Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “Bayesian estimation”
Data from: Bayesian estimation of the global biogeographical history of the Solanaceae
Open the record for dataset details and reuse information.
Data from: Bayesian species recognition and abundance estimation: Unravelling the mysteries of salmonid migration in the Teno River
Open the record for dataset details and reuse information.
All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
Open the record for dataset details and reuse information.
Scalable Bayesian divergence time estimation with ratio transformations
Open the record for dataset details and reuse information.
Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference
Open the record for dataset details and reuse information.
Data from: Multilocus phylogeny and Bayesian estimates of species boundaries reveal hidden evolutionary relationships and cryptic diversity in Southeast Asian monitor lizards
Recent conceptual, technological, and methodological advances in phylogenetics have enabled increasingly robust statistical species delimitation in studies of biodiversity. As the variety of evidence purporting species diversity has increased, so too have the kinds of tools and inferential power of methods for delimiting species. Here we showcase an organismal system for a data-rich, comparative molecular approach to evaluating strategies of species delimitation among monitor lizards of the genus Varanus. The water monitors (Varanus salvator Complex), a widespread group distributed throughout Southeast Asia and southern India, have been the subject of numerous taxonomic treatments, which have drawn recent attention due to the possibility of undocumented species diversity. To date, studies of this group have relied on purportedly diagnostic morphological characters, with no attention given to the genetic underpinnings of species diversity. Using a 5-gene dataset, we estimated phylogeny and used multilocus genetic networks, analysis of population structure, and a Bayesian coalescent approach to infer species boundaries. Our results contradict previous systematic hypotheses, reveal surprising relationships between island and mainland lineages, and uncover novel, cryptic evolutionary lineages (i.e. new putative species). Our study contributes to a growing body of literature suggesting that, used in concert with other sources of data (e.g., morphology, ecology, biogeography), multilocus genetic data can be highly informative to systematists and biodiversity specialists when attempting to estimate species diversity and identify conservation priorities. We recommend holding in abeyance taxonomic decisions until multiple, converging lines of evidence are available to best inform taxonomists, evolutionary biologists, and conservationists.
Data from: A Bayesian method for the joint estimation of outcrossing rate and inbreeding depression
The population outcrossing rate (t) and adult inbreeding coefficient (F) are key parameters in mating system evolution. The magnitude of inbreeding depression as expressed in the field can be estimated given t and F via the method of Ritland (1990). For a given total sample size, the optimal design for the joint estimation of t and F requires sampling large numbers of families (100-400) with fewer offspring (1-4) per family. Unfortunately, the standard inference procedure (MLTR) yields significantly biased estimates for t and F when family sizes are small and maternal genotypes are unknown (a common occurrence when sampling natural populations). Here, we present a Bayesian method implemented in the program BORICE that effectively estimates t and F when family sizes are small and maternal genotype information is lacking. BORICE should enable wider use of the Ritland approach for field-based estimates of inbreeding depression. As proof of concept, we estimate t and F in a natural population of Mimulus guttatus. In addition, we describe how individual maternal inbreeding histories inferred by BORICE may prove useful in studies of inbreeding and its consequences.
Data from: Bayesian phylogenetic estimation of clade ages supports trans-atlantic dispersal of cichlid fishes
Divergence-time estimation based on molecular phylogenies and the fossil record has provided insights into fundamental questions of evolutionary biology. In Bayesian node dating, phylogenies are commonly time calibrated through the specification of calibration densities on nodes representing clades with known fossil occurrences. Unfortunately, the optimal shape of these calibration densities is usually unknown and they are therefore often chosen arbitrarily, which directly impacts the reliability of the resulting age estimates. As possible solutions to this problem, two non-exclusive alternative approaches have recently been developed, the "fossilized birth-death" model and "total-evidence dating". While these approaches have been shown to perform well under certain conditions, they require including all (or a random subset) of the fossils of each clade in the analysis, rather than just relying on the oldest fossils of clades. In addition, both approaches assume that fossil records of different clades in the phylogeny are all the product of the same underlying fossil sampling rate, even though this rate has been shown to differ strongly between higher-level taxa. We here develop a flexible new approach to Bayesian age estimation that combines advantages of node dating and the fossilized birth-death model. In our new approach, calibration densities are defined on the basis of first fossil occurrences and sampling rate estimates that can be specified separately for all clades. We verify our approach with a large number of simulated datasets, and compare its performance to that of the fossilized birth-death model. We find that our approach produces reliable age estimates that are robust to model violation, on par with the fossilized birth-death model. By applying our approach to a large dataset including sequence data from over 1000 species of teleost fishes as well as 147 carefully selected fossil constraints, we recover a timeline of teleost diversification that is incompatible with previously assumed vicariant divergences of freshwater fishes. Our results instead provide strong evidence for trans-oceanic dispersal of cichlids and other groups of teleost fishes.
Data from: Estimating age and age class of harvested hog deer from eye lens mass using frequentist and Bayesian methods
Estimation of the age or age class of harvested animals is often necessary to interpret the condition and dynamics of wildlife populations. The mammalian eye lens continues to grow until death and hence the dry mass of the eye lens has commonly been used to estimate the age of mammals. The method requires the relationship between eye lens mass and age to be parameterized using individuals of known age. However, predicting age is complicated by the curvilinear relationship between eye lens mass and age. We used frequentist and Bayesian methods to predict the ages and age classes of harvested hog deer Axis porcinus from eye lens mass. Deer were tagged as calves and harvested 4–177 months later in southeastern Australia. Lenses were extracted, fixed and oven-dried. Of the five growth models evaluated, the Lord model best described the relationship between age and eye lens dry mass (R2 = 95%). The precision of age predictions obtained using the Lord model in a Bayesian mode of inference decreased with increasing eye lens dry mass, with the size of the 95% CI equaling or exceeding predicted age for hog deer > 6 years. However, most predictions of hog deer age will have reasonable precision because few animals > 6 years are harvested. Linear discriminant analysis had high predictive power for classifying hog deer to four widely-used age classes (juvenile, yearling, prime-age and senescent). The Bayesian method is recommended for inverse non-linear prediction of age and the frequentist linear discriminant analysis method is recommended for estimating age class. We provide tables of correspondence between hog deer eye lens dry mass and predicted age and age class. Our statistical methods can be used to estimate age and age class for other mammalian species, including from other ageing techniques such as tooth eruption-wear criteria.
Data from: Bayesian estimates of male and female African lion mortality for future use in population management
The global population size of African lions is plummeting, and many small fragmented populations face local extinction. Extinction risks are amplified through the common practice of trophy hunting for males, which makes setting sustainable hunting quotas a vital task. Various demographic models evaluate consequences of hunting on lion population growth. However, none of the models use unbiased estimates of male age-specific mortality because such estimates do not exist. Until now, estimating mortality from resighting records of marked males has been impossible due to the uncertain fates of disappeared individuals: dispersal or death. We develop a new method and infer mortality for male and female lions from two populations that are typical with respect to their experienced levels of human impact. We found that mortality of both sexes differed between the populations and that males had higher mortality across all ages in both populations. We discuss the role that different drivers of lion mortality may play in explaining these differences and whether their effects need to be included in lion demographic models. Synthesis and applications. Our mortality estimates can be used to improve lion population management and, in addition, the mortality model itself has potential applications in demographically informed approaches to the conservation of species with sex-biased dispersal.
FIGURE. Maximum likelihood phylogram of Multiclavula spp. based on ITS sequences. Rooted to Clavulina cristata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap. in New and interesting species of Agaricomycetes from Panama
FIGURE. Maximum likelihood phylogram of Multiclavula spp. based on ITS sequences. Rooted to Clavulina cristata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap.
FIGURE. Maximum likelihood phylogram of Humidicutis spp. based on ITS sequences. Rooted to Humidicutis marginata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap. in New and interesting species of Agaricomycetes from Panama
FIGURE. Maximum likelihood phylogram of Humidicutis spp. based on ITS sequences. Rooted to Humidicutis marginata. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap.
FIGURE. Bayesian MCMC phylogram of Gliophorus spp. based on ITS sequences. Rooted to Hygrophorus pudorinus. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap. in New and interesting species of Agaricomycetes from Panama
FIGURE. Bayesian MCMC phylogram of Gliophorus spp. based on ITS sequences. Rooted to Hygrophorus pudorinus. Bar = estimated changes/nucleotide. Support values above or below branches: Bayesian posterior probability/maximum likelihood bootstrap.
Fig. 6 in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 6. The parsimony length of three characters on a single tree. Each character has been scored for how many changes it exhibits on the displayed tree. Character one is not considered parsimony informative, as every tip on the tree has a different state, and therefore, it cannot be used to discriminate among trees. Character three is non-informative because it has no variation. Character two is considered informative because it favors trees containing one grouping over another. Under parsimony, characters one and three would not be collected. Under a Bayesian model, not observing invariant characters must be corrected for in order to avoid overestimating the true rate of evolutionary change (Lewis 2001).
Fig. 3 in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 3. Schematic of an exponential (10) distribution. A commonly used distribution in Bayesian phylogenetics, the exponential is often used to place a prior on branch lengths. Under the exponential (10), most branch lengths are expected to be fairly short (to the left-hand side of the distribution), though longer branches are allowed.
Fig. 4 in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 4. Flowchart of the MCMC algorithm. In the MCMC algorithm, initial conditions are proposed and evaluated for likelihood.Then, the tree and/or other model parameters are changed.The likelihood of these new values is then evaluated. If they represent an improvement over the old ones, they are used to seed the next MCMC step. If not, they are rejected.
Fig. 5. A in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 5. A drawing showing different types of characters. In the center is Sphecomyrma freyi Wilson 1967 (Hymenoptera: Formicidae), a Cretaceous ant (Wilson 1967). Ant silhouette via T. Michael Keesey. (A) shows a binary discrete trait, fusion of the petiole.This trait has two possible states—fused and unfused.The distribution beside it shows how common each of the two character states are in the Barden and Grimaldi (2016) dataset. (B) shows a discrete, multistate trait. The number of antennal segments can take on multiple possible values, though only three are observed in the dataset. (C) shows a hypothetical continuous trait, tarsus length. Continuous traits can take on any real number, not only discrete values.
Fig. 7. A in A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data
Fig. 7. A schematic showing common assumptions about character evolution. (a) shows the Q-matrix under the Mk model for binary data.This corresponds to the assumptions on the right-hand side of the figure, that a character is equally likely to change from a 0 state to a 1 state as the reverse. (b) shows the same assumptions, expanded to a multistate character. (c) shows a Q-matrix with each character state allowed to have a different stationary character frequency, enabling different 0 → 1 and 1 → 0 rates. (d) displays a parsimony step matrix that penalizes 0 → 1 transitions. In (d), the rows represent the starting state, and the columns represent the state to which the character is changing.
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Bayesian models' outputs for: A new method to explicitly estimate the shift of optimum along gradients in multispecies studies
<p>This repository contains data to reproduce analysis presented in the paper:</p> <p>B. Mourguiart, B. Liquet, K. Mengersen, T. Couturier, J. Mansons, Y. Braud, A. Besnard . A new method to explicitly estimate the shift of optimum along gradients in multispecies studies. <em>Journal of Biogeography</em>, (in press).</p> <p>The paper introduces a new formulation of a Bayesian hierarchical linear model that explicitly estimates optimum shifts for multiple species having symmetrical response curves. This new formulation, called Explicit Hierarchical Model of Optimum Shifts (EHMOS), is compared to a mean comparison method and a Bayesian generalized linear mixed model (GLMM) using simulated and real datasets. Fitting the models to the simulated data took several days. Here we provide the models' outputs needed to reproduce the results presented in the paper without re-running the models. </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.