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493 results for “Multivariate”
Figure 2 in Multivariate analysis of neognath skeletal measurements: implications for body mass estimation in Mesozoic birds
Figure 2. Illustration of osteological limb measurements used in this study and defined in Table 1: A, cranial aspect of the humerus; B, dorsal aspect of the ulna and the radius; C, dorsal aspect of the carpometacarpus; D, caudal aspect of the femur; E, caudal aspect of the tibiotarsus; F, cranial aspect of the tarsometarsus.
Multivariate Ordinary Least Squares (OLS) regression-based Seismic Hazard Model Data
<p>This dataset includes earthquake parameters, slab geometry, gravity anomalies, and fault proximities used for seismic hazard modeling in the Makran Subduction Zone (MSZ). Supplementary Table S1 contains earthquake data (location, depth, magnitude), slab properties (depth, dip, thickness, strike), and distances to key faults. Supplementary Table S2 provides intraslab seismicity, slab geometry, trench distances, and gravity data. The data are sourced from the USGS Earthquake Catalog, IRIS, Slab-2 model, GMRT, and other geophysical models.</p>
Multivariate sodalite zeolitic imidazolate frameworks: a direct solvent-free synthesis
<p>Relevant data for publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D1SC04779E">10.1039/D1SC04779E</a></p>
Promoting photoswitching in mismatching mixed-linker multivariate Zr6 MOFs
<p>This is the data that accompanies the article ''Promoting photoswitching in mismatching mixed-linker multivariate Zr6 MOFs''. <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4RA07366E">https://doi.org/10.1039/D4RA07366E</a></p> <p>Information of data collection and treatment can be found in the supporting information of the article. </p>
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity
<p>Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. </p>
Raw data and R code for the stone artifact analysis in "Lithic miniaturization in South China since the terminal Pleistocene: a multivariate analysis of lithic reduction from Fodongdi, Fulin and Xiqiaoshan"
Open the record for dataset details and reuse information.
North Atlantic Ocean Drifter Dataset for Multivariate Probabilistic Regression with Natural Gradient Boosting
<p>Dataset created for Multivariate Probabilistic Regression with Natural Gradient Boosting application section. Supplied for replication. Variables in the dataset:</p> <ul> <li>Tx, Ty, Wx, Wy: Wind Stress (Pa) (T), Wind Speed (m/s) (W) in the longitudinal (x) and latitudinal (y) directions.</li> <li>u_av, v_av: surface geostrophic sea water velocity (m/s) longitudinal and latitudinal respectively.</li> <li>lon,lat: The longitude (-180,180) and latitude (-90,90) of the drifters.</li> <li>t: day of year (0,366).</li> <li>u, v: The velocities of the drifters (m/s).</li> </ul> <p>Note u, v, Tx, Ty, Wx, Wy are all filtered as described in the papers supplemental information.</p> <p>If used the following must be acknowledged or cited appropriately. You must follows the terms of the use of the following sources:</p> <p>The Global Drifter Program</p> <p>https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php</p> <p>(lon, lat, u, v)</p> <p>CMEMS datasets which are interpolated to the drifter locations:</p> <p>1. GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE) https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057/INFORMATION<br> (u_av, v_av)</p> <p>2. Global Ocean Wind L4 Reprocessed 6 hourly Observations https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L4_REP_OBSERVATIONS_012_006/INFORMATION (Wx, Wy, Tx, Ty)<br> <br> <br> <strong>Note</strong> this dataset will not be regularly updated. Any changes in the source products will not be brought into this dataset.</p>
A Multivariate Study Of Calyptrogyne (Palmae)
<p>ABSTRACT. Multivariate analyses of qualitative and quantitative characters taken from herbarium specimens of Calyptrogyne H. Wendl. (Palmae), together with analysis of geographic distributions, reveals 27 groups of specimens. Application of the Phylogenetic Species/Subspecies Concept to these groups gives 18 species and 13 subspecies, giving a total of 27 taxa in the genus. Eight species (C. tutensis, C. fortunensis, C. sanblasensis, C. baudensis, C. deneversii, C. coloradensis, C. panamensis, C. osensis) and seven subspecies (C. costatifrons subsp. occidentalis, C. costatifrons subsp. dariensis, C. allenii subsp. centralis, C. panamensis subsp. centralis, C. panamensis subsp. tutensis, C. panamensis subsp. occidentalis, C. ghiesbreghtiana subsp. hondurensis) are new, and two new combinations are made (C. ghiesbreghtiana subsp. spicigera, C. ghiesbreghtiana subsp. glauca). These taxa are distributed from southern Mexico to northwestern Colombia, with the greatest diversity in western Panama.<br> Calyptrogyne consists of small understory</p>
Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"
<p><strong>Files description</strong></p> <p>Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth.</p> <p>File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx</p> <p>Files have been organized as follows:</p> <p><strong>Lipidomics</strong></p> <blockquote> <p>1) Control samples: lip_controls.rar<br> 2) Watering low As exposure: lip_water_1.rar<br> 3) Watering high As exposure: lip_water_1000.rar<br> 4) Soil low As exposure: lip_soil_5.rar<br> 5) Soil high As exposure: lip_soil_50.rar<br> 6) QC samples: lip_qcs.rar</p> </blockquote> <p><strong>Metabolomics (positive ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_pos_controls.rar<br> 2) Watering low As exposure: met_pos_water_1.rar<br> 3) Watering high As exposure: met_pos_water_1000.rar<br> 4) Soil low As exposure: met_pos_soil_5.rar<br> 5) Soil high As exposure: met_pos_soil_50.rar<br> 6) QC samples: met_pos_qcs.rar</p> </blockquote> <p><strong>Metabolomics (negative ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_neg_controls.rar<br> 2) Watering low As exposure: met_neg_water_1.rar<br> 3) Watering high As exposure: met_neg_water_1000.rar<br> 4) Soil low As exposure: met_neg_soil_5.rar<br> 5) Soil high As exposure: met_neg_soil_50.rar<br> 6) QC samples: met_neg_qcs.rar<br> </p> </blockquote> <p> </p> <p><strong>Experimental details</strong></p> <blockquote> <p><strong>Arsenic Exposure</strong></p> <p>Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure.</p> <p>For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies.</p> <p>For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L<sup>-1</sup>). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L<sup>-1</sup> as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L<sup>-1</sup>, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels.</p> <p><strong>Lipidomic Analysis</strong></p> <p>The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da.</p> <p>The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min<sup>-1</sup>. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H<sub>2</sub>O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20.</p> <p><strong>Metabolomic analysis</strong></p> <p>The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from <em>m/z</em> 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV.</p> <p>The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min<sup>-1</sup>, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B.</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). </p> <p> </p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
FIGURE 3. Multivariate analysis. A in Delimiting species within the Lysmata vittata (Stimpson, 1860) (Decapoda: Lysmatidae) species complex in a world full of invaders
FIGURE 3. Multivariate analysis. A, resultant biplot of Principal Component Analysis computed between Brazil (red) and USA (green) populations representing Lysmata vittata species complex based on 11 morphological characters showing the two first principal components, which represent 44.08% of total variation. B, resultant histogram of Discriminant Function Analysis values computed between Brazil (red) and USA (green) populations representing Lysmata vittata species complex based on 10 morphological characters.
Supplemental material for "Statins, type 2 diabetes and body mass index: a univariable and multivariable Mendelian randomization study"
<p>Supplemental material for "Statins, type 2 diabetes and body mass index: a univariable and multivariable Mendelian randomization study" by Guoyi Yang and C Mary Schooling.</p>
Multivariate Circles
<p>Images with Multivariate Normal circle-like shapes.</p> <p>Folder is structured in the Coco format.</p>
FrenchPiezo: the French mainland groundwater level multivariate time series
<p><strong>THIS REPOSITORY IS INCOMPLETE, THE CORRECT DATASET IS AT: <a href="https://zenodo.org/record/7193812#.Y3-ThhTMLic">FrenchPiezo| 7193812#.Y3-ThhTMLic</a> </strong></p> <p>This dataset is a multivariate time series of groundwater level (a.k.a piezometric level) measured by sensors in many cities in the French mainland from January 2015 to January 2021 (2,221 days). The dataset contains 1026 multivariate time series composed of three dimensions sampled daily:</p> <ul> <li><strong>p: </strong>groundwater level</li> <li><strong>tp: </strong>precipitation</li> <li><strong>e: </strong>evapotranspiration</li> </ul> <p>Each time series is identified by a <strong>bss code </strong> which is the identifier of the piezometer using to collect the associated groundwater level.</p> <p>The groundwater level is collected from <a href="https://hubeau.eaufrance.fr/">Hub'Eau</a>, the french service for accessing water data. The precipitation and evapotranspiration are collected from the Copernicus ERA5 climate database. </p> <p>The file <em>dataset_2015_2021.csv</em> contains the raw dataset with missing values</p> <p>The file <em>dataset_nomissing_linear.csv</em> is similar to the previous one, but the missing data have been imputed using linear interpolation.</p> <p> </p> <p>More details about this dataset with source code for collecting it can be found in our paper: </p> <p>```</p> <p>Mbouopda, Michael Franklin, et al. "Experimental study of time series forecasting methods for groundwater level prediction." <em>ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data</em>. 2022.</p> <p>```</p>
Supplementary material 1 from: Christophoryová J, Krajčovičová K, Henderickx H, Španiel S (2016) A multivariate study of differentiating characters between three European species of the genus Lasiochernes Beier, 1932 (Pseudoscorpiones, Chernetidae). ZooKeys 629: 51-81. https://doi.org/10.3897/zookeys.629.8445
Results of eight canonical discriminant analyses : Explanation note: Results of eight canonical discriminant analyses (CDA 1–CDA 8, fig. 8) based on morphological characters measured/scored on 19 specimens and eight body segments of Lasiochernes cretonatus, Lasiochernes jonicus, and Lasiochernes pilosus. Values of the total canonical structure listed in the table express correlations of characters with canonical axes (Can 1 and Can 2) in each CDA. Higher total canonical structure values are in bold type.
Supplementary material 1 from: Rakotonirina JC, Csősz S, Fisher BL (2016) Revision of the Malagasy Camponotus edmondi species group (Hymenoptera, Formicidae, Formicinae): integrating qualitative morphology and multivariate morphometric analysis. ZooKeys 572: 81-154. https://doi.org/10.3897/zookeys.572.7177
Measurements of individual specimens : Explanation note: Basic measurements of individual specimens arranged by species code, collection code, and specimen code (unique identification number). See text for abbreviations.
Supplementary material 3 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Documentation on phylogeny.: Explanation note: This is a list of references including the most relevant sources of information used to build the hypothesis on phylogenetic relationships which were not quoted in the main text.
Supplementary material 2 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Frequency distribution graph.: Explanation note: Frequency distribution of the dry body weight data (mg) across the species studied.
Supplementary material 5 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Mean by superfamily.: Explanation note: Mean dry body weight and wing length by superfamily, and sample sizes.
Supplementary material 4 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Tree topology.: Explanation note: Graphic display (dendrogram) to show the hypothesis on phylogenetic relations adopted in this work, after the sources quoted in the main texta and in the file: Supplementary material 3.
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.