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493 results for “Multivariate”
Multivariate Criminal investigation network
<p>Manipulation des fichiers csv</p> <p>Le fichier <em>nodes.csv</em> décrit les sommets du réseau multivarié. Les colonnes sont:</p> <ul> <li> `node id`: identifiant interne du sommet dans le logiciel Tulip quand l'export des données a été réalisé</li> <li> `CATEGORIE`: type du sommet</li> <li> `viewLabel`: label anonymisé du sommet</li> <li> `viewIcon`: nom de l'icône utilisé dans Tulip issu des libraires Material Design (<a href="https://materialdesignicons.com">https://materialdesignicons.com</a>/) et FontAwesome (<a href="https://fontawesome.com/">https://fontawesome.com/</a>)</li> <li> `viewShape`: identifiant interne Tulip pour les formes des sommets (ici, un icône décrit par `viewIcon`)</li> </ul> <p>Le fichier <em>edges.csv</em> décrit les arêtes du réseau multivarié entre les sommets du fichier <em>nodes.csv</em>. Les colonnes sont:</p> <ul> <li> `src id`: identifiant interne de Tulip du sommet source de l'arête (dans Tulip les arêtes sont orientées. On peut ou pas se servir de l'orientation)</li> <li> `tgt id`: identifiant interne de Tulip du sommet destination de l'arête</li> <li> `CATEGORIE`: type de l'arête</li> <li> `ponderations`: ponderation ou force liante telle que définie par l'experte en sciences criminelles</li> <li> `viewLabel`: label de l'arête apportant des détails sur le lien</li> </ul> <p><br> Pour importer ces fichiers dans Tulip:</p> <ul> <li> Ouvrir le module `csv` (bouton `csv` en bas à gauche de la fenêtre)</li> <li> Importer le fichier `nodes.csv`</li> <li> Après avoir choisi les bons paramètres de lecture du fichier, il faut cliquer sur le bouton de la propriété `viewLabel` et changer son type en `string` (bug de la version 5.6.3 de Tulip, corrigée dans la version 5.7)</li> <li> Importer de la même manière le fichier `edges.csv`</li> <li> Au moment de choisir le type de données à importer, il faut choisir `New relations`</li> <li> Modifier les boutons sous `Source properties` et `Destination properties` pour ne choisir que la propriété `node id`. Le lien entre les sommets déjà importés et les arêtes se fera sur cette propriété existante.</li> </ul>
Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
<p>These are the Supplementary Material, R scripts and numerical results accompanying Bartoszek, Fuentes Gonzalez, Mitov, Pienaar, Piwczyński, Puchałka, Spalik and Voje "Model Selection Performance in Phylogenetic Comparative Methods under multivariate Ornstein–Uhlenbeck Models of Trait Evolution".</p> <p>The four data files concern two datasets. Ungulates: measurements of muzzle width, unworn lower third molar crown height, unworn lower third molar crown width and feeding style and their phylogeny; Ferula: measurements of ratio of canals, periderm thickness, wing area, wing thickness, and fruit mass, and their phylogeny.</p>
Dataset: 1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis
<p>This dataset contains the set of 1H NMR data used in https://doi.org/10.1038/srep30982.</p> <p>Yeast was grown in five different liquid media and their metabolism was characterized at 6 different time-points during 24 h.</p> <p>The media used were YSC (Yeast nitrogen base Synthetic Complete) and four Drop-Out (DM) medium that do not contain one of the following nutrients (L-histidine, L-leucine, L-methionine and uracil). Since the used yeast strain does not encode in its genome some genes relative to the biosynthesis of these four nutrients, some gene de-regulations process will occur, detectable at the metabolome level.</p> <p>In this study, we have characterized the metabolome using <sup>1</sup>H NMR spectroscopy, detecting more than 40 metabolites, and the evolution of this metabolome along the measured time-points was described by application of PCA, ASCA and MCR-ALS chemometric methods.</p>
Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>Predictors and predictands for "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain". Training predictors from the ERA5 reanalysis, projecting predictors from CORDEX EUR11, and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rds" format, to be read from R, except for CORDEX files in NetCDF.</p>
Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses
<p>1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type.</p> <p>2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (<em>Anas</em> <em>platyrhychos</em>) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged.</p> <p>3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery.</p> <p>4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.</p>
Gap-filled Multivariate Observations of Global Land-climate Interactions
<p>The NETCDF encompasses monthly time series spans the years 1995-2020 globally at 0.5-degree resolution with gap-free estimates of nine variables, gap-filled using the CLIMFILL (CLIMate data gapFILL) framework [1,2]</p> <p>The nine variables are:</p> <p>- surface layer soil moisture from the Climate Change Initiative (CCI) of the European Space Agency (ESA),</p> <p>- land surface temperature and</p> <p>- diurnal temperature range from the Moderate Resolution Imaging Spectroradiometer (MODIS),</p> <p>- precipitation from the Global Precipitation Measurement (GPM),</p> <p>- terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE),</p> <p>- ESA-CCI burned area,</p> <p>- ESA-CCI snow cover fraction, </p> <p>- two-meter temperature and precipitation from the Climate Research Unit (CRU).</p> <p> </p> <p>Note: this dataset is only validated and tested for the use cases in the accompanying study (DOI to come). Please have caution using the data for analysis that might include trends in high latitude, in regions where the variable has high fraction of missing values, or in mountainous regions.</p> <p> </p> <p>References:</p> <p>[1] Bessenbacher, V., Seneviratne, S.I. and Gudmundsson, L. (2022): CLIMFILL v0.9: a framework for intelligently gap filling Earth observations. Geosci. Model Dev., 15, 4569–4596, 2022 <a href="https://doi.org/10.5194/gmd-15-4569-2022">https://doi.org/10.5194/gmd-15-4569-2022</a></p> <p>[2] <a href="https://github.com/climachine/climfill/releases/tag/1.0">https://github.com/climachine/climfill/releases/tag/1.0</a></p>
PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK
<p>The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.</p>
Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
<p>This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as concatenated raw results from S-PrediXcan. Files belong to the research paper "Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations" published at HGG Advances (2023, doi: 10.1016/j.xhgg.2023.100216). Please refer to the README file for a more detailed description.</p>
Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning
<p><strong>Data Description</strong>:</p> <p>To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.</p> <p>Files in collection (32):</p> <p>Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts</p> <p>gNATSGO TIF files:</p> <p>├── available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]<br> ├── available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]<br> ├── caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]<br> ├── caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]<br> ├── cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]<br> ├── cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]<br> ├── clay_30arc_30cm_us.tif [30 cm depth soil clay content]<br> ├── clay_30arc_100cm_us.tif [100 cm depth soil clay content]<br> ├── depthWT_30arc_us.tif [depth to water table]<br> ├── kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]<br> ├── kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]<br> ├── ph_30arc_100cm_us.tif [100 cm depth soil pH]<br> ├── ph_30arc_100cm_us.tif [30 cm depth soil pH]<br> ├── pondingFre_30arc_us.tif [ponding frequency]<br> ├── sand_30arc_30cm_us.tif [30 cm depth soil sand content]<br> ├── sand_30arc_100cm_us.tif [100 cm depth soil sand content]<br> ├── silt_30arc_30cm_us.tif [30 cm depth soil silt content]<br> ├── silt_30arc_100cm_us.tif [100 cm depth soil silt content]<br> ├── water_content_30arc_30cm_us.tif [30 cm depth soil water content]<br> └── water_content_30arc_100cm_us.tif [100 cm depth soil water content]</p> <p>SOC TIF files:</p> <p>├──30cm SOC mean.tif [30 cm depth soil SOC]<br> ├──100cm SOC mean.tif [100 cm depth soil SOC]<br> ├──30cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]<br> └──100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation]</p> <p>site observations csv files:</p> <p>ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p>ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p><br> <strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p> </p>
Multivariate Genome-wide association summary statistics for shared aging factor
<p>This dataset contains genome-wide summary statistics (autosomal variants) computed from a multivariate genome-wide association study of five aging-related phenotypes using Genomic Structural Equation Modeling (https://github.com/GenomicSEM/GenomicSEM). The effective sample size is calculated to be 1,958,774. Column descriptions are included in the accompanying README file. </p> <p>The summary statistics are provided on an "AS-IS" basis, without any type of warranty, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</p> <p>If investigators use these data, any and all consequences are entirely their responsibility. The user agrees that to cite the appropriate publication in any communications or publications arising directly or indirectly from these data by downloading and and using these data,. The user also agrees to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles and also agree that they will never attempt to identify any participant.</p> <p> </p>
Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 7 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 5 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 10 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 3 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 1 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 11 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 16 of 16
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Data from: Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies, Part 4 of 16
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Data from: Fast mvSLOUCH: 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 9 of 16
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ScienceDex guides
Understand access before you commit
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.