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493 results for “Multivariate”
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 6 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 12 of 16
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Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 14 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 8 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 13 of 16
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Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 15 of 16
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Supplemental data from: Exploring phylogenetic signal in multivariate phenotypes by maximizing Blomberg’s K
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Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 2 of 16
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Data for Avolio et al. Making sense of multivariate community responses in global change experiments
Ecological communities are being impacted by global change worldwide. Experiments are a powerful tool to understand how global change will impact communities by comparing control and treatment replicates. Communities consist of multiple species and their associated abundances making multivariate methods a powerful approach to studying community compositional differences between control and treated replicates. Dissimilarity metrics are a commonly employed multivariate measure of compositional differences; however, while highly informative, dissimilarity metrics do not elucidate the specific ways in which communities differ. Integrating two multivariate methods, dissimilarity metrics and rank abundance curves has the potential to detect complex differences based on dissimilarity metrics and detail the how these differences came about through differences in richness, evenness, species ranks, or species identity. Here we use a database of 106 global change experiments located in herbaceous ecosystems and explore how patterns of ordinations based on dissimilarity metrics relate to rank abundance-curve based differences. We find that combining dissimilarity metrics alongside rank abundance curve-based measures clarify how global change treatments are altering communities. We find that when there is no difference in community composition (no distance between centroids of control and treated replicates), there are rarely differences in species ranks or species identities and more often differences in richness or evenness alone. In contrast, when there are differences between centroids of control and treated replicates, this is most often associated with differences in ranks either alone or co-occurring with differences in richness, evenness, or species identities. We suggest that integrating these two multivariate measures of community composition results in a deeper understanding of how global change impacts communities.
Multivariate time series for testing -- RacketSports dataset
<p>The original data was retrieved from http://www.timeseriesclassification.com/description.php?Dataset=RacketSports</p> <p>Original data description:<br> The data was created by university students plyaing badminton or squash whilst wearing a smart watch (Sony Smart watch 35). The watch relayed the x-y-z coordinates for<br> both the gyroscope and accelerometer to an android phone (One Plus 56). The phone<br> wrote these values to an Attribute-Relation File Format (arff) file using an app developed<br> by a UEA computer science masters student. The problem is to identify which sport and which stroke the players are making. The data was collected at a rate of 10 HZ over 3 seconds whilst the player played<br> either a forehand/backhand in squash or a clear/smash in badminton.<br> The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.</p> <p>Pre-processing<br> Data processing was done as described in: https://github.com/NLeSC/mcfly-tutorial/blob/master/utils/tutorial_racketsports.py<br> The original data was split into train and test set. Here the data was loaded and further divided into train, test, validation sets.<br> To keep it simple we here simply divided the original test part into test and validation.<br> The resulting data was stored as numpy .npy files.</p> <p>The zip file contains three sets of time series data (X_train, X_test, X_valid) and the respective labels (y_train, y_test, y_valid).</p> <p>Reference:<br> http://www.timeseriesclassification.com/description.php?Dataset=RacketSports<br> (The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.)</p>
Complex multi-trait responses to multivariate environmental cues in a seasonal butterfly
<p>Dataset for 'Complex multi-trait responses to multivariate environmental cues in a seasonal butterfly'.</p>
A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
<p>The spectra were provided as unprocessed raw data in the manufacturers data format (Bruker), as labelled two zip archives with SARS CoV 2 positives and negative, according to the reviewer's recommendation.</p> <p>This information belongs to the publication (in review in <em>Journal of Virological Methods</em>)<br> "A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs"<br> All the information belongs to the National Reference Institute, INEI-ANLIS DR CARLOS G MALBRAN, BUENOS AIRES, ARGENTINA.</p>
Interspecific allometry for sexual shape dimorphism: macroevolution of multivariate sexual phenotypes with application to Rensch's rule
<p>Allometric trends in the degree of sexual dimorphism with body size have long fascinated evolutionary biologists. Many male-biased clades display more prominent sexual dimorphism in larger taxa (Rensch's rule), with most examples documenting this pattern for body size dimorphism. While sexual dimorphism in traits other than body size is equally functionally relevant, characterizing allometric patterns of sexual dimorphism in such traits is hampered by lack of an analytical framework that can accommodate multivariate phenotypes. In this paper we derive a multivariate equivalency for investigating trends in sexual dimorphism – relative to overall body size – across taxa and provide a generalized test to determine whether such allometric patterns correspond with Rensch's rule. For univariate linear traits like body size, our approach yields equivalent results to those from standard procedures, but our test is also capable of detecting trends in multivariate datasets like shape. Computer simulations reveal the method displays appropriate statistical properties, and an empirical example in Mediterranean lizards provides the first demonstration of Rensch's rule in a multivariate phenotype (head shape). Our generalized procedure substantially extends the analytical toolkit for investigating macroevolutionary patterns of sexual dimorphism and seeking a better understanding of the processes that underlie them.</p>
NPLM: Learning Multivariate New Physics
<p>Archive of synthetic data used for the studies presented in <a href="https://arxiv.org/abs/1912.12155">arXiv:1912.12155</a></p>
Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.
Egg-induced changes to sperm phenotypes shape patterns of multivariate selection on ejaculates
<p class="MsoPlainText">Ejaculates exhibit extraordinary phenotypic diversity and rapid rates of evolution, yet the adaptive value of most sperm traits remains equivocal. Recent findings suggest that to understand how selection targets ejaculates we must recognize that female-imposed physiological conditions often alter ejaculate phenotypes. These phenotypic changes to ejaculates may influence the relationships among sperm traits and their association with fitness. Here, we show that chemical substances released by eggs (known to modify sperm physiology and behavior) alter patterns of selection on a suite of sperm traits in the mussel Mytilus galloprovincialis. We use multivariate selection analyses to characterize linear and nonlinear selection acting on sperm traits in (a) seawater alone, and (b) seawater containing egg-derived chemicals (egg water). Our analyses revealed that nonlinear selection on canonical axes of multiple traits (notably sperm velocity, sperm linearity and percentage of motile sperm) was the most important form of selection overall, but importantly these patterns were only evident when sperm phenotypes were measured in egg water. These findings reveal the subtle way that females can alter patterns of selection, with the implication that overlooking environmentally-moderated changes to ejaculate phenotypes may result in erroneous interpretations of how selection targets phenotypic (co)variation in ejaculate traits.</p>
Data from: A multivariate study of differentiating characters between three European species of the genus Lasiochernes Beier, 1932 (Pseudoscorpiones, Chernetidae)
Morphological variation in three rarely collected European species of the genus Lasiochernes Beier, 1932 is thoroughly examined in the present study. Detailed descriptions of previously ignored morphological characters of L. cretonatus Henderickx, 1998, L. jonicus (Beier, 1929) and L. pilosus (Ellingsen, 1910) are presented. The female of L. cretonatus and the nymphs of L. pilosus are described for the first time. Multivariate morphometric techniques (principal coordinate analysis and discriminant analyses) were employed to confirm morphological differentiation of the three Lasiochernes species and to identify the most reliable characters for their separation. The usefulness of particular body parts for species identification was evaluated. An identification key for the females of the Lasiochernes species studied is provided. Geographic distribution and habitat preferences of the three species are summarized.
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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.