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.
635
datasets available to search
ShareScore release 0.9.0
Dataset results
635 results for “comparative phylogenetics”
Figure 25 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 25 Clasper; external morphology. A, Scyliorhinus boa, USNM 221563, 348 mm TL; B, Scyliorhinus canicula, BMNH 1983.8.3.1–4, 585.7 mm TL; C, Scyliorhinus duhamelii, MCZ S-63, 338.7 mm TL; D, Scyliorhinus retifer, UF 36359, 372 mm TL; E, Scyliorhinus stellaris, BMNH 1976.7.30.10, 476 mm TL; F, Scyliorhinus torazame, NSMT 50632, 427.9 mm TL. ch, clasper hooks; crh, cover rhipidion; dd, dermal denticles; en, envelope; erh, exorhipidion; hp, hypopyle; rh, rhipidion; tdc, terminal dermal cover. Modified from Soares and de Carvalho (2019).
Figure 7 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 7 Dermal denticles above the origin of the first dorsal fin. A, Apristurus longicephalus, HUMZ 170382, male, 475 mm TL; B, Asymbolus rubiginosus, AMS I.30393-004, male, 527 mm TL; C, Atelomycterus fasciatus, CSIRO H1298-7, male, 370 mm TL; D, Cephaloscyllium sufflans, SAIAB 6242, male, 800 mm TL; E, Cephalurus cephalus, USNM 221527, female, 285 mm TL; F, Galeus antillensis, UF 77853, female, 370 mm TL; G, Halaelurus natalensis, SAIAB 26951, male, 400 mm TL; H, Haploblepharus edwardsii, AMNH 40988, male, 480 mm TL; I, Holohalaelurus regani, SAIAB 25717, male, 610 mm TL; J, Parmaturus xaniurus, CAS 232152, female, 450 mm TL; K, Poroderma africanum, SAIAB 25343, male, 920 mm TL; L, Schroederichthys saurisqualus, UERJ uncatalogued, female, 564 mm TL. Scale bar: 250 µm.
Figure 20 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 20 Detail of the hyomandibular cartilage; internal surface. A, Scyliorhinus haeckelii, UERJ 1691, male, 522 mm TL; B, Apristurus longicephalus, HUMZ 170382, male, 475 mm TL. 2358.
Figure 12 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 12 Detail of muscle coracobranchialis. A, Cephaloscyllium umbratile, USP uncatalogued, male, 409 mm TL; B, Schroederichthys saurisqualus, UERJ uncatalogued, female, 564 mm TL. bbq, basibranchial; cb I–V, ceratobranchials I–V; cbb, basibranchial copula; cor, coracoid bar; hb II–III, hypobranchials II–III; mcb II–IV, muscles coracobranchialis I–IV; mpc, pericardial membrane.
Figure 13 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 13 Neurocranium; dorsal view. A, Scyliorhinus ugoi, USNM 221611, male, 432 mm TL; B, Cephaloscyllium variegatum, AMS I.43762-001, female, 670 mm TL; C, Schroederichthys saurisqualus, UERJ uncatalogued, female, 564 mm TL; D, Atelomycterus fasciatus, CSIRO H1298-7, male, 370 mm TL; E, Asymbolus rubiginosus, AMS I.30393-004, male, 527 mm TL; F, Figaro boardmani, CSIRO H989-5, female, 465 mm TL; G, Apristurus longicephalus, HUMZ 170382, male, 475 mm TL. af, anterior fontanelle; asc, anterior semicircular canal; eph, epiphyseal notch; foe, external foramen of preorbital canal; ins, internasal septum; iof, infraorbital canal of the lateral line; lrc, lateral rostral cartilage; mrc, medial rostral cartilage; nc, nasal capsule; pep, preorbital process; prf, parietal fossa; psc, posterior semicircular canal; pt, pterotic process; ptp, postorbital process; sc, supraorbital crest.
Figure 10 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 10 Detail of the insertion region of the muscle coracomandibularis. A, Cephaloscyllium umbratile, USP uncatalogued, male, 409 mm TL; B, Haploblepharus edwardsii, AMNH 40988, male, 480 mm TL. cbm, basimandibular cartilage; cor, coracoid bar; mca, muscle coracoarcualis; mch, m. coracohyoideus; mck, Meckel's cartilage; mcm, m. coracomandibularis.
Figure 11 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 11 Hypobranchial musculature. A, Cephaloscyllium umbratile, uncatalogued, male, 409 mm TL; B, Galeus antillensis, UF 77853, female, 370 mm TL; C, Holohalaelurus regani, SAIAB 25717, male, 610 mm TL; D, Apristurus longicephalus, HUMZ 170382, male, 475 mm TL; bh, basihyal; bhf, adjacent flap of the basihyal; ch, ceratohyal; cor, coracoid bar; mca, muscle coracoarcualis; mch, m. coracohyoideus; mcm, m. coracomandibularis; ocm, origin of the m. coracomandibularis.
Figure 1 from: Soares KDA, de Carvalho MR (2020) Phylogenetic relationship of catshark species of the genus Scyliorhinus (Chondrichthyes, Carcharhiniformes, Scyliorhinidae) based on comparative morphology. Zoosystematics and Evolution 96(2): 345-395. https://doi.org/10.3897/zse.96.52420
Figure 1 Ventral view of the head. A, Scyliorhinus canicula, MNHN 1999–1732, female, 418.5 mm TL; B, Schroederichthys saurisqualus, UERJ uncatalogued, female, 564 mm TL; C, Holohalaelurus regani, SAIAB 25717, male, 610 mm TL. anf, anterior nasal flap; llf, lower labial furrow; mrd, mesonarial crest; ulf, upper labial furrow. Scale bar: 20 mm.
Supplementary material 1 from: Zhongying Q, Huihui C, Hao Y, Yuan H, Huimeng L, Xia L, Xingchun G (2020) Comparative mitochondrial genomes of four species of Sinopodisma and phylogenetic implications (Orthoptera, Melanoplinae). ZooKeys 969: 23-42. https://doi.org/10.3897/zookeys.969.49278
Tables S1–S7
Figure 3 from: Zhongying Q, Huihui C, Hao Y, Yuan H, Huimeng L, Xia L, Xingchun G (2020) Comparative mitochondrial genomes of four species of Sinopodisma and phylogenetic implications (Orthoptera, Melanoplinae). ZooKeys 969: 23-42. https://doi.org/10.3897/zookeys.969.49278
Figure 3 The long polythymine stretch and conserved sequence blocks in the A+T rich regions from four species. Note: The long polythymine stretch. T-stretch sequence was labelled with box, located in the majority strand. Within each block, nucleotides identical in the two sequences are bottom-marked with asterisks.
Supplementary material 2 from: Zhongying Q, Huihui C, Hao Y, Yuan H, Huimeng L, Xia L, Xingchun G (2020) Comparative mitochondrial genomes of four species of Sinopodisma and phylogenetic implications (Orthoptera, Melanoplinae). ZooKeys 969: 23-42. https://doi.org/10.3897/zookeys.969.49278
Figures S1–S4
Generalized hidden Markov models for phylogenetic comparative datasets
<ol> <li class="JamesManuscriptBody">Hidden Markov models (HMM) have emerged as an important tool for understanding the evolution of characters that take on discrete states. Their flexibility and biological sensibility make them appealing for many phylogenetic comparative applications.</li> <li class="JamesManuscriptBody">Previously available packages placed unnecessary limits on the number of observed and hidden states that can be considered when estimating transition rates and inferring ancestral states on a phylogeny.</li> <li class="JamesManuscriptBody">To address these issues, we expanded the capabilities of the R package corHMM to handle <i>n</i>-state and <i>n</i>-character problems and provide users with a streamlined set of functions to create custom HMMs for any biological question of arbitrary complexity.</li> <li class="JamesManuscriptBody">We show that increasing the number of observed states increases the accuracy of ancestral state reconstruction. We also explore the conditions for when an HMM is most effective, finding that an HMM is an appropriate model when the degree of rate heterogeneity is moderate to high.</li> <li class="JamesManuscriptBody">Finally, we demonstrate the importance of these generalizations by reconstructing the phyllotaxy of the ancestral angiosperm flower. Partially contradicting previous results, we find the most likely state to be a whorled perianth, whorled androecium, whorled gynoecium. The difference between our analysis and previous studies was that our modeling explicitly allowed for the correlated evolution of several flower characters.</li> </ol>
Data from: A penalized likelihood framework for high- dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution
Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.
Data from: Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum
The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no comprehensive non-parametric framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.
Data from: Interpreting the evolutionary regression: the interplay between observational and biological errors in phylogenetic comparative studies
Regressions of biological variables across species are rarely perfect. Usually there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major-axis and reduced major-axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
Data from: Kakusan4 and Aminosan: two programs for comparing nonpartitioned, proportional, and separate models for combined molecular phylogenetic analyses of multilocus sequence data
Proportional and separate models able to apply different combination of substitution rate matrix and among-site rate variation model to each locus are frequently used in phylogenetic studies of multilocus data. However, the selection from among nonpartitioned (i.e., a common combination of models is applied to all-loci concatenated sequences), proportional, and separate models is usually based on the researcher's preference rather than on any information criteria. The present study describes two programs, "Kakusan4" (for DNA sequences) and "Aminosan" (for amino-acid sequences), that allow the selection of evolutionary models based on several types of information criteria. The programs can handle both multilocus and single-locus data, in addition to providing an easy-to-use wizard interface and a non-interactive command line interface. In the case of multilocus data, substitution rate matrices and among-site rate variation models are compared at each locus and at all-loci concatenated sequences, after which nonpartitioned, proportional, and separate models are compared based on information criteria. The programs also provide model configuration files for MrBayes, PAUP*, PHYML, RAxML, and Treefinder to support further phylogenetic analysis using a selected model. The best-fit models were found to differ depending on the data set. Furthermore, differences in the information criteria among nonpartitioned, proportional, and separate models were much larger than those among the nonpartitioned models. These findings suggest that selecting from nonpartitioned, proportional, and separate models results in a better phylogenetic tree. Kakusan4 and Aminosan are available at http://www.fifthdimension.jp/. They are licensed under GNU GPL Ver.2, and are able to run on Windows, MacOS X, and Linux.
Data from: Comparing the rates of speciation and extinction between phylogenetic trees
Over the past decade or so it has become increasingly popular to use reconstructed evolutionary trees to investigate questions about the rates of speciation and extinction. Although the methodology of this field has grown substantially in its sophistication in recent years, here I'll take a step back to present a very simple model that is designed to investigate the relatively straightforward question of whether the tempo of diversification (speciation and extinction) differs between two or more phylogenetic trees, without attempting to attribute a causal basis to this difference. It is a likelihood method, and I demonstrate that it generally shows type I error that is close to the nominal level. I also demonstrate that parameter estimates obtained with this approach are largely unbiased. Since this method can be used to compare trees of unknown relationship, it will be particularly well-suited to problems in which a difference in diversification rate between clades is suspected, but in which these clades are not particularly closely related. Since diversification methods can easily take into account an incomplete sampling fraction, but missing lineages are assumed to be missing at random, this method is also appropriate for cases in which we've hypothesized a difference in the process of diversification between two or more focal clades, but in which many un-sampled groups separate the few of interest. The method of this study is by no means an attempt to replace more sophisticated models in which, for instance, diversification depends on the state of an observed or unobserved discrete or continuous trait. Rather, my intention is to provide a complementary approach for circumstances in which a simpler hypothesis is warranted and of biological interest.
Data from: A unifying comparative phylogenetic framework including traits coevolving across interacting lineages
Models of phenotypic evolution fit to phylogenetic comparative data are widely used to make inferences regarding the tempo and mode of trait evolution. A wide range of models is already available for this type of analysis, and the field is still under active development. One of the most needed development concerns models that better account for the effect of within- and between-clade interspecific interactions on trait evolution, which can result from processes as diverse as competition, predation, parasitism, or mutualism. Here, we begin by developing a very general comparative phylogenetic framework for (multi)-trait evolution that can be applied to both ultrametric and nonultrametric trees. This framework not only encapsulates many previous models of continuous univariate and multivariate phenotypic evolution, but also paves the way for the consideration of a much broader series of models in which lineages coevolve, meaning that trait changes in one lineage are influenced by the value of traits in other, interacting lineages. Next, we provide a standard way for deriving the probabilistic distribution of traits at tip branches under our framework. We show that a multivariate normal distribution remains the expected distribution for a broad class of models accounting for interspecific interactions. Our derivations allow us to fit various models efficiently, and in particular greatly reduce the computation time needed to fit the recently proposed phenotype matching model. Finally, we illustrate the utility of our framework by developing a toy model for mutualistic coevolution. Our framework should foster a new era in the study of coevolution from comparative data.
Data from: Phylogenetic and comparative genomics of the family Leptotrichiaceae and introduction of a novel fingerprinting MLVA for Streptobacillus moniliformis
Background: The Leptotrichiaceae are a family of fairly unnoticed bacteria containing both microbiota on mucous membranes as well as significant pathogens such as Streptobacillus moniliformis, the causative organism of streptobacillary rat bite fever. Comprehensive genomic studies in members of this family have so far not been carried out. We aimed to analyze 47 genomes from 20 different member species to illuminate phylogenetic aspects, as well as genomic and discriminatory properties. Results: Our data provide a novel and reliable basis of support for previously established phylogeny from this group and give a deeper insight into characteristics of genome structure and gene functions. Full genome analyses revealed that most S. moniliformis strains under study form a heterogeneous population without any significant clustering. Analysis of infra-species variability for this highly pathogenic rat bite fever organism led to the detection of three specific variable number tandem analysis loci with high discriminatory power. Conclusions: This highly useful and economical tool can be directly employed in clinical samples without laborious prior cultivation. Our and prospective case-specific data can now easily be compared by using a newly established MLVA database in order to gain a better insight into the epidemiology of this presumably under-reported zoonosis.
Data from: Inferring bounded evolution in phenotypic characters from phylogenetic comparative data
Our understanding of phenotypic evolution over macroevolutionary timescales largely relies on the use of stochastic models for the evolution of continuous traits over phylogenies. The two most widely used models, Brownian motion and the Ornstein–Uhlenbeck (OU) process, differ in that the latter includes constraints on the variance that a trait can attain in a clade. The OU model explicitly models adaptive evolution toward a trait optimum and has thus been widely used to demonstrate the existence of stabilizing selection on a trait. Here we introduce a new model for the evolution of continuous characters on phylogenies: Brownian motion between two reflective bounds, or Bounded Brownian Motion (BBM). This process also models evolutionary constraints, but of a very different kind. We provide analytical expressions for the likelihood of BBM and present a method to calculate the likelihood numerically, as well as the associated R code. Numerical simulations show that BBM achieves good performance: parameter estimation is generally accurate but more importantly BBM can be very easily discriminated from both BM and OU. We then analyze climatic niche evolution in diprotodonts and find that BBM best fits this empirical data set, suggesting that the climatic niches of diprotodonts are bounded by the climate available in Australia and the neighboring islands but probably evolved with little additional constraints. We conclude that BBM is a valuable addition to the macroevolutionary toolbox, which should enable researchers to elucidate whether the phenotypic traits they study are evolving under hard constraints between bounds.
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.