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493 results for “Multivariate”
Fig. 3 in Morphotype And Multivariate Analysis Of The Occlusal Pattern Of The First Lower Molar In European And Asian Arvicoline Species (Rodentia, Microtus, Alexandromys)
Fig. 3. Differentiation on six East Asian vole samples by the morphotypic variation of the occlusal pattern.
Fig. 1 in Morphotype And Multivariate Analysis Of The Occlusal Pattern Of The First Lower Molar In European And Asian Arvicoline Species (Rodentia, Microtus, Alexandromys)
Fig. 1. Elements of the occlusal surface of m1 (the terminology follows van der Meulen, Zagwijn, 1974; Maul et al., 2007).
Fig. 1 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach
Fig. 1. Distribution of centroids for the age-based song thrush samples in the factor space of PC1 and PC2 (figs 1–13 denote nestling's age in days).
Fig. 3 in Growth Processes In The Postembryonic Development In Altricial Birds On The Example Of Song Thrush, Turdus Philomelos (Passeriformers, Turdidae): A Multivariate Approach
Fig. 3. Results of comparison of 20 morphometric traits (1–20) in nestlings of song thrush according to their DRPI and dynamics of growth processes. Numbers of traits as given in table 1.
Data and Scripts from: Bayesian prediction of multivariate ecology from phenotypic data yields novel insights into the diets of extant and extinct taxa
<p>Morphology often reflects ecology, enabling the prediction of ecological roles for taxa that lack direct observations such as fossils. In comparative analyses, ecological traits, like diet, are often treated as categorical, which may aid prediction and simplify analyses but ignores the multivariate nature of ecological niches. Futhermore, methods for quantifying and predicting multivariate ecology remain rare. Here, we ranked the relative importance of 13 food items for a sample of 88 extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether those rankings could be predicted from dental morphology and body size. Traditional diet categories fail to capture the true multivariate nature of carnivoran diets, but Bayesian regression models derived from living taxa have good predictive accuracy for importance ranks. Using our models to predict the importance of individual food items, the multivariate dietary niche, and the nearest extant analogs for a set of data-deficient extant and extinct carnivoran species confirms long-standing ideas for some taxa, but yields new insights about the fundamental dietary niches of others. Our approach provides a promising alternative to traditional dietary classifications. Importantly, this approach need not be limited to diet, but serves as a general framework for predicting multivariate ecology from phenotypic traits.</p>
Library of multivariate time series
<p>A database of many different types of multivariate time series, each with between 5-25 processes and between 100-2500 observations.</p> <p>The database contains a serialized Python dictionary of 1053 datasets, where the key for the dictionary is the dataset name, and each value is another dictionary of: "data", an MxT numpy array of processes-by-observations; and "labels", a list of descriptive labels for the dataset.</p>
FrenchPiezo: the French mainland groundwater level multivariate time series
<p><strong>FrenchPiezo </strong>is the French mainland multivariate time series dataset of groundwater levels (a.k.a piezometric levels) across the French mainland. The dataset contains 1026 multivariate time series, each made of 3 dimensions which are:</p> <ul> <li>the piezometric level (<strong>p</strong>),</li> <li>the precipitation (<strong>t</strong><strong>p</strong>),</li> <li>the evapotranspiration (<strong>e</strong>).</li> </ul> <p>Each time series is identified by a code (<strong>bss</strong>) which is the identifier of the piezometer used to measure the piezometric level. The measurements are sampled daily from January 2015 to January 2021, corresponding to 2221 days. The piezometric levels are collected from <a href="https://hubeau.eaufrance.fr/">Hub'Eau</a>, the French free API for accessing water data. The climate data <strong>tp </strong>and <strong>e </strong>are collected from the Copernicus ERA5 archive using their free API. These data are in the file <em>dataset_2015_2021.csv</em><strong> </strong>in which time series with more than 50 missing values have been dropped. The file <em>dataset_2015_2021_nomissing_linear.csv</em> is the same dataset in which missing values have been imputed using linear interpolation.</p> <p>In addition to the climate data, information about the nature of the soil where each time series is collected are downloaded from <a href="https://bdlisa.eaufrance.fr/decouvrir-la-bdlisa"><em>BDLISA</em></a> and are given in the file <em>dataset_stations.csv </em>. BDLISA classifies soils as hydrologic entities (EH), characterized by a set of attributes described <a href="https://www.sandre.eaufrance.fr/ftp/documents/fr/ddd/saq/2.1/sandre_dictionnaire_SAQ__2.1.pdf">here</a>: </p> <p>More information on how we created the dataset, the scripts used to collect the data, and some experiments on forecasting the future values of groundwater levels using global and local foresting methods can be found on our <a href="https://github.com/frankl1/piezoforecast">GitHub page</a>.</p>
Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
<p>The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.</p>
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
Dataset and Data treatment for Data mining Raman Microspectroscopic Responses of Cells to Drugs in Vitro using Multivariate Curve Resolution-Alternating Least Squares
<p><strong>Matlab scripts for the simulation and treatment of Raman datasets obtained from time dependent experiments Using MCR-ALS.</strong></p> <p> </p> <p><strong>- SIMULATED DATA: </strong>Simulated data is obtained by adding spectra of artificially generated responses (weighted considering artificially generated time profiles) to an experimental cell spectrum (Initial component) Three Different Scenarios are generated. </p> <p>Spectral and time profiles are obtained from here: </p> <p> </p> <p><strong>- EXPERIMENTAL DATA: </strong>DOX dataset obtained from here</p> <p>https://doi.org/10.1002/jbio.201800328</p> <p> </p> <p> </p> <p><strong>- DATA ANALYSIS INSTRUCTIONS</strong></p> <p>Run <em>datatreatment.m</em></p>
Linked collectors and determiners for: Multivariate discrimination and description of a new species of Tapinoma from the Western United States..
Natural history specimen data linked to collectors and determiners held within, "Multivariate discrimination and description of a new species of Tapinoma from the Western United States.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://bionomia.net/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745">https://gbif.org/dataset/7bae7513-ec11-4c9b-a570-3040c4734745</a>. Formatted as a Frictionless Data package.
FIGURE 1. Scatterplots from multivariate statistical analyses. Ellipses define the 95 in Morphological Variation in a Unisexual Whiptail Lizard (Aspidoscelis exsanguis) and One of Its Bisexual Parental Species (Aspidoscelis inornata) (Reptilia: Squamata: Teiidae): Is the Clonal Species Less Variable?
FIGURE 1. Scatterplots from multivariate statistical analyses. Ellipses define the 95% confidence limits of score distributions. A. Principal component scores of 14 field A. exsanguis, 42 laboratory A. exsanguis of two lineages pooled, and 19 field A. inornata. Axis percentages reflect variance explained by PC1 and PC2 (table 5). B. Canonical variate scores of the same specimens as in A. Axis percentages are relative contributions of CV1 and CV2 to the discrimination (table 5).
Figure 15 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 15. Geographical distribution of Pseudonaja nuchalis specimens examined in this study (closed circles) and/or included in Skinner et al.'s (2005) phylogenetic analysis (open circles represent specimens not included in the morphometric analyses). Codes for geographical groups correspond with those in Table 2.
Figure 14 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 14. Geographical distribution of Pseudonaja mengdeni specimens examined in this study (closed circles) and/or included in Skinner et al.'s (2005) phylogenetic analysis (open circles represent specimens not included in the morphometric analyses). Codes for geographical groups correspond with those in Table 2.
Figure 7 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 7. Plot of scores for first and second canonical roots extracted in a discriminant function analysis including all male specimens.
Figure 11 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 11. Geographical distribution of Pseudonaja aspidorhyncha specimens examined in this study (closed circles) and/or included in Skinner et al.'s (2005) phylogenetic analysis (open circles represent specimens not included in the morphometric analyses). Codes for geographical groups correspond with those in Table 2.
Figure 6 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 6. Plots of scores for first and second, and first and third canonical roots extracted in a discriminant function analysis including female P. affinis, P. nuchalis 'Darwin', P. nuchalis 'Orange with black head'-'Pale head, grey nape' and P. nuchalis 'Southern' clade group specimens.
Figure 2 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 2. Pseudonaja mengdeni (SAMA R20981), lateral perspective of head; see Table 1 for abbreviations.
Figure 5 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 5. Plot of scores for first and second canonical roots extracted in a discriminant function analysis including all female specimens.
Figure 9 in A multivariate morphometric analysis and systematic review of Pseudonaja (Serpentes, Elapidae, Hydrophiinae)
Figure 9. Plot of scores for first and second canonical roots extracted in a discriminant function analysis including male P. affinis, P. inframacula and P. textilis clade group specimens.
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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.