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486 results for “Hierarchical”
Data from: Regression commonality analyses on hierarchical genetic distances.
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Data from: A spatially explicit hierarchical model to characterize population viability
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Data from: Estimating abundance of the federally endangered Mitchell’s satyr butterfly using hierarchical distance sampling
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Data from: A chorus of color: hierarchical and graded information content of rapid color change signals in chameleons
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Dataset and codes for "BaHSYM: parsimonious Bayesian Hierarchical Model to predict river Sediment Yield"
<p>This folder contains:</p> <ul> <li>R project file</li> <li>R code for Best Fit model</li> <li>R code for temporal cross-validation</li> <li>R code for spatial cross-validation</li> <li>R code for cluster analysis</li> <li>dataset containing all input variables for the river gauges (and catchments) used for the development and testing of the BaHSYM model in Austria</li> </ul> <p>It also contains the same codes and datasets adapted to reproduce the model by de Vente et al. (2011), i.e. with the same structure but with the variables used in such model.</p>
Supplemental Material for Paper 'Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering'
<p>Contains input data and gold standard for the non-expressive extraction task, as well as examples of extracted taxonomies for both the non-expressive and expressive cases. Extracted taxonomies can also be found at <a href="http://labowest.ca/sdb2020/">labowest.ca</a>.</p>
Nitrogen‐Doped Carbons with Hierarchical Porosity via Chemical Blowing Towards Long‐Lived Metal‐Free Catalysts for Acetylene Hydrochlorination
<p>Porous nitrogen-doped carbons (NCs) are sustainable alternatives to the toxic mercury-based acetylene hydrochlorination catalysts applied in the manufacture of polyvinyl chloride. However, the application of NCs as metal-free catalysts is hampered by their insufficient durability under industrially relevant process conditions. In particular, pore blockage leads to accelerated deactivation of NCs compared to the state-of-the-art precious metal-based systems. Herein, we develop a salt template-assisted synthesis strategy coupled with chemical blowing to tune the textural properties of NCs, while preserving the N-content and speciation. The addition of metal salts (i.e., Mg(OAc)2 or CaCO3) enhances gas evolution, leading to an increased formation of micro- and mesopores, while the in-situ generated CaO/CaCl2 and MgO/MgCl2 develop auxiliary pore networks. Micropores are easily blocked during acetylene hydrochlorination, but meso- and macropores are structurally stable, enhancing the lifetime of hierarchical NCs by ca. 50 times compared to their non-templated analogues, rivalling the stability of benchmark metal-based catalysts.</p>
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
<p>The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (<a href="https://github.com/swagnercarena/ovejero">https://github.com/swagnercarena/ovejero</a>).</p>
Hierarchical and Iterative Learning Control framework
<p>A diagram depicting a framework for Hierarchical and Iterative Learning Control of distributed HVAC systems.</p>
Data from: Metacommunity structure of stream insects across three hierarchical spatial scales
<p>A major challenge in community ecology is to understand the underlying factors driving metacommunity (i.e. a set of local communities connected through species dispersal) dynamics. However, little is known about the effects of varying spatial scale on the relative importance of environmental and spatial (i.e. dispersal related) factors in shaping metacommunities and on the relevance of different dispersal pathways. Using a hierarchy of insect metacommunities at three spatial scales (a small, within-stream scale, intermediate, among-stream scale, and large, among-sub-basin scale), we assessed whether the relative importance of environmental and spatial factors shaping metacommunity structure varies predictably across spatial scales, and tested how the importance of different dispersal routes vary across spatial scales. We also studied if different dispersal ability groups differ in the balance between environmental and spatial control. Variation partitioning showed that environmental factors relative to spatial factors were more important for community composition at the within-stream scale. In contrast, spatial factors (i.e. eigenvectors from Moran's eigenvector maps) relative to environmental factors were more important at the among-sub-basin scale. These results indicate that environmental filtering is likely to be more important at the smallest scale with highest connectivity, while dispersal limitation seems to be more important at the largest scale with lowest connectivity. Community variation at the among-stream and among-sub-basin scales were strongly explained by geographical and topographical distances, indicating that overland pathways might be the main dispersal route at the larger scales among more isolated sites. The relative effect of environmental and spatial factors on insect communities varied between low and high dispersal ability groups; this variation was inconsistent among three hierarchical scales. In sum, our study indicates that spatial scale, connectivity and dispersal ability jointly shape stream metacommunities.</p>
Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
Distance sampling is a common survey method in wildlife studies, because it allows accounting for imperfect detection. The framework has been extended to hierarchical distance sampling (HDS), which accommodates the modelling of abundance as a function of covariates, but rare and elusive species may not yield enough observations to fit such a model. We integrate HDS into a community modelling framework that accommodates multi-species spatially replicated distance sampling data. The model allows species-specific parameters, but these come from a common underlying distribution. This form of information sharing enables estimation of parameters for species with sparse data sets that would otherwise be discarded from analysis. We evaluate the performance of the model under varying community sizes with different species-specific abundances through a simulation study. We further fit the model to a seabird data set obtained from shipboard distance sampling surveys off the East Coast of the USA. Comparing communities comprised of 5, 15 or 30 species, bias of all community-level parameters and some species-level parameters decreased with increasing community size, while precision increased. Most species-level parameters were less biased for more abundant species. For larger communities, the community model increased precision in abundance estimates of rarely observed species when compared to single-species models. For the seabird application, we found a strong negative association of community and species abundance with distance to shore. Water temperature and prey density had weak effects on seabird abundance. Patterns in overall abundance were consistent with known seabird ecology. The community distance sampling model can be expanded to account for imperfect availability, imperfect species identification or other missing individual covariates. The model allowed us to make inference about ecology of species communities, including rarely observed species, which is particularly important in conservation and management. The approach holds great potential to improve inference on species communities that can be surveyed with distance sampling.
Data from: Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild
The growing interest for studying questions in the wild requires acknowledging that eco-evolutionary processes are complex, hierarchically structured and often partially observed or with measurement error. These issues have long been ignored in evolutionary biology, which might have led to flawed inference when addressing evolutionary questions. Hierarchical modelling (HM) has been proposed as a generic statistical framework to deal with complexity in ecological data and account for uncertainty. However, to date, HM has seldom been used to investigate evolutionary mechanisms possibly underlying observed patterns. Here, we contend the HM approach offers a relevant approach for the study of eco-evolutionary processes in the wild by confronting formal theories to empirical data through proper statistical inference. Studying eco-evolutionary processes requires considering the complete and often complex life histories of organisms. We show how this can be achieved by combining sequentially all life histories components and all available sources of information through HM. We demonstrate how eco-evolutionary processes may be poorly inferred or even missed without using the full potential of HM. As a case study, we use the Atlantic salmon and data on wild marked juveniles. We assess a reaction norm for migration and two potential trade-offs for survival. Overall, HM has a great potential to address evolutionary questions and investigate important processes that could not previously be assessed in laboratory or short time-scale studies.
Figure 6 in Functional morphology of neck musculature in the Tyrannosauridae (Dinosauria, Theropoda) as determined via a hierarchical inferential approach
Figure 6. Neck muscle origins and insertions in crocodilians. A, muscle attachments on C1–C9 of Caiman crocodylus. B, muscle insertions on the occiput of Caiman crocodylus. m. trans. cap., m. transversospinalis capitis; m. epi.-cap. lat., m. epstropheo-capitis lateralis; m. epi.-cap. med., m. epstropheo-capitis medialis; m. sp.cap. post., m. spinocapitis posticus; m. trans. cerv., m. transversospinalis cervicis; m. long. cerv., m. longissimus cervicis (m. articulares/m. tendinoarticulares); m. long. cap., m. longus capitis; m. long. cap. sup., m. longissimus capitis superficialis; m. long. cap. prof., m. longissimus capitis profundus; m. il. cap., m. iliocostalis capitis; m. il. cerv., m. iliocostalis cervicis.
Radial velocities for compact hierarchical triples: BD+44 2258 and KIC 06525196
<p>Radial velocity from the CREME project used in the work "Detached eclipsing binaries in compact hierarchical triples: triple-lined systems BD+442258 and KIC 06525196".</p>
An Interpretable 3D Multi-hierarchical Representation-based Deep Neural Network for EH&S Properties Prediction
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An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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MAdVerse: A Hierarchical Dataset of Multi-Lingual Ads from Diverse Sources and Categories
<p>MAdVerse, an extensive, multilingual compilation of more than 50,000 ads from the web,<br>social media websites, and e-newspapers. Advertisements are hierarchically grouped with uniform granularity into 11 categories, divided into 51 sub-categories, and 524 finegrained brands at leaf level, each featuring ads in various languages.</p>
A generic framework for hierarchical de novo protein design
<p><strong>A small MASTER database</strong> that (most of the time) will be enough for most of the design tasks. The data includes the PDB files <em>master_pdb</em>, PDS files <em>maps</em>, structure fragments <em>frags</em>, ABEG0 torsions <em>master_abego.fa.gz</em>, and secondary structure <em>master_sse.fa.gz</em>.</p>
Dataset for "An approach for the pore-centred description of adsorption in hierarchical porous materials"
<p>Supporting dataset for: “An approach for the pore-centred description of adsorption in hierarchical porous materials”, J. D. Evans, <strong>2022</strong></p> <p><strong>Contains:</strong></p> <ul> <li>structures: Structural models used in YAFF .chk format.</li> <li>code: All the code used to generate the pore distributions, adsorption simulations and analysis.</li> <li>geometricporosity: Output from zeo++ used in this analysis.</li> <li>porestructure: Pore size distribution probes labeled by Pr, Nd, Pm.</li> <li>clustering: Metadata produced from the clustering analysis.</li> <li>porecenters: Locations of the pore centres combined with the </li> <li>structure in CIF format, where each pore is labeled by Pr, Nd, Pm.</li> <li>adsorption: Results of Ar simulation at 87K.</li> </ul>
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.