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1,066 results for “bayesian”
Data and supplementary information from: Sequential bayesian phylogenetic inference
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The optimal time to approach an unfamiliar object: A Bayesian model
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Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities
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Data from: Blouch: Bayesian linear Ornstein-Uhlenbeck models for comparative hypotheses
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Data for: Linking cell size, Vmax, and Km in phototrophs and chemotrophs: Insights from Bayesian inference
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Data from: Bayesian quantification of ecological determinants of outcrossing in natural plant populations: computer simulations and the case study of biparental inbreeding in English yew
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Original dataset, coded matrix, and Bayesian phylogenetic trees of three Late Ordovician brachiopod genera (Atrypida: Anazygidae)
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Data from: Assessing bayesian phylogenetic information content of morphological data using knowledge from anatomy ontologies
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Data for: Evaluating the impact of anatomical partitioning on summary topologies obtained with Bayesian phylogenetic analyses of morphological data
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Bayesian inference of ancestral host-parasite interactions under a phylogenetic model of host repertoire evolution
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Data from: How to validate a Bayesian evolutionary model
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Commonly used Bayesian diversification methods lead to biologically meaningful differences in branch-specific rates on empirical phylogenies
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FIGURE 10. Bayesian inference tree for 2,778 in A new species of the genus Pseudocrangonyx (Crustacea: Amphipoda Pseudocrangonyctidae) from Simbok Cave, Korea
FIGURE 10. Bayesian inference tree for 2,778 bp of nuclear 28S rRNA and histone H3, and mitochondrial COI and 16S rRNA markers. Numbers on nodes represent bootstrap values for maximum likelihood and Bayesian posterior probabilities. Taxonomically confused clades are highlighted in a gray box. The genus-group name Pseudocrangonyx is abbreviated to P.
Figure 5. Bayesian tree for the 38 in Two new species of lithobiid centipedes and the first record of Lamyctes africanus Porath (Chilopoda: Lithobiomorpha) in China
Figure 5. Bayesian tree for the 38 sequences based on COI sequences. The Bayesian posterior probabilities from Bayesian analyses are presented above the main branches. The scale bar represents substitutions per site. Country of origin given in square brackets: AU = Australia; CH = China; GE = Germany; DK = Denmark; SA = South Africa.
MCMC data for A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection
<p>These are unprocessed Markov-chain Monte-Carlo datasets accompanying the manuscript "A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection"</p>
FIG. 1. Bayesian 50 in A taxonomic revision of the Kerivoula hardwickii complex (Chiroptera: Vespertilionidae) with the description of a new species
FIG. 1. Bayesian 50% majority-rule consensus trees for Kerivoulinae species reconstructed based on (a) mitochondrial COI gene (657 bp) and (b) autosomal gene (1054 bp). For major clades, node support values are shown above branches as Bayesian posterior probabilities (before slash) and ML bootstrap values (after slash). In (a), clades K. hardwickii A–C are
FIGURE 1. Bayesian inference tree derived from the cyt b in Vanmanenia intermedia Fang, 1935, a valid hill-stream species of loach (Teleostei Gastromyzontidae) from the middle Yangtze River basin, Southwest China
FIGURE 1. Bayesian inference tree derived from the cyt b gene for nine species of Vanmanenia. Nodal numbers are posterior probability values larger than 50%.
Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis
<p>1. Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses.</p> <p>2. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval.</p> <p>3. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults.</p> <p>4. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.</p>
Data from: Bayesian analyses in phylogenetic palaeontology: interpreting the posterior sample
<p>Establishing hypotheses of relationships is a critical prerequisite for any macroevolutionary analysis, but different approaches exist for achieving this goal. Amongst palaeontologists using morphological data the Bayesian approach is increasingly preferred over parsimony, but this shift also alters the way we think about samples of trees. Here we revisit stratigraphic congruence as a comparator between Bayesian and parsimony samples, but in a new visual context: treespace. Such spaces represent an ordination of unique topologies that can also be extended to create a "landscape" where altitude represents some comparative measure (here congruence with stratigraphy). By co-opting existing visualization tools and applying them to a meta-analysis of 128 cladistic data sets we show that there is no consistent favouring of either Bayesian or parsimony according to stratigraphic congruence metrics, and further that empirical treespace visualizations suggest a complex variety of topological landscapes. We conclude by arguing that treespaces should become a standard exploratory tool in phylogenetic analysis.</p>
Supplementary evaluation files for the paper: Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones
<p>This package contains evaluation supplementary files for the paper: <em>Grid-Based Bayesian Filtering Methods for Pedestrian Dead Reckoning Indoor Positioning Using Smartphones</em> by Miroslav Opiela and František Galčík.</p> <p><strong>Contents: </strong></p> <ul> <li>ground_truth - real positions of checkpoints for given input files</li> <li>input - sensor measurements recordings with initial positions (also after floor transitions) and checkpoint labels </li> <li>maps - processed map models containing positions of points and connections (e.g., walls) in custom coordinate system. Reference to GNSS and map rotation is inducted in maps-meta.xml</li> <li>output - data processed by the localization system. JSON containing the applied method, its configuration, and all estimated positions. Errors for every folder are summarized in the csv file</li> <li>visualization - trajectories visualized for selected output files</li> <li>readme.txt - describes data formats used for particular files in this dataset and summarizes output files</li> </ul> <p><strong>Venues</strong></p> <p>Data are recorded in three buildings:</p> <ul> <li>codename: SA1, SA1_rotated - recorded by the author in the faculty building (Park Angelinum 9, 04001, Košice, Slovakia) using Lenovo tablet</li> <li>codename: AtlantisR0, AtlantisR-1, AtlantisR+1, AtlantisR+2 - the shopping mall Atlantis Le Centre (Boulevard Salvador Allende, 44800 Saint-Herblain, France). Dataset is from IPIN 2018 competition and loc_20180922_160206 is recorded by the author using Xiaomi Mi 5.</li> <li>codename: CNR_0, CNR_1, CNR_2 - the research institute building CNR (Via Giuseppe Moruzzi, 56127 Pisa, Italy). Dataset is from IPIN 2019 competition. </li> </ul> <p><strong>Used datasets</strong></p> <p>A subset of input data is derivated from available logfiles provided by organizers of IPIN 2018 and IPIN 2019 competitions:</p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a> </li> </ul> <p><strong>Funding</strong></p> <p>The work was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant no. 1/0056/18 and by the Slovak Research and Development Agency under the contract no. APVV-15-0091.</p> <p><strong>Contact</strong></p> <p>For any further questions, please contact:</p> <p>Miroslav Opiela, miroslav.opiela@upjs.sk Institute of Computer Science, Faculty of Science, P. J. Šafárik University (UPJS), Košice, Slovakia</p>
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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.