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Dataset results
114 results for “hierarchical modelling”
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"
<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into The histology images were classified as “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) according to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer”, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>
Cell features for "Datasets for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model""
<p>This repository contains features and corresponding coordinates of cells extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) and corresponded to the article dividing the training and test sets.<br><br></p> <p>The features of all cells were extracted by Hovernet and Transnuseg at 40x objective magnification for extraction masking and further feature extraction</p>
Experimental Results for the SoCS 2024 Paper: "Modeling Assistance for Hierarchical Planning: An Approach for Correcting Hierarchical Domains with Missing Actions"
<p>This collection contains all the experimental results produced in the empirical evaluation for the paper "Modeling Assistance for Hierarchical Planning: An Approach for Correcting Hierarchical Domains with Missing Actions", accepted by The 17th International Symposium on Combinatorial Search (SoCS 2024). For a detailed description about this collection, please read the README file. </p>
Forest Carbon Modeling Improved through Hierarchical Integration of Pool-Based Measurements
<p>This folder contains data from </p> <ol> <li>measured carbon stocks (carbonpools) from forest inventories</li> <li>carbon stocks (modeled_C_stock_40) estimated by each HDA constraint</li> <li>posterior parameter sets (para_posterior) after burn-in</li> <li>carbon fluxes (modeled_C_HD) estimated by 500 randomly selected posterior parameter sets from each HDA step.</li> <li>carbon stocks (modeled_C_stock_40_DEF) estimated by the default model settings (uninformed)</li> </ol> <p>figures.R to reproduce the figures in the manuscript. </p>
Reproduction package for the paper "BH-BH mergers with & without EM counterpart: A model for stable tertiary mass transfer in hierarchical triple systems"
<p>This is a reproduction package for the paper "BH-BH mergers with & without EM counterpart: A model for stable tertiary mass transfer in hierarchical triple systems" by Kummer et al. (2024). It aims to provide the most important data products and reproduce the Figures of the paper.</p> <p> </p>
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>
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>
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>
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>
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>
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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Data from: Estimating phenology and phenological shifts with hierarchical modeling
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Data from: Propagule pressure in the presence of uncertainty: extending the utility of proxy variables with hierarchical models
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Identification of determinants of pollen donor fecundity using the hierarchical neighborhood model
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Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
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Data from: Multi-DICE: R package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes
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Data from: Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits
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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.