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.
493
datasets available to search
ShareScore release 0.9.0
Dataset results
493 results for “Multivariate”
FIGURE 3 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 3. UPGMA trees (males) derived from Mahalanobis' distances among sample centroids. See text for interpretation.
FIGURE 8 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 8. Box Plots of dorsalia values in different populations of the D, clarkorum/dryada studied (numbers of samples as in Material and Methods): 1) Sümela, 2) Çataldere, 3) Hemşin, 4) Ayder, 5) Hatila Plateau, 6) Ortacalar 7) Subaşı & Yoldere (Turkish "dryada"), 8) Cankurtaran Pass, 9) Karagöl, 10) Charnaly River Gorge (Georgian D. dryada). The center is the median, the length of the rectangle is the interquartile range (from the 25th to the 75th percentile) and represents the central 50% of data. Line is the spread from minimum to maximum values. Green point is a severe outlier.
FIGURE 2 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 2. Canonical Discriminant Analysis (CDA) plots for males. Specimens, sample centroids and group perimeters are represented. For details, percentage of variance explained, and other data, see text.
FIGURE 5 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 5. UPGMA trees (females) derived from Mahalanobis' distances among sample centroids. See text for interpretation.
FIGURE 1. Map showing the localities from where specimens have been studied. 1 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 1. Map showing the localities from where specimens have been studied. 1. Sümela, Maçka, Trabzon, northeastern Anatolia, Turkey, 2. Çataldere, Kaptanpaşa, Rize, northeastern Anatolia, Turkey, 3. 11 km southwest of Hemşin, Rize, northeastern Anatolia, Turkey, 4. Ayder Plateau, Çamlıhemşin, Rize, northeastern Anatolia, Turkey, 5. Hatila Plateau, Artvin, northeastern Anatolia, Turkey, 6. 16 km northeast of Ortacalar, Arhavi, Artvin, northeastern Anatolia, Turkey, 7. Subaşı Village, Hopa, Artvin, northeastern Anatolia, Turkey, 8. Cankurtaran, Hopa, Artvin, northeastern Anatolia, Turkey, 9. Karagöl, Borçka, Artvin, northeastern Anatolia, Turkey, 10. Charnaly, Georgia
FIGURE 4 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 4. Canonical Discriminant Analysis (CDA) plots for females. Specimens, sample centroids and group perimeters are represented. For details, percentage of variance explained, and other data, see text.
FIGURE 9 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 9. Plot showing the inverse relationship between altitude and dorsalia mean values in the studied D. clarkorum/D. dryada populations (Spearman Rank Correlation coefficient: ρ = -0.72; P=0.01).
FIGURE 7 in Multivariate analysis of geographic variation in Darevskia clarkorum (Darevsky & Vedmederja, 1977), correlation with geographic and climatic parameters, and true status of Darevskia dryada (Darevsky & Tuniyev, 1997)
FIGURE 7. UPGMA trees derived from Mahalanobis' distances among sample centroids. See text for interpretation (males and females together, Turkey and Georgia).
Data from: Extremophile Poeciliidae: multivariate insights into the complexity of speciation along replicated ecological gradients
Background: Replicate population pairs that diverge in response to similar selective regimes allow for an investigation of (a) whether phenotypic traits diverge in a similar and predictable fashion, (b) whether there is gradual variation in phenotypic divergence reflecting variation in the strength of natural selection among populations, (c) whether the extent of this divergence is correlated between multiple character suites (i.e., concerted evolution), and (d) whether gradual variation in phenotypic divergence predicts the degree of reproductive isolation, pointing towards a role for adaptation as a driver of (ecological) speciation. Here, we use poeciliid fishes of the genera Gambusia and Poecilia that have repeatedly evolved extremophile lineages able to tolerate high and sustained levels of toxic hydrogen sulfide (H2S) to answer these questions. Results: We investigated evolutionary divergence in response to H2S in Gambusia spp. (and to a lesser extent Poecilia spp.) using a multivariate approach considering the interplay of life history, body shape, and population genetics (nuclear miscrosatellites to infer population genetic differentiation as a proxy for reproductive isolation). We uncovered both shared and unique patterns of evolution: most extremophile Gambusia predictably evolved larger heads and offspring size, matching a priori predictions for adaptation to sulfidic waters, while variation in adult life histories was idiosyncratic. When investigating patterns for both genera (Gambusia and Poecilia), we found that divergence in offspring-related life histories and body shape were positively correlated across populations, but evidence for individual-level associations between the two character suites was limited, suggesting that genetic linkage, developmental interdependencies, or pleiotropic effects do not explain patterns of concerted evolution. We further found that phenotypic divergence was positively correlated with both environmental H2S-concentration and neutral genetic differentiation (a proxy for gene flow). Conclusions: Our results suggest that higher toxicity exerts stronger selection, and that divergent selection appears to constrain gene flow, supporting a scenario of ecological speciation. Nonetheless, progress toward ecological speciation was variable, partially reflecting variation in the strength of divergent selection, highlighting the complexity of selective regimes even in natural systems that are seemingly governed by a single, strong selective agent.
Data from: Temporally variable multivariate sexual selection on sexually dimorphic traits in a wild insect population
A widely held view is that the strength and form of natural selection varies in time and space in response to varying ecological forces; however, adequate quantitative evaluations of this are relatively scarce. In this study, we measured the strength and form of sexual selection acting on a suite of male morphological traits in a wild ambush bug (Phymata americana) population at 10 sampling dates over 2 years. We tested the prediction that the strength and direction of sexual selection would be associated with one or more important ecological variables. We found that patterns of multivariate selection varied considerably over time, and even within a season. Yet, for this population, a sexually dimorphic color pattern trait was consistently a target of directional selection. The strength of sexual selection on this trait was related to both sex ratio and density, which is consistent with the idea that ecological factors can play an important role in generating patterns of sexual selection. We also demonstrate that the median strength of linear selection obtained from replicated cross‐sectional methods was qualitatively similar to the estimates obtained from longitudinal methods, providing multiple lines of evidence that the evolution of sexual color dimorphism in this species is attributable to sexual selection.
Data from: Multivariate sexual selection on ejaculate traits under sperm competition
The widespread prevalence of sperm competition means that ejaculates face intense sexual selection. However, prior investigations of sexual selection on gametes have been hampered by two difficulties: (1) deriving estimates of relative fitness from sperm competition trials that are comparable across rival male and female genotypes; and (2) obtaining measures of competitive fertilization success that are not confounded by post-zygotic effects. Here, we exploit the experimental tractability of a broadcast spawning marine invertebrate to overcome these challenges and characterise multivariate sexual selection on sperm traits when multiple ejaculates compete. In multi-male spawning events, we tracked real-time success of sperm using fluorescent tags that are visible inside fertilized eggs. We then used multivariate selection analyses to identify patterns of linear and non-linear sexual selection on multiple sperm morphology and motility traits. Specifically, we found non-linear selection against divergent combinations of sperm length, velocity and swimming path linearity. These patterns likely reflect the way different swimming strategies allow sperm to locate and track eggs. Our results demonstrate that there are overall patterns of selection on ejaculates across a biologically realistic range of ejaculate-ejaculate and ejaculate-female interactions; therefore, there is the potential for adaptive evolution of ejaculate traits under sperm competition.
Data from: Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence
BACKGROUND: Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. OBJECTIVE: To study the potential association between allelic variants of dopamine D2 receptor (DRD2), ANKK1 (ankyrin repeat and kinase domain containing 1), dopamine D4 receptor (DRD4), Catechol-O-methyl transferase (COMT) and dopamine transporter (SLC6A3) genes and heroin dependence in Hungarian patients. METHODS: 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs): rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs) were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also provide a multivariate model for the associations among them implying Bayesian networks in Bayesian multilevel analysis. FINDINGS AND CONCLUSIONS: In single marker analysis the TaqIA (rs1800497) and TaqIB (rs1079597) variants were associated with heroin dependence. Moreover, -521 C/T SNP (rs1800955) of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A) allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462) of the DRD4 gene and this effect is mediated through the -521 C/T (rs1800955) polymorphism in the promoter.
Data from: Multivariate phylogenetic comparative methods: evaluations, comparisons, and recommendations
Recent years have seen increased interest in phylogenetic comparative analyses of multivariate datasets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the dataset, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for datasets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the dataset, and the number of trait dimensions. The consequences of these debilitating deficiencies is that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the dataset. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, while algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons.
Data from: Differential drivers of benthic foraminiferal and molluscan community composition from a multivariate record of Early Miocene environmental change
Climate changes are multivariate in nature, and disentangling the proximal drivers of biotic responses to paleoclimate events requires time series of multiple environmental proxies. We reconstruct a multivariate time series of local environmental change for the early Miocene Newport Member of the Astoria Formation (20.26–18 Ma), using proxies for temperature (δ18O), productivity (δ13C), organic carbon flux (Δδ13C), oxygenation (δ15N), and sedimentary grain size (% mud). Our data suggest increases in productivity and declines in oxygenation on the Oregon shelf during this interval of global warming. We evaluate the association of individual environmental factors, and combinations of factors, with changes in faunal composition observed in benthic foraminiferal and molluscan communities collected from the exact same sediments as the environmental data. The δ15N values are the most parsimonious correlates with major changes in foraminiferal composition, whereas molluscan composition is most closely related to δ13C values, suggesting that different components of the environment are influencing each group. When the proxies that have the best supported relationships with the faunal gradients are removed from the analyses to simulate the absence of those proxy data, significant relationships between the faunal gradients and the remaining environmental proxies can still be found. This suggests that environmental drivers can be incorrectly attributed to faunal changes when key proxy data are missing. Paleoecological studies of biotic response that test multiple environmental drivers for multiple taxonomic groups are powerful tools for identifying the ecological consequences of past warming events and the regional drivers of ecological changes.
Evolution of multivariate wing allometry in schizophoran flies (Diptera: Schizophora)
<p>The proximate and ultimate mechanisms underlying scaling relationships as well as their evolutionary consequences remain an enigmatic issue in evolutionary biology. Here I investigate the evolution of wing allometries in the Schizophora, a group of higher Diptera that radiated ~65 million years ago, by studying static allometries in five species using multivariate approaches. Despite the vast ecological diversity observed in contemporary members of the Schizophora and independent evolutionary histories throughout most of the Cenozoic, size-related changes represent a major contributor to overall variation in wing shape, both within and among species. Static allometries differ between species and sexes, yet allometric changes are correlated across species, suggesting a shared program underlying size-dependent developmental plasticity. Lastly, static allometries within species also correlate with evolutionary divergence across 33 different families (belonging to 11 of 13 superfamilies) of the Schizophora. This again points towards a general developmental, genetic, or evolutionary mechanism that canalizes or maintains the covariation between shape and size in spite of rapid ecological and morphological diversification during the Cenozoic. I discuss the putative roles of developmental constraints and natural selection in the evolution of wing allometry in the Schizophora.</p>
Accompanying simulated data for "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
Figure 5 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 5. Box plots with whiskers showing the variation among locomotion modes of percentage prediction errors (%PE) for the multiple regression analysis (MR; grey) and the single regression analysis (SR; white) equations adjusted in modern birds. Box length shows the interquartile range (25th and 75th percentiles). The horizontal line within boxes indicates the median. Vertical lines show the 5–95% confidence limits. Points indicate values outside these limits (i.e. outliers). Black horizontal lines between the grey and white boxes show the %PE estimates obtained with the equation based on humeral length (HL), the osteological variable less affected by ecological groupings (as shown by its lower F-statistic values; see Tables 6 and 7). %PE values greater than zero indicate an underestimation and %PE values lower than zero indicate an overestimation. A, %PE variation with respect to groups of aerial locomotion. Abbreviations: CF, continuous flapping; FG, flapping and gliding; FB, flapping and bounding; S, soaring. B, %PE variation with respect to groups of non-aerial locomotion. Abbreviations: A, aquatic; TG, terrestrial ground-dwelling; TN, terrestrial non-ground-dwelling; ATG, aquatic and grounddwelling; TGN, perching and ground-dwelling.
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 1. Calibrated phylogeny for the main avian taxa. Tree topology was obtained from O'Connor, Chiappe & Bell (2011) and divergence times are based on a 'literal' interpretation of the fossil record from Brockelhurst et al. (2012). Taxa abbreviations: Nth, Neornithes; Orph, Ornithuromorpha; Orn, Ornithothoraces; Orth, Ornithurae; Pyg, Pygostylia.
Figure 4 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 4. Biplots used in the selection of four variables for each fossil group of Mesozoic birds: humeral length (HL), femoral length (FL), diaphyseal craniocaudal width of ulna (dUW), and diaphyseal craniocaudal width of femur (dFWcc). Each predictor variable was plotted against a combination of the 14 remaining variables used for estimating body mass (BM; Table S3). The biplots show the regression line fitted for extant birds (grey circles) with the 95% confidence intervals for BM predictions (dotted lines). Following the procedure of selection of variables (see text), HL and FL were used for generating functions that can be applied to all fossil avian taxa. In contrast, dUW and dFWcc could not be incorporated into the functions adjusted for estimating BM in Archaeopterygidae and Enantiornithes, respectively.
Figure 3 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 3. Effects of weighting for the best-fitting multiple regression equation obtained from the modern data set (see Table 2). A, distribution of familiar residuals for unweighted data, and for data weighted by families. Each box plot with whiskers represents one family. Box length shows the interquartile range (25th and 75th percentiles). Horizontal lines indicate the 5–95% confidence limits. Asterisks show outliers. Abbreviations for families: Ac, Accipitridae; Ad, Alcedinidae; Ae, Aegothelidae; Al, Alcidae; An, Anatidae; Ap, Apodidae; Ar, Ardeidae; At, Artamidae; Au, Alaudidae; Ca, Caprimulgidae; Cc, Cacatuidae; Ch, Charadriidae; Ci, Ciconiidae; Co, Columbidae; Cn, Cinclidae; Cr, Coraciidae; Ct, Catharthidae; Cu, Cuculidae; Cv, Corvidae; Di, Diomedeidae; Fa, Falconidae; Fg, Fringillidae; Fr, Fregatidae; Ga, Gaviidae; Gr, Gruidae; He, Hemiprocnidae; Hi, Hirundinidae; La, Laridae; Ln, Lanidae; Me, Meleagridae; Mg, Megapodidae; Mo, Motacillidae; Mp, Meropidae; Ms, Musophagidae; Mu, Muscicapidae; Ot, Otididae; Pa, Paridae; Pc, Pelecanoididae; Pd, Podicipedidae; Pe, Pelecanidae; Ph, Phasianidae; Pic, Picidae; Pit, Pittidae; Pl, Phalacrocoracidae; Pn, Pandionidae; Po, Podargidae; Pr, Procellaridae; Ps, Psittacidae; Pt, Pteroclidae; Pu, Prunellidae; Ra, Rallidae; Re, Recurvirostridae; Ry, Rynchopidae; Sc, Stercoriidae; Sg, Strigidae; So, Scolopacidae; Sr, Sturnidae; St, Sternidae; Su, Sulidae; Sy, Sylviidae, Te, Tetraonidae; Th, Threskiornithidae; Tt, Tytonidae; Tu, Turdidae; Ty, Tyrannidae; Up, Upupidae. B, plot showing the variations of %MPE with the increase of individuals per family. The dashed line represents the unweighted multiple regression analysis (MR) and the grey line represents the weighted MR.
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.