breeders to breeders. The reintroduction of Oriental stork (<em>Ciconia boyciana</em>), a long-lived, territorial, endangered species, was initiated in Japan in 2005 using captive birds in hopes of increasing the population's use of restored habitat. Our objective of this study was to elucidate the factors determining reintroduced stork survival and recruitment to the breeding populations. We estimated the survival rate and breeding participation rate by sex, age, generation, wild-born or not, haplotypes, and breeding status in storks reintroduced during 2005–2022 using Bayesian hierarchical models. There was no significant difference in survival rate between non-breeders and breeders. However, the survival rate was lower in wild-born birds than released birds, which may be related to the longer-distance natal dispersal of new generations. Accelerated habitat restoration around breeding areas and preventive measures for collision with human-built structures should be implemented for the sustained growth of reintroduced populations. A low survival rate was also detected for a specific mtDNA haplotype that accounts for the majority of the reintroduced population. This phenomenon might be explained by mtDNA-encoded mutations. Moreover, captive breeding and release history might contribute to an increase in the proportion of this haplotype in the wild.</p>
Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
<p><strong>Aim</strong>: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.<br><strong>Location</strong>: Tanzania, East Africa.<br><strong>Time period</strong>: 1980-1999 and 2000-2020.<br><strong>Major taxa studied</strong>: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.<br><strong>Methods</strong>: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits.<br><strong>Results</strong>: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results.<br><strong>Main conclusions</strong>: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.</p>
FIGURE 4 Phylogenetic relationships within the genus Longidorus. Bayesian 50 in Molecular phylogenetic analysis and comparative morphology reveals the diversity and distribution of needle nematodes of the genus Longidorus (Dorylaimida: Longidoridae) from Spain
FIGURE 4 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from cytochrome c oxidase subunit I (CoxI) mtDNA gene sequence alignment under the general time-reversible model of sequence evolution with correction for invariable sites and a gammashaped distribution (GTR + I + G). Posterior probabilities greater than 0.70 are given for appropriate clades. Newly obtained sequences in this study are shown in bold. Scale bar = expected changes per site.
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.