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493 results for “Multivariate”
Fig. 13 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 13. Dendrograms of the studied taxa constructed using unweighted pair group method (Rohlf, 1997). Inset shows portion of dendrogram which differs when both Hadar naviculars are considered as a single sample.
Fig. 12. A in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 12. A plot of the first two canonical variates with vectors representing the contribution of each of the measured variables to the scatter within and among measured taxa. Arrows point to fossils. Note the distinctiveness of H. sapiens, the uniqueness of Oreopithecus and the similarities of Hadar and African apes, of OH 8 and Homo, and of great ape species or subspecies within genera. The vectors representing the frontal mesoectocuneiform angle (EctMsFn) and the mediolateral diameter of the entocuneiform facet (EntFml) are nearly overlapping. Vector lengths are exagerated by a factor of ten, and owing to a twodimensional projection, are not proportional to their actual length. Eighty percent of the variance among means relative to the withingroup variance is summarized by the first two canonical variates (see figure 14 for plotted means of the first two canonical variates). Program written in Matlab version 5.1. This ''biplot'' is after Rohlf (1997); see Marcus (1993) for a discussion. The program and navicular data are available from one of us (LM). TalFlng = talar facet dorsoplantar (major axis) diameter, TalFwd = talar facet mediolateral (minor axis) diameter, EctFpd= ectocuneiform facet dorsoplantar diameter, EctFml= ectocuneiform facet mediolateral diameter, MesFdp= mesocuneiform facet dorsoplantar diameter, MesFml= mesocuneiform facet mediolateral diameter, EntFml= entocuneiform facet mediolateral diameter, EntFdp= entocuneiform facet dorsoplantar diameter, MaxLng= navicular maximum length, CuFdp= cuboid facet dorsoplantar diameter, CuFml= cuboid facet mediolateral diameter, TalFlDp= depth of talar facet along major axis, TalTrDp= depth of talar facet along minor axis; CubEcto = transverse cuboectocuneiform angle, EctMstr= transverse mesoectocuneiform angle, Tor= navicular torsion, EctMsFn= frontal mesoectocuneiform angle, TalEct= sagittal taloectocuneifrom angle, EctEmt= entoectocuneiform angle.
Fig. 11 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 11. Cube root of lower limbvolume (mm) vs. square root of total navicular crosssectional area (mm) in humans, great apes, and fossil hominoids.
Fig. 10 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 10. The sum of the femoral and tibial crosssectional areas (mm2) vs. talar facet crosssectional area (mm2) in humans, great apes, and fossil hominoids.
Fig. 9 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 9. Bivariate plot of cube root of body weight (kg⅓) vs. square root of total navicular facet crosssectional area (mm) in great apes. N = 62, Slope = 6.58, y intercept = 143.77. At 95% confidence limits OH 8, the two Hadar naviculars, and Oreopithecus were calculated to have body weights of 12.0– 100.3 kg, 17.6–143.6 kg (AL 33347), 18.6–155.1 kg (AL 33336), and 4.4–39.5 kg respectively.
Fig. 7 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 7. Dorsoplantar (major axis) diameter (mm) vs. mediolateral (minor axis) diameter (mm) of the talar facet (i.e., talar facet length vs. talar facet width) in humans, great apes, and fossil hominoids. Arrows point to fossils.
Fig. 4 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 4. Mesocuneiform facet crosssectional area (mm2) vs. total navicular facet crosssectional area
Fig. 3 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 3. Ectocuneiform facet crosssectional area (mm2) vs. total navicular facet crosssectional area
Fig. 5 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 5. Entocuneiform facet crosssectional area (mm2) vs. total navicular facet crosssectional area
Fig. 6 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 6. Cuboid facet crosssectional area (mm2) vs. total navicular facet crosssectional area (mm2) in humans, great apes, and fossil hominoids. Arrows point to fossils.
Fig. 1 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 1. Proximal (A), distal (B), lateral (C) and dorsal (D) views of the OH 8 navicular showing the measured lengths, and proximal (E), distal (F), lateral (G) and dorsal (H) views of a left gorilla navicular showing the measured angles; a = talar facet major axis (dorsoplantar) diameter, b = talar facet minor axis (mediolateral) diameter, c = ectocuneiform facet dorsoplantar diameter, d = ectocuneiform facet mediolateral diameter, e = mesocuneiform facet dorsoplantar diameter, f = mesocuneiform facet mediolateral diameter, g = entocuneiform facet mediolateral diameter, h = entocuneiform facet dorsoplantar diameter, i = navicular maximum length, j = cuboid facet dorsoplantar diameter, k = cuboid facet mediolateral diameter, l = depth of talar facet along major axis, m = depth of talar facet along minor axis; 1 = frontal talocuboid angle, 90° 2 = navicular torsion, 3 = frontal mesoectocuneiform angle, 4 = entoectocuneiform angle 5 = sagittal taloectocuneifrom angle, 6 = transverse mesoectocuneiform angle, 7 = transverse cuboectocuneiform angle. In all cases the lines chosen for angular measurements bisect facets into approximately equal halves. For comparative purposes the major bisecting axes of the talar head and cuneiform facets are referred to in the text as the dorsoplantar axes. In neither great apes nor humans do all these axes have a dorsoplantar orientation, but are held in varying inclination to a dorsoplantar axis according to talar head and navicular torison and the frontal mesoectocuneiform angle. The crosssectional area of the talar, ectocuneiform, mesocuneiform entocuneiform and cuboid facets are given by the products of a and b, c and d, e and f, g and h, and j and k, respectively. Relative crosssectional area for each facet is compared as a percentage of the sum of all of the navicular facets. The subtended angle of curvature and the radius of curvature of the talar facet along the dosoplantar (major) and mediolateral (minor) axes are given by 4 arctan(2l/a) and (l2 + a2/4)/ 2l, and 4 arctan(2m/b) and (m2 + b2/4)/2m, respectively.
Fig. 2 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 2. Talar facet crosssectional area (mm2) vs. total navicular facet crosssectional area (mm2) in humans, great apes, and fossil hominoids. Arrows point to fossils.
Figure 2 in Multivariate analysis of the effects of site factors on the distributions of raphignathoid mites (Acari: Raphignathoidea)
Figure 2. Total abundance values of the species according to habitat types (a), seasons (b) and elevation belts (c).
Figure 1 in Multivariate analysis of the effects of site factors on the distributions of raphignathoid mites (Acari: Raphignathoidea)
Figure 1. The frequency (1) and abundance (2) values of the species (a), genus (b), and families (c).
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnosis of Depression: Data and code
<p><strong>In the archive are the datasets and code used to create the results in article "Application of Mass Multivariate Analysis on Neuroimaging Data Sets fo Precision Diagnosis of Depression". The methods is based on the Multivariate Linear toolbox mad F. Kherif’s lab and available on GitHub. <a href="https://github.com/LREN-CHUV/MLM">https://github.com/LREN-CHUV/MLM</a>. </strong></p> <p><strong>A total of 44 patients with a current psychotic (n=19) or depressive (n=25) episode were analyzed by MLM (details can be found in the article itself) utilizing resting sate fMRI, tasks fMRI, and anatomical T1w images. </strong></p> <p><strong>The data results are provided in MATLAB format, in a matrix called MLM.mat. The matrix includes the canonical variables for each of the three imaging modalities, chi-square statistics, and p-values for each brain region. Additionally, we provided a function MLM_plot_fun that allows mapping the statistics results to a canonical three-dimensional brain as per the article. </strong></p>
Accompanying simulated data for "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model 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: a Monte Carlo study of a multilevel hidden Markov model 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>
Datasets for "Comparison of multivariate ANOVA-based approaches for the determination of relevant variables in experimentally designed metabolomic studies"
<p><em><strong>Raw files of the LC-MS designed experiments.</strong></em></p> <p> </p> <p>* <strong>YEAST GROWTH DATASET</strong></p> <blockquote> <p><strong>A) Phospholipids extraction</strong></p> <p>- FosfoB1.raw</p> <p>- FosfoB3.raw</p> <p>- FosfoC2.raw</p> <p>- FosfoD1.raw</p> <p>- FosfoE1.raw</p> <p>- FosfoE2.raw</p> <p>- FosfoE3.raw</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Sphingolipids extraction</strong></p> <p>- EsfingoB1.raw</p> <p>- EsfingoB2.raw</p> <p>- EsfingoC1.raw</p> <p>- EsfingoC2.raw</p> <p>- EsfingoC3.raw</p> <p>- EsfingoD1.raw</p> <p>- EsfingoD2.raw</p> <p>- EsfingoD3.raw</p> </blockquote> <p> </p> <p><strong>* ZEBRAFISH DATASET</strong></p> <blockquote> <p><strong>A) BPA study</strong></p> <p>Low BPA exposure</p> <p>- Low_BPA_A.d</p> <p>- Low_BPA_B.d</p> <p>- Low_BPA_C.d</p> <p>High_BPA_exposure</p> <p>- High_BPA_A.d</p> <p>- High_BPA_B.d</p> <p>- High_BPA_C.d</p> <p>Controls</p> <p>- Control_BPA_A.d</p> <p>-Control_BPA_B.d</p> <p>- Control_BPA_C.d</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Estradiol study</strong></p> <p>Low E2 exposure</p> <p>- Low_E2_A.d</p> <p>- Low_E2_B.d</p> <p>- Low_E2_C.d</p> <p>High_E2_exposure</p> <p>- High_E2_A.d</p> <p>- High_E2_B.d</p> <p>- High_E2_C.d</p> <p>Controls</p> <p>- Control_E2_A.d</p> <p>-Control_E2_B.d</p> <p>- Control_E2_C.d</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). The 1290 LC system and 6545 XT QTOF instrumentation were provided to DS as gifts by Agilent Technologies through their Thought Leader program.</p>
Fig. 4 in Morphotype And Multivariate Analysis Of The Occlusal Pattern Of The First Lower Molar In European And Asian Arvicoline Species (Rodentia, Microtus, Alexandromys)
Fig. 4. Differentiation on 16 Microtus samples by the morphotypic variation of the occlusal pattern.
ScienceDex guides
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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.