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165 results for “multilevel”
In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors
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Data from: Environmental variability and acoustic signals: A multilevel approach in songbirds
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Soil microfauna mediate multifunctionality under multilevel warming in a primary forest
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ECG and EEG stress features for: ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study
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Data from: Multilevel and sex-specific selection on competitive traits in North American red squirrels.
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Multilevel Text Alignment with Cross-Document Attention
<p>This benchmark belongs to our EMNLP2020 paper: <a href="https://arxiv.org/abs/2010.01263">Multilevel Text Alignment with Cross-Document Attention</a>.</p>
Multilevel modeling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development
<p>What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.</p>
Data from: A multilevel society of herring-eating killer whales indicates adaptation to prey characteristics
Non-social factors can influence animal social structure. In killer whales (Orcinus orca), fish- versus mammal-eating ecological differences are regarded as key ecological drivers of their multilevel society, including group size, but the potential importance of specific target prey remains unclear. Here, we investigate the social structure of herring-eating killer whales in Iceland and compare it to the described social structures of primarily salmon- and seal-eating populations in the Northeast Pacific, which form stable coherent basic units nested within a hierarchical multilevel society. Using 29023 photographs collected over 6 years, we examined the association patterns of 198 individuals combining clustering, social network structure, and temporal patterns of association analysis. The Icelandic population had largely weak but non-random associations, which were not completely assorted by known ranging patterns. A fission–fusion dynamic of constant and temporary associations was observed but this was not due to permanent units joining. The population-level society was significantly structured but not in a clear hierarchical tier system. Social clusters were highly diverse in complexity and there were indications of subsclusters. There was no indication of dispersal nor strong sex differences in associations. These results indicate that the Icelandic herring-eating killer whale population has a multilevel social structure without clear hierarchical tiers or nested coherent social units, different from other populations of killer whales. We suggest that local ecological context, such as the characteristics of the specific target prey (e.g., predictability, biomass, and density) and subsequent foraging strategies may strongly influence killer whale social association patterns.
Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: </p> <p>Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms</p> <p><br> Journal: Annals of Nuclear Energy</p>
Accompanying simulated data for "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
Multilevel Interventions for Mental Health in SMEs and Public Workplaces - H-Work EU Project Dataset (Only for Partners)
<p>This Dataset includes data collected as part of the H-Work project, at timepoints T1-T7. It contains data from each timepoint per site in single files and merged Data from multiple sites and timepoints, for more information see info file.</p>
Female countertactics to male feticide and infanticide in a multilevel primate society
<p class="MsoNormal"><span class="None"><span>We observed 19 male replacement events involving 17 OMUs and 16 adult males. The dataset (presented as a table) includes detailed information of social circumstances during male takeover events occurred, such as new male name, name of dependent offspring in the social unit, individual number in that social unit, Residency of the mother, and fate of fetal or unweaned infant. Furthermore, the dataset also included other information which would be useful for evaluate female counter-strategies against infanticide. For example, when male takeover events occur if the females in the social unit were pregnant; if new male attacked the pregnant female or unweaned infant after male takeover. These data might be important for the readers who hope to know the context of the male takeover except the interest were mentioned in the manuscript.</span></span></p>
Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"
<p>Source data and codes for the paper "Inviting atomic mechanics to macro-continua: A study on monocrystalline Si using a spatial multilevel coarsening model"</p> <p>This file includes </p> <p>- Source data for Figs 1-5 and Supplementary Materials</p> <p>- LAMMPS codes and raw log files used to produce the results of this study</p> <p> </p>
St. Jude Lifetime Study Cohort dataset for multilevel characteristics of cumulative symptom burden in young survivors of childhood cancer
<p>The dataset contains variables from the St. Jude Lifetime Cohort Study used in the study of multilevel characteristics of cumulative symptom burden in young survivors of childhood cancer in Horan et al., 2024 (DOI:<a href="https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2818384" target="_blank" rel="noopener">10.1001/jamanetworkopen.2024.10145</a>). </p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li>Horan MR, Srivastava DK, Choi J, et al. Multilevel characteristics of cumulative symptom burden in young survivors of childhood cancer. <em>JAMA Netw Open.</em> 2024;7(5):e2410145. doi:10.1001/jamanetworkopen.2024.10145</li> <li>Horan MR, Srivastava DK, Choi J, et al. (2018). St. Jude Lifetime Study Cohort Dataset for multilevel characteristics of cumulative symptom burden in young survivors of childhood cancer (Version 1) [Data set]. 2024. Zenodo. http://doi.org/10.5281/zenodo.11474180</li> </ol> <p><strong>Funding:</strong></p> <p>The research reported in the manuscript was supported by the US National Cancer Institute under award numbers U01CA195547, R01CA238368, T32CA225590, and P30CA021765. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.</p>
High Definition Modular Multilevel Converter (1st Call for Joint Experiment Final Report and data set)
<p>This project aims to validate experimentally the High definition Modular Multilevel Converter (HD-MMC) which was developed by ORE Catapult. The concept can generate a lower THD than Conventional MMC (C-MMC) helping to increase power density and efficiency. You will find data set and final report here</p>
Data to "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"
<p>The zip files contains the tex file, figure, matlab files, and raw experimental and simulation data of the paper "Symmetry breaking and non-ergodicity in a driven-dissipative ensemble of multilevel atoms in a cavity"</p>
Multilevel Modeling of Training Needs in Artificial Intelligence
<p>Nowadays, Artificial Intelligence (AI) is playing a rapidly increasing role in several fields of research and in almost all sectors of real life. However, few studies have assessed the effects of AI applications on training needs. This paper proposes an innovative multilevel modeling in order to investigate Awareness, Attitude and Trust towards AI and their reflections on learning needs. In particular, it is shown how a machine learning variable selection algorithm can support the definition of the optimal subset of all relevant covariates with respect to the outcome variable and improve the multilevel model performance for estimating the probability of educational needs. Thus, starting from a complex web survey to European citizens distributed in eight countries, the estimation of a multilevel binary model, defined on the basis of covariates selected through the Boruta random forest algorithm, is proposed. A discussion on the gender differences of the related estimated multilevel logit models is presented. A sensitivity analysis is also included in order to assess the prediction accuracy of the proposed multilevel logit modeling.</p> <p> </p> <p>This repository contains data generated for the manuscript: " A two-stage procedure for optimal modeling of the probability of training needs in artificial intelligence". It comprehends: (1) the dataset Data_Boruta_Random_Forest used to estimate the variables importance. (2) the dataset Data_Multilevel to perform the comparison among different multilevel binary models proposed in the paper.</p>
Geometries for 'Multilevel CC2 and CCSD in reduced orbital spaces: electronic excitations in large molecular systems'
<p>Geometries in the .xyz format for molecular systems used in '<em>Multilevel CC2 and CCSD in reduced orbital spaces: electronic excitations in large molecular systems</em>'</p>
P3 Trial: Estimating the Impact of a Multilevel, Multicomponent Intervention to Increase Uptake of HIV Testing and Biomedical HIV Prevention Among African-American/Black Gay, Bisexual, and Same-gender
ClinicalTrials.gov study NCT06785376. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Multilevel Interventions to Increase Adherence to Lung Cancer Screening
ClinicalTrials.gov study NCT05747443. IPD Sharing: NO. Countries: 1. Publications: 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.