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187 results for “Multivariate analysis”
Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques - dataset
<p>Dataset from Tuyishime M, Spreng RL, et al. Multivariate analysis of FcR-mediated NK cell functions identifies unique clustering among humans and rhesus macaques. Frontiers in Immunology 2023 doi: 10.3389/fimmu.2023.1260377</p>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.
<p><strong>Data availability</strong>. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence. </p><ul><li>SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).</li><li>Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).</li><li>FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).</li></ul>
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).
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>
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.
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).
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>
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.
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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.