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486
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486 results for “Hierarchical”
Supplementary Website, Data, and Scripts for the Paper "Hierarchical and Hybrid Organizational Structures in Open-Source Software Projects: A Longitudinal Study"
<p>Supplementary website containing result plots and data, anonymized raw data, and scripts used to produce the results of the paper "Hierarchical and Hybrid Organizational Structures in Open-Source Software Projects: A Longitudinal Study".</p>
Schematic representing the vehicle–to–cell hierarchical overview of a typical electrified powertrain architecture. This represents the system-level context within which the proposed layer optimisation framework has been developed. Two xEV powertrains — a) BEV and b) series PHEV — are chosen as examples to demonstrate how the methodology facilitates common module designs for such battery packs.
<p>Schematic representing the vehicle–to–cell hierarchical overview of a typical electrified powertrain architecture. This represents the<br> system-level context within which the proposed layer optimisation framework has been developed. Two xEV powertrains — a) BEV and b) series PHEV — are chosen as examples to demonstrate how the methodology facilitates common module designs for such battery packs.</p>
Probabilistic Hierarchical Interpolation and Interpretable Configuration for Flood Prediction
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HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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The code and dataset for the paper "Hierarchical Clustering for Consistent Inverse Rendering of Indoor Scenes"
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Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation - Data and Samples
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Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
Hierarchical genetic structure and implications for conservation of the world's largest salmonid, Hucho taimen
<p>Population genetic analyses can evaluate how evolutionary processes shape diversity and inform conservation and management of imperiled species. Taimen (<i>Hucho taimen</i>), the world's largest freshwater salmonid, is threatened, endangered, or extirpated across much of its range due to anthropogenic activity including overfishing and habitat degradation. We generated genetic data using high throughput sequencing of reduced representation libraries for taimen from multiple drainages in Mongolia and Russia. Nucleotide diversity estimates were within the range documented in other salmonids, suggesting moderate diversity despite widespread population declines. Similar to other recent studies, our analyses revealed pronounced differentiation among the Arctic (Selenge) and Pacific (Amur and Tugur) drainages, suggesting historical isolation among these systems. However, we found evidence for finer-scale structure within the Pacific drainages, including unexpected differentiation between tributaries and the mainstem of the Tugur River. Differentiation across the Amur and Tugur basins together with coalescent-based demographic modeling suggests the ancestors of Tugur tributary taimen likely diverged in the eastern Amur basin, prior to eventual colonization of the Tugur basin. Our results suggest the potential for differentiation of<i> </i>taimen<i> </i>at different geographic scales, and suggest more thorough geographic and genomic sampling may be needed to inform conservation and management of this iconic salmonid.</p>
MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
<p><strong>Overview</strong></p> <p>This dataset contains 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019 classified by using NewsCodes Media Topic taxonomy. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories.</p> <p>This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.</p> <p><strong>Reproducibility of results</strong></p> <p>The results presented in the research paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification", technical validation can be reproduced using functions in <a href="https://github.com/alinapetukhova/mn-ds-news-classification">github repository</a>.</p> <p><strong>Licenses</strong></p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see `LICENSE_DATA.txt`).</p>
Stroke Rehabilitation: A Systemized and Hierarchized Rehabilitation Program for the Brazilian Unified Health System
ClinicalTrials.gov study NCT06494202. IPD Sharing: YES. Countries: 1. Publications: 0.
iTBS/fMRI Study of Hierarchical Control in the PFC
ClinicalTrials.gov study NCT05878080. IPD Sharing: YES. Countries: 1. Publications: 0.
cTBS/fMRI Study of Hierarchical Control in the PFC
ClinicalTrials.gov study NCT04464473. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Complex interaction of dendritic connectivity and hierarchical patch size on biodiversity in river-like landscapes
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Data from: Land-use intensity and the effects of organic farming on biodiversity: a hierarchical meta-analysis
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Data from: Damage shielding mechanisms in hierarchical composites in nature with potentials in design of tougher structural materials
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Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
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Data from: Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild
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Data from: A hierarchical Bayesian model for calibrating estimates of species divergence times
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Data from: Metabolic scaling of individuals vs. populations: evidence for variation in scaling exponents at different hierarchical levels
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Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
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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.