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139 results for “bayesian analysis”
Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011
The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
Bayesian analysis of the equation of state of quantum chromodynamics from a holographic model
<p>Prior and posterior samples obtained from a Bayesian analysis of the equation of state of quantum chromodynamics (QCD) within a holographic Einstein-Maxwell-Dilaton model, constrained by state-of-the art lattice QCD results at a vanishing net density of baryons.</p> <p>Samples contain metadata, model parameters, and model predictions for the location of the QCD critical point.</p> <p>Supplement to <a title="Bayesian location of the QCD critical point from a holographic perspective" href="https://arxiv.org/abs/2309.00579">arXiv:2309.00579</a>.</p>
Phlorest phylogeny derived from Kitchen et al. 2009 'Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kitchen A, Ehret C, Assefa S & Mulligan CJ. 2009. Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Proceedings of the Royal Society B: Biological Sciences, 270(1668), 2703-2710.</p> </blockquote>
Phlorest phylogeny derived from Lee & Hasegawa 2011 'Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2011) Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725):3662–9.</p> </blockquote>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
CLDF dataset derived from Lee and Hasegawa's "Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee, Sean and Hasegawa, Toshikazu (2011). Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725), 3662–3669. doi:10.1098/rspb.2011.0518.</p> </blockquote>
Supplementary Materials to "Subgrouping in a `dialect continuum': A Bayesian phylogenetic analysis of the Mixtecan language family"
<p>SM0: metadata on the languages of the sample</p> <p>SM 1: custom word list</p> <p>SM2: prose explanation of cognate coding and IPA conversion</p> <p>SM3: annotated cognate sets</p> <p>SM4: nexus files of the broad and fine grained cognate coding</p> <p>SM5: NeighborNet visualization with coloring by Josserand (1983)'s groupings and by groupings from our analysis</p> <p>SM6: BEAST2 xml files</p> <p>SM7: MCC trees from BEAST2 analysis</p> <p>SM8: DensiTree visualization and visualization of full MCC tree of best performing model</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-16, and the abundance values range from log10(VMR) = -3.5 to -1.5 in steps of 0.5 and 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input H2O value (abundance case), the retrieved H2O value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for H2O. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) I: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for H2O, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input H2O': str})</p>
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.
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>
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.
FIGURE 3 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 3 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from 18S rRNA gene sequence alignment under a transitional model with invariable sites and a gamma correction (TIM 2 + I + G). Posterior probabilities greater Downloaded than 0.70 from are Brill given.comfor08/29/ appropriate 2023 05:44:51PM clades. Newly obtained sequences in this study are shown in bold. Scale bar = expected changesvia per site free. access
FIGURE 1 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 1 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from D2 and D3 expansion domains of 28S rRNA sequence alignment under an SYM model with invariable sites and a gamma-shaped distribution (SYM + 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. ** = Branches collapsed, indicating clustered Longidorus species. For a more specific detail of collapsed clades, see supplementary fig. S1.
FIGURE 2 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 2 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from ITS1 rRNA sequence alignment under a 3-parameter model with invariable sites and a gamma-shaped distribution (TPM3 µf + 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. Downloaded from Brill.com08/29/2023 05:44:51PM via free access
Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis
<p>Measurement data, simulation data and code from the manuscript Braccini et al. "Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis" </p> <p>ArXiv preprint arXiv:2309.11270 [nucl-ex] at https://doi.org/10.48550/arXiv.2309.1127</p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN; Data for CH4
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.8-1.5 micron, with 25 evenly spaced 20%, 30%, and 40% bandpasses in this range. The SNR ranges from 3-20. We present the lower and upper wavelength per bandpass, the input CH4 value (abundance case), the retrieved CH4 value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for CH4. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input CH4': str})</p>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
<p>Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (<em>Ambystoma</em> <em>californiense</em>; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species.</p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Data for "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain"
<p>Data for the "BayesCMD: A Bayesian framework for the analysis of systems biology models of the brain".</p> <p>All files except 'simulated_hypoxia.csv' contains both input and output data.</p>
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.