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486 results for “Hierarchical”
Data from: Hierarchical decision-making balances current and future reproductive success
Parental decisions in animals are often context-dependent and shaped by fitness trade-offs between parents and offspring. For example, the selection of breeding habitats can considerably impact the fitness of both offspring and parents, and therefore parents should carefully weigh the costs and benefits of available options for their current and future reproductive success. Here we show that resource-use preferences are shaped by a trade-off between parental effort and offspring safety in a tadpole-transporting frog. In a large-scale in-situ experiment, we investigated decision-strategies across an entire population of poison frogs that distribute their tadpoles across multiple water bodies. Pool use followed a dynamic and sequential selection process and transportation became more efficient over time. Our results point to a complex suite of environmental variables that are considered during offspring deposition, which necessitates a highly dynamic and flexible decision-making process in tadpole-transporting frogs.
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
<p>Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSF), or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from Golden Eagles (Aquila chrysaetos) from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.</p>
Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data
<p>Superficial white matter bundle atlas constructed using fiber clustering algorithms, based on 100 subjects of HCP database (probabilistic tractography). The ROIs of Desikan-Killiany atlas where used to label the final clusters.</p> <p>The atlas is composed of 525 fascicles, 267 bundles in the left hemisphere and 258 bundles in the right hemisphere. It has 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI.</p>
Data for: A hierarchical framework for unpacking the nitrogen challenge
<p>To feed the world population while mitigating pressing nitrogen (N) pollution problems, tremendous efforts have been devoted to developing and implementing N-efficient technologies in food production, but limited progress has been made. The N management improvement on a farm does not necessarily translate to N pollution reduction on a broader scale due to complex responses of natural and human systems and lack of coordination among stakeholders. Consequently, it is imperative to develop an N management framework that encompasses the complex N dynamics across systems and spatial scales, yet simple enough to guide policies and actions of various stakeholders. Here, we propose a new framework, <em>CAFE</em>, that defines four N management systems (Cropping, Animal-crop, Food, and Ecosystem) in a hierarchical manner, and apply it to 13 representative countries to partition N surpluses across systems in a simple and consistent manner, thereby facilitating the identification and prioritization of systems-based intervention strategies. Surprisingly, the Cropping system contributes less than half of the total N surplus within its Ecosystem for most countries, highlighting the importance of N management beyond croplands. As N surplus increases along the <em>CAFE</em> hierarchy, systems-based intervention strategies are revealed: coupling chemical fertilizers with other N sources by maintaining half of the N from manure and N bio-fixation; coupling animal-crop production by reducing animal density to lower than 1.2 livestock units, and increasing self-sufficiency of animal feed to above 50%; coupling food trade with domestic demand and production; and coupling population needs for economic opportunities with environmental capacity of the region.</p>
Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model
<p>This dataset contains the hyperspectral images and train-test split from the article. </p> <p> </p> <p>The corresponding comet ML experiment is:</p> <p><a href="https://www.comet.com/bw4sz/deeptreeattention2/209ca047ed004d778c0f0e728e126bda?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=VWzVg9fkZDMidwu9VOZi2weM9&xAxis=epoch">CometML</a></p> <p>To protect ongoing scientific activities in the area, the geospatial position of the crops have been omitted. Please contact ForestGEO for data requests. </p> <p><a href="http://ForestGEO">https://forestgeo.si.edu/</a></p> <p>For the corresponding git repo please see: </p> <p>Contents:</p> <p>train.csv: The image crops used for model training. The image_path corresponds to the relative path to the image file in the directory.</p> <p>test.csv: The image crops used for model evaluation.</p> <p>*.tif images: A 369 band hyperspectral image using the NEON surface reflectance data cropped by the tree crown.</p> <p>The original HSI data: https://data.neonscience.org/data-products/DP3.30006.001. The location of the crown was predicted using the RGB data product and the deepforest model: https://deepforest.readthedocs.io/. </p> <p>The taxonID abbreviations follow NEON's taxonomy: https://data.neonscience.org/taxonomic-lists</p> <p>OSBS.shp: Shapefile with ensemble predictions for the full Ordway Swisher Biological Station. The ensemblaTa column is the predicted taxonID for each crown. The ens_score is the confidence prediction for that crown. The CHM_height is the extracted raster value from NEON's ecosystem structure canopy height model.</p>
WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning
<p>Here, we provide the Wikifonia dataset in MIDI format and two sets of MIDI files generated in the experiments. We used MuseScore software (i.e., batch convert) to <strong>synthesize MIDI files into audio files</strong></p>
Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
<p>Composite data with NWM 2.0 streamflow, basin PET, drainage area and stoage.</p> <p>SAR data used in this study are downloaded from</p> <p><a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Statistical aspects of interface adhesion and detachment of hierarchically patterned structures: dataset
<p><strong>Data from:</strong></p> <p>N. Esfandiary, M. Zaiser, P. Moretti: Statistical aspects of interface adhesion and detachment of hierarchically patterned structures (2022) Journal of Statistical Mechanics: Theory and Experiment 023301,<br> https://doi.org/10.1088/1742-5468/ac52a4</p> <p> </p> <p><strong>Abstract:</strong></p> <p>We introduce a three dimensional model for interface failure of hierarchical materials adhering to heterogeneous substrates. We find that the hierarchical structure induces scale invariant detachment patterns, which in the limit of low interface disorder prevent interface failure by crack propagation ('detachment fronts'). In the opposite limit of high interface disorder, hierarchical patterns ensure enhanced work of failure as compared to reference non-hierarchical structures. While the study of hierarchical adhesion is motivated by examples of fibrous materials of biological interest, our results indicate that hierarchical patterns can be useful in engineering scenarios in view of tuning and optimizing adhesion properties.</p> <p> </p> <p><strong>Contact:</strong></p> <p>Paolo Moretti<br> Institute of Materials Simulation<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Dr.-Mack-Str. 77<br> 90762 Fürth<br> Germany</p> <p> </p> <p><strong>File naming scheme:</strong></p> <p>hfn: hierarchical fuse network<br> rrn: random reference network</p> <p>uni: uniform threshold distribution <br> wei15: Weibull threshold distribution (shape factor 1.5)<br> wei4: Weibull threshold distribution (shape factor 4)<br> wei9: Weibull threshold distribution (shape factor 9)</p> <p>s1: largest connected components<br> s2: 2nd largest connected components<br> iv: sample iv curve<br> envelope: envelope of iv (displacement control)</p>
Data from: Estimating phenology and phenological shifts with hierarchical modeling
<p class="MsoNormal">This dataset contains daily counts of juvenile chum salmon (<em>Oncorhynchus keta</em>) from the Skagit River, WA between 1990–2019. The analyzed dataset contains 30 years and 4,636 monitoring days in which 2,358,284 migrating chum salmon were counted. This dataset is the companion dataset for phenomix R package.</p>
Supplementary Video for Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning
<p>This file contains video of the real world experiments presented in the paper "Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning". </p> <p>It also provides a high-level overview of the motivation and the theory developed in the paper.<br> </p>
Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method
<p><span>With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.</span></p>
Hierarchical generative modelling for autonomous robots
<p>This is the dataset accompanying the paper "Hierarchical generative modelling for autonomous robots" by Yuan et al.</p>
Validation of SEARCH, a Novel Hierarchical Algorithm to Define Long-term Outcomes After Pulmonary Embolism
ClinicalTrials.gov study NCT05568927. IPD Sharing: NO. Countries: 1. Publications: 46.
Causal Role of Delta and Theta Oscillations in Hierarchical Cognitive Control
ClinicalTrials.gov study NCT06734377. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error
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Data from: Hierarchical Hybrid Enrichment: multi-tiered genomic data collection across evolutionary scales, with application to chorus frogs (Pseudacris)
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Data from: Motion analysis of non-model organisms using a hierarchical model: influence of setup enclosure dimensions on gait parameters of Swinhoe’s striped squirrels as a test case
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Data from: Bayesian hierarchical models suggest oldest known plant-visiting bat was omnivorous
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Data from: Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals
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Recipient and donor characteristics govern the hierarchical structure of heterospecific pollen competition networks
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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.