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493 results for “Multivariate”
Data from: Multivariate phenotypic selection on a complex sexual signal
Animal signals are complex, comprising multiple components that receivers may use to inform their decisions. Components may carry information of differing value to receivers, and selection on one component could modulate or reverse selection on another, necessitating a multivariate approach to estimating selection gradients. However, surprisingly few empirical studies have estimated the strength of phenotypic selection on complex signals with appropriate design and adequate power to detect non-linear selection. We used phonotaxis assays to measure sexual selection on the advertisement signal of Cope's gray tree frog, Hyla chrysoscelis. Female preferences were assessed for five signal components using single-stimulus and two-stimulus behavioral assays. Linear, quadratic, and correlational selection gradients were estimated from the single-stimulus data. Significant directional selection is acting on call duration, call rate, pulse rate, and relative amplitude; stabilizing selection is acting on call duration and call rate. Under the two-stimulus paradigm, conclusions were qualitatively different, revealing non-linear selection on all components except call duration. For individual subjects, the outcomes of single-stimulus and two-stimulus trials were frequently discordant, suggesting that the choice of testing paradigm may affect conclusions drawn from experiments.
Fig. 8 in The Os Navicular of Humans, Great Apes, OH 8, Hadar, and Oreopithecus: Function, Phylogeny, and Multivariate Analyses
Fig. 8. Total cuneiform facet crosssectional area (mm2) vs. total navicular facet crosssectional
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class III) vs NoLineal (Class III)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions (linear interactions)</strong> and <strong>high-order interactions (non-linear interactions)</strong>.</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset, whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlinearx means that the linear coupling occurs in variable <em><strong>x</strong></em> (class III) whereas the non-linear coupling occurs in variable<em><strong> x</strong></em> (class III).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the<strong><em> x</em></strong> and <strong><em>y</em></strong> variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class III) vs NoLineal (Class II)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in<strong> simplicial complexe</strong>s. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and <strong>high-order interactions</strong> (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (second), whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlineary means that the linear coupling occurs in variable <em><strong>x</strong></em> (class III) whereas the non-linear coupling occurs in variable <em><strong>y</strong></em> (class II).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the <em><strong>x</strong></em> and <em><strong>y</strong></em> variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class III)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and <strong>high-order interactions</strong> (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (third), whose name explicitly indicates in which state variable occurs each coupling. In these case, Lineary-nonlinearx means that the linear coupling occurs in <strong>variable y</strong> (class II) whereas the non-linear coupling occurs in <strong>variable x</strong> (class III).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</p>
Experimental validation of simplicial complexes in multivariable coupled oscillators. Coupling: Lineal (Class II) vs NoLineal (Class II)
<p>The data sets correspond to the experimental implementation of synchronization phenomenon in <strong>simplicial complexes</strong>. In this particular case, the simplicial complex consists of 3-node network, where each node is an electronic Rössler-like oscillator whose parameters were fixed to operate in <strong>chaotic regime</strong>. In simplicial complexes, it is possible to model two types of interactions among nodes: <strong>pair-wise interactions</strong> (linear interactions) and<strong> high-order interaction</strong>s (non-linear interactions).</p> <p>The complete experiment consists of coupling simultaneously by means of linear and non-linear interactions the simplicial complex, the coupling occurs in state variables x (class III) and y (class II). The full data sets are organized in four Dataset (fourth), whose name explicitly indicates in which state variable occurs each coupling. In these case, Linearx-nonlinearx means that the linear coupling occurs in variable <strong>y</strong> (class II) whereas the non-linear coupling occurs in variable <strong>y</strong> (class II).</p> <p>Now, each folder contains 10000 files which come from varying the linear coupling and the nonlinear coupling 100 times each one. The file name is composed as follows: rootname XX YY, where XX corresponds to the variation number in linear coupling, whereas YY corresponds to the variation number in non-linear coupling.</p> <p>Internally in each file we can find 6 columns and 30,000 rows. Each pair of columns corresponds to the x and y variables of each oscillator and the rows correspond to time-varying samples.</p>
MST NDSI Collection: A Cloud-free MODIS NDSI Dataset (2001–2022) for Tibetan Plateau Generated by a LightGBM-Based Method Using Multivariate Features
<p>1. Cloud-free MODIS normalized difference snow index (NDSI) dataset for Tibetan Plateau from 2001 to 2022 is generated.</p> <p>2. This dataset is derived from daily 500-m MODIS NDSI datasets (MOD10A1 and MYD10A1).</p> <p>3. This dataset is provided using a WGS_1984_UTM_45N projection, with the data format of TIFF images. </p> <p>4. The NDSI value ranges from -10,000-10,000, corresponding to the raw NDSI band of MOD10A1 and MYD10A1. The fill value is set to -32768.</p> <p>5. Due to the large amount of data, the data for each year has been divided into several split archives (YYYY.partX.rar).</p> <p>6. Each RAR contains NDSI data for one year (January to December). After uncompressing into the TIFF format, the files are named as "NDSI_Daily_YYYY_mm_dd.tif" for NDSI data and "NDSI_Daily_YYYY_mm_dd.tif".</p> <p>7. The accuracy of this collection has been well validated by simulation experiments.</p> <p>8. This version includes data from 2017 to 2022 (https://doi.org/10.5281/zenodo.14177009). Version 1 includes data from 2001 to 2004 (https://doi.org/10.5281/zenodo.14027672). Version 2 includes data from 2005 to 2010(https://doi.org/10.5281/zenodo.14038846). Version3 includes data from 2011 to 2016 (https://doi.org/10.5281/zenodo.14089925).<br><br><br></p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Data from: Axes of multivariate sexual signal divergence among incipient species: concordance with selection, genetic variation, and phenotypic plasticity
<p>Sexual signaling traits are often observed to diverge rapidly among populations, thereby playing a potentially key early role in the evolution of reproductive isolation. While often assumed to reflect divergent sexual selection among populations, patterns of sexual trait diversification might sometimes be biased along axes of standing additive genetic variation and covariation among trait components. Additionally, theory predicts that environmentally-induced phenotypic variation might facilitate rapid trait evolution, suggesting that patterns of divergence between populations should mirror phenotypic plasticity within populations. Here we evaluate the concordance between observed axes of multivariate sexual trait divergence and predicted divergence based on (1) interpopulation variation in sexual selection, (2) additive genetic variances, and (3) temperature-related phenotypic plasticity in male courtship song among geographically isolated populations of the Hawaiian swordtail cricket, Laupala cerasina, which exhibit sexual isolation due sexual signaling traits. The major axis of multivariate divergence, dmax, accounted for 76% of variation among population male song trait means, and was moderately correlated with interpopulation differences in directional sexual selection based on female preferences. However, the majority of additive genetic variance was largely oriented away from the direction of divergence, suggesting that standing genetic variation may not play a dominant role in the patterning of signal divergence. In contrast, the axis of phenotypic plasticity strongly mirrored patterns of interpopulation phenotypic divergence, which is consistent with a role for temperature-related plasticity in facilitating instead of inhibiting male song evolution and sexual isolation in these incipient species. We propose potential mechanisms by which sexual selection might interact with phenotypic plasticity to facilitate the rapid acoustic diversification observed in this species and clade.</p>
Data supporting "A multivariate approach to evaluate reduced tillage systems and cover crop sustainability"
<p>Data supporting "A multivariate approach to evaluate reduced tillage systems and cover crop sustainability" by Sartori et al. (2022) Land, 11, 55. https://doi.org/ 10.3390/land11010055</p>
A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures
<pre>This repository contains the data for "A Multivariate Index for Tropical intraseasonal Oscillations based on the Seasonally-varying Modal Structures". </pre>
Data from: Multivariate selection mediated by aridity predicts divergence of drought resistant traits along natural aridity gradients of an invasive weed
<p><span>Geographic variation in the environment underpins selection for local adaptation and evolutionary divergence among populations. Because many environmental conditions vary across species' ranges, identifying the specific environmental variables underlying local adaptation is profoundly challenging. </span></p> <p><span>We tested whether natural selection mediated by aridity predicts clinal divergence among invasive populations of capeweed (<i><span>Arctotheca calendula</span></i>) that established and spread across southern Australia during the last two centuries. </span></p> <p><span>Using common garden experiments with two environmental treatments (wet and dry) that mimic aridity conditions across capeweed's invasive range, we estimated clinal divergence and effects of aridity on fitness and multivariate phenotypic selection in populations sampled along aridity gradients in Australia. We show that: (<i>i</i>) capeweed populations have relatively high fitness in aridity environments similar to their sampling locations; (<i>ii</i>) the magnitude and direction of selection strongly differs between wet and dry treatments, with drought stress increasing the strength of selection; and (<i>iii</i>) differences in directional selection between wet and dry treatments predict patterns of clinal divergence across the aridity gradient, particularly for traits affecting biomass, flowering phenology and putative antioxidant expression. </span></p> <p><span>Our results suggest that aridity-mediated selection contributes to trait diversification among invasive capeweed populations, possibly facilitating the expansion of capeweed across southern Australia. </span></p>
Datasets and scripts from: Sensory-based quantification of male colour patterns in Trinidadian guppies reveals no support for parallel phenotypic evolution in multivariate trait space
<p>Parallel evolution, in which independent populations evolve along similar phenotypic trajectories, offers insights into the repeatability of adaptive evolution. Here, we revisit a classic example of parallelism, that of repeated evolution of brighter males in the Trinidadian guppy (<em>Poecilia reticulata</em>). In guppies, colonisation of low predation habitats is associated with emergence of 'more colourful' phenotypes since predator-induced viability selection for crypsis weakens while sexual selection by female preference for conspicuousness remains strong. Our study differs from previous investigations in three respects. First, we adopt a multivariate phenotyping approach to characterise parallelism in multi-trait space. Second, we use ecologically-relevant colour traits defined by the visual systems of the two selective agents (i.e. guppy, predatory cichlid). Third, we estimate population genetic structure to test for adaptive (parallel) evolution against a model of neutral phenotypic divergence. We find strong phenotypic differentiation that is inconsistent with a neutral model but very limited support for the predicted pattern of greater conspicuousness at low predation. Effects of predation regime on each trait were in the expected direction, but weak, largely non-significant, and explained little among-population variation. In multi-trait space, phenotypic trajectories of lineages colonising low from high predation regimes were not parallel. Our results are consistent with reduced predation risk facilitating adaptive differentiation, potentially by female choice, but suggest that this proceeds in independent directions of multi-trait space across lineages. Pool-sequencing data also revealed SNPs showing greater differentiation than expected under neutrality, among which some are found in genes contributing to colour pattern variation, presenting opportunities for future genetic study.</p>
Multivariate trait analysis reveals diatom plasticity constrained to a reduced set of biological axes
<p>Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a 'trait-scape'. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.</p>
autonomous-vehicle-interests-multivariate-modeling
<p>This is a release of data and analysis scripts of the "Private or on-demand autonomous vehicles? Modeling public interest using a multivariate model " research study. It contains the 2019 California Vehicle Survey data as well as the scripts followed to clean and analyze the data. A word document within the folder "Description.docx" presents the steps followed to get the outputs. All scripts are written in R.</p>
MobileWell100+: A Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being
<p>This study engaged 103 participants over a period spanning from November 14 to December 16, 2021, ensuring representation across various demographic factors: 51 females, 52 males, aged 18-70, with varied annual incomes and from 17 Spanish regions. The MobileWell100+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic details, COVID-19-related inquiries, emotional, behavioral, and well-being data. Complementing this, social welfare data from external sources offers contextual insight. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent. </p> <p>The following is a non-exhaustive list of collected data:</p> <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ).</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. is requested.</li> </ul> <p>For a more detailed description of the study please refer to <strong>MobileWell100+StudyDescription.pdf</strong>.</p> <p>For a more detailed description of the collected data, variables and data files please refer to <strong>MobileWell100+FilesDescription.pdf</strong>. </p>
MobileWell400+: A Large-Scale Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being
<p>This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent. </p> <p>The following is a non-exhaustive list of collected data:</p> <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.</li> <li>Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.</li> </ul> <p>For a more detailed description of the study please refer to <strong>MobileWell400+StudyDescription.pdf</strong>.</p> <p>For a more detailed description of the collected data, variables and data files please refer to <strong>MobileWell400+FilesDescription.pdf</strong>. </p>
Metadata - Exploring the Luminescence, Redox, and Magnetic Properties in a Multivariate Metal–Organic Radical Framework
Open the record for dataset details and reuse information.
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation
<p>Raw data supporting the article 'Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation'.</p>
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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.