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493 results for “Multivariate”
Multivariate Flash Drought Intensity Index Datasets
<p><span>This repository contains the Flash Drought Intensity Index (FDII) datasets associated with the manuscript entitled “</span><span>Multivariate Evaluation of Flash Drought Across the United States”. The FDII explicitly accounts for the two key components of a flash drought – the rapid rate of intensification and the subsequent drought severity.</span></p>
Supplementary Material v1: Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer
<p>Supplementary Material PDF</p> <p>Multivariate entropy characterizes the gene expression and protein -protein networks in four types of cancer</p>
Time-generalized multivariate analysis of EEG responses reveals a cascading architecture of semantic mismatch processing
<p>This entry includes the required data to conduct the analysis from our paper. It includes recordings, a look-up csv for target word onsets and montage file. Github page for analysis code: https://github.com/heikele/GAT_n4-p6</p> <p> </p> <p>Abstract from submitted paper:</p> <p>Event-related brain potentials have a strong impact on neurocognitive models, as they inform about the temporal sequence of cognitive processes. Nevertheless, their value for deciding among alternative cognitive architectures is partly limited by component overlap and the possibility of ambiguity regarding component identity. Here, we apply temporally-generalized multivariate pattern analysis – a recently-proposed machine learning method capable of tracking the evolution of neurocognitive processes over time – to constrain possible alternative architectures underlying the processing of semantic incongruency in sentences. In a spoken sentence paradigm, we replicate established N400/P600 correlates of semantic mismatch. Time-generalized decoding indicatesthat early vs. late mismatch-sensitive processes are (i) distinct in their neural substrate, arguing against recurrent or latency-shifted single process architectures, and (ii) partially overlapping in time, inconsistent withpredictions of strictly serial models. These results are in accordance withan incremental-cascading neurocognitive organization of semantic mismatch processing. We propose time-generalized multivariate decoding as a valuable tool for neurocognitive language studies.</p> <p> </p> <p>Keywords: EEG; ERP; semantic mismatch; N400; P600; multivariate pattern analysis; generalization across time decoding</p>
Multivariate mixed model application to mass cytometry data (processed data)
<p>This bachelor thesis demonstrates the results of mass cytometry data re-analysis using multivariate regression. I reanalyse a dataset by Palgen et al. (2019) using two models: a Poisson log-normal mixed model and a logistic linear mixed model from the R package ‘cytoeffect’ (Seiler et al., 2019). By exposing multivariate patterns and the associated uncertainty profiles in the data, the aim of this analysis is to replicate biological conclusions and uncover new biological findings. </p>
ESA 4DMED-SEA - Mediterranean Multivariate Optimal Interpolated Salinity and Density fields
<p>Daily Mediterranean gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/24° of resolution from 2016 to 2022, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite UHR SST information. </p>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
A multivariate approach reveals diversity of ontogenetic niche shifts across taxonomic and functional groups
<p class="MsoBodyText">Shifts in the fundamental and realize niche of individuals during their ontogeny are ubiquitous in nature, but we know little about what aspects of the niche change and how these changes vary across species within communities. Yet, this knowledge is essential to predict the dynamics of populations and communities and how they respond to environmental change. Here I introduce a range of metrics to describe different aspects of shifts in the realized trophic niche of individuals based on stable isotopes. Applying this multi-variate approach to 2,272 individuals from 13 taxonomic and functional distinct species (Amphibia, Hemiptera, Coleoptera, Odonata) sampled in natural pond communities allowed me to: (1) describe and quantify the diversity of trophic niche shift patterns over ontogeny in multi-dimensional space, and (2) identify what aspects of ontogenetic shifts vary across taxa, and functional groups. Results revealed that species can differ substantially in which aspects of the trophic niche change and how they change over ontogeny. Interestingly, patterns of ontogenetic niche shifts grouped in distinct taxonomic clusters in multi-variate space, including two distinct groups of predators (Hemiptera vs. Odonata). Given the differences in traits (especially feeding mode) across groups, this suggests that differences in ontogenetic niche shifts across species could at least partially be explained by variation in traits and functional roles of species. These results emphasize the importance of a multivariate approach to capture the large diversity of trophic niche shifts patterns possible in natural communities, and suggest that differences in ontogenetic niche shifts follow general patterns. Shifts in the fundamental and realize niche of individuals during their ontogeny are ubiquitous in nature, but we know little about what aspects of the niche change and how these changes vary across species within communities. Yet, this knowledge is essential to predict the dynamics of populations and communities and how they respond to environmental change.</p>
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
Multivariate projected ensemble of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Multivariate statistically downscaled projected ensemble for different combinations of GCM-RCMs of both stochastic and deterministic runs for eight historical runs, eight RCP85 runs and one RCP26 run. Selection of good perfoming GCM-RCM combinations in NetCDF format. Variables: precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature.</p>
Multivariate mapping of ontogeny, taphonomy, and phylogeny to reconstruct problematic fossil taxa
<p>Exceptionally preserved fossils of soft-bodied organisms provide unique evidence of evolutionary history, but they are often contentious; different approaches frequently produce radically different reconstructions of taxa and their affinities. Conflict arises due to difficulties in disentangling the three non-independent factors that underlie all morphological variation within and between fossils: ontogeny, taphonomy, and phylogeny. Comparative data from extant organisms can be extremely powerful in this context, but is often difficult to apply given the multidimensionality of anatomical variation. Here we present a multivariate ordination method using discrete morphological character data from modern taxa at different ontogenetic and taphonomic stages (semaphoront and 'semataphonts'). Analysing multiple axes of morphological variation simultaneously allows us to visualise character combinations that are likely to exist in fossil specimens at intersecting stages of growth and decay, and thus constrain interpretation of fossils. Application to early vertebrates finds variation in fossil specimens to be accounted for by all three axes: primarily decay in <em>Mayomyzon</em>, ontogeny in <em>Priscomyzon</em>, and phylogeny in 'euphaneropoids' and <em>Palaeospondylus</em>. Our demonstration of empirical multifactorial variation underscores the need for multivariate approaches to fossil interpretation, especially non-biomineralized taxa. As such, this conceptual approach provides a new method for resolving enigmatic taxa throughout the fossil record.</p>
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
Multivariable Assessment of Coronary Artery Disease Using Cardiac CT Imaging
ClinicalTrials.gov study NCT00857792. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements
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Data from: A multivariate study of differentiating characters between three European species of the genus Lasiochernes Beier, 1932 (Pseudoscorpiones, Chernetidae)
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Data from: Fossil abalone of Europe and their relationships with modern Haliotis (Haliotidae, Gastropoda): A multivariate analysis
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Data from: Punctuated versus gradual shifts in the multivariate evolutionary process: a test with paired radiations of scincid lizards
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Data from: Wheat genotypic and phenotypic data for multivariate genomic prediction
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Data from: Axes of multivariate sexual signal divergence among incipient species: concordance with selection, genetic variation, and phenotypic plasticity
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Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes
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Interspecific allometry for sexual shape dimorphism: macroevolution of multivariate sexual phenotypes with application to Rensch's rule
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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.