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.
20
datasets available to search
ShareScore release 0.7.1
Dataset results
20 results for “ancestral state reconstruction”
Bayesian Methods for Ancestral State Reconstruction in Morphosyntax
<p>Supplementary files to accompany journal submission.</p> <p>Files are:</p> <p> </p> <p>tree.pdf - pdf consensus tree, for illustration</p> <p>data.txt - coding file</p> <p>TREE_Set.t - nexus format sample of trees.</p> <p>sources.pdf - source materials used for languages</p>
Evolutionary insights into Felidae iris color through ancestral state reconstruction
<p>There have been almost no studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning cluster image analysis of publicly available photographs of all felid species, as well as a number of subspecies, five felid eye colors were identified: brown, hazel/green, yellow/beige, gray, and blue. Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors' eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown-eyed individuals, as well as a novel evolution of gray-eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors. It was also found that the loss of brown eyes and the gain of yellow/beige eyes is associated with an increase in the likelihood of evolving round pupils, which in turn influence the shades present in the eyes. Along with these important insights, the unique methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises.</p>
Evolutionary insights into Felidae iris color through ancestral state reconstruction
Open the record for dataset details and reuse information.
FIGURE 5 Ancestral state reconstructions. A. Whorl count. B. Body length. C in Phylogeny and systematic revision of the helicarionid semislugs of eastern Queensland (Stylommatophora, Helicarionidae)
FIGURE 5 Ancestral state reconstructions. A. Whorl count. B. Body length. C. Altitude.
Data and R Script from: The ancestor of sharks and rays laid eggs, but ancestral state reconstructions need empirically supported traits and transparent reporting
Open the record for dataset details and reuse information.
Ancestral state reconstruction for regeneration and autotomy in arthopods and reptiles
<p>Some form of regeneration occurs in all lifeforms and extends from single-cell organisms to humans. The degree to which regenerative ability is distributed across different taxa, however, is harder to ascertain given the potential for phylogenetic constraint or inertia, and adaptive processes to shape this pattern. Here, we examine the phylogenetic history of regeneration in two groups where the trait has been well-studied: arthropods and reptiles. Because autotomy is often present alongside regeneration in these groups, we performed ancestral state reconstructions for both traits to more precisely assess the timing of their origins and the degree to which these traits coevolve. Using an ancestral trait reconstruction, we find that autotomy and regeneration were present at the base of the arthropod and reptile trees. We also find that when autotomy is lost it does not re-evolve easily. Lastly, we find that the distribution of regeneration is intimately connected to autotomy with the association being stronger in reptiles than in arthropods. While these patterns suggest that decoupling autotomy and regeneration at a broad phylogenetic scale may be difficult, the available data provides useful insight into their entanglement. Ultimately, our reconstructions provide important groundwork to explore how selection may have played a role during the loss of regeneration in specific lineages.</p>
Data from: Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification
The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth–death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states.
FIGURE 1. Ancestral character-state reconstructions for Characters 1–9 in Concentrated evolutionary novelties in the foot musculature of Odontophrynidae (Anura: Neobatrachia), with comments on adaptations for burrowing
FIGURE 1. Ancestral character-state reconstructions for Characters 1–9. Ambiguities in Macrogenioglottus alipioi and Odontophrynus carvalhoi in Characters 5–7 are due to polymorphism.
Figure 2 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 2. Bayesian consensus tree resulting from the LW-Rh and Wg gene alignments (871 bp). Coloured dots on the branches indicate the values of posterior probability (PP): green dots represent values between 1.00 and 0.95, yellow dots between 0.94 and 0.90, and red dots ≤ 0.89. The nodes are indicated with numbers. Values above and below the branches represent the ancestral genome size (GS; 1C-values, in picograms) at particular nodes: in blue is the value generated by the maximum likelihood (ML) [asterisks are related to confidence interval (CI) values shown in Supporting Information, Table S4]; orange is the value generated by maximum parsimony (MP); and black, given below the branches, is the value generated by Bayesian inference (BI). Genome size data (1C-values) were obtained in the present work (pink dots) or taken from the literature (grey dots).
Figure 1 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 1. Fluorescence intensity histograms obtained from three different species, with Drosophila melanogaster as internal standard, stained with propidium iodide (PI; A–C) or 4,6-diamidino-2-phenylindole (DAPI; D–F). The x-axis corresponds to the scale of fluorescence intensity, and the y-axis represents the number of nuclei with that fluorescence intensity.
Figure 3 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 3. Mean genome size (in picograms and megabase pairs) estimated for Formicidae subfamilies. The phylogenetic tree generated in the present study was redrawn, with collapsed branches corresponding to species of the same subfamily.
Figure 3 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 3. Mean genome size (in picograms and megabase pairs) estimated for Formicidae subfamilies. The phylogenetic tree generated in the present study was redrawn, with collapsed branches corresponding to species of the same subfamily.
Figure 2 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 2. Bayesian consensus tree resulting from the LW-Rh and Wg gene alignments (871 bp). Coloured dots on the branches indicate the values of posterior probability (PP): green dots represent values between 1.00 and 0.95, yellow dots between 0.94 and 0.90, and red dots ≤ 0.89. The nodes are indicated with numbers. Values above and below the branches represent the ancestral genome size (GS; 1C-values, in picograms) at particular nodes: in blue is the value generated by the maximum likelihood (ML) [asterisks are related to confidence interval (CI) values shown in Supporting Information, Table S4]; orange is the value generated by maximum parsimony (MP); and black, given below the branches, is the value generated by Bayesian inference (BI). Genome size data (1C-values) were obtained in the present work (pink dots) or taken from the literature (grey dots).
Data from: Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification
Open the record for dataset details and reuse information.
Reconstructing Ecological Niche Evolution via Ancestral State Reconstruction with Uncertainty Incorporated
Open the record for dataset details and reuse information.
Ancestral state reconstruction for regeneration and autotomy in arthopods and reptiles
Open the record for dataset details and reuse information.
Data from: Ancestral state reconstruction sheds new light on the loss of divarication hypothesis on New Zealand’s outlying islands
Open the record for dataset details and reuse information.
Data from: Ancestral state reconstruction, rate heterogeneity, and the evolution of reptile viviparity
Virtually all models for reconstructing ancestral states for discrete characters make the crucial assumption that the trait of interest evolves at a uniform rate across the entire tree. Although methods for identifying evolutionary rate shifts in continuous characters have attracted recent attention (e.g. Eastman et al., 2011, Stack et al., 2011), such methods for discrete characters have only very recently been developed (Beaulieu et al., 2013, Beaulieu and O'Meara 2014) and have yet to be widely used. However, ancestral state reconstructions of discrete characters are being performed on increasingly large phylogenies, where it is likely that evolutionary rates will vary greatly between different clades (Beaulieu and O'Meara 2014). Here, we show how failure to account for such variable evolutionary rates can cause highly anomalous (and likely incorrect) results, while three methods that accommodate rate variability yield the opposite, more plausible, and more robust reconstructions. The random local clock method, implemented in BEAST, estimates the position and magnitude of rate changes on the tree, split BiSSE estimates separate rate parameters for pre-specified clades, and the hidden rates model partitions each character state into a number of rate categories. The importance of accounting for rate heterogeneity in ancestral state reconstruction is highlighted empirically with a new analysis of the evolution of viviparity in squamate reptiles. Additionally, simulations show the inadequacy of traditional models when characters evolve with both asymmetry (different rates of change between states within a character) and heterotachy (different rates of character evolution across different clades).
Data from: Ancestral state reconstruction, rate heterogeneity, and the evolution of reptile viviparity
Open the record for dataset details and reuse information.
Figure 1 in The tight genome size of ants: diversity and evolution under ancestral state reconstruction and base composition
Figure 1. Fluorescence intensity histograms obtained from three different species, with Drosophila melanogaster as internal standard, stained with propidium iodide (PI; A–C) or 4,6-diamidino-2-phenylindole (DAPI; D–F). The x-axis corresponds to the scale of fluorescence intensity, and the y-axis represents the number of nuclei with that fluorescence intensity.
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.