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69 results for “feature selectivity”
The density of anthropogenic features explains seasonal and behaviour-based functional responses in selection of linear features by a social predator
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Data from: Compensatory selection for roads over natural linear features by wolves in northern Ontario: implications for caribou conservation
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Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?
<p>This is a research compendium (RC) for the publication</p> <blockquote> <p>Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?</p> </blockquote> <p>Code, figures, appendices and the manuscript can be found in the corresponding <a href="https://github.com/pat-s/2019-feature-selection">GitHub repository</a>.</p> <p>This RC is a static snapshot at the time of submission. The GitHub repository holds the latest version and may see changes after the publication was accepted.</p> <p><strong>Data sources and description</strong></p> <ul> <li><em>aoi.gpkg</em><strong>:</strong> Area of interest for downloading Sentinel-2 images. <em>Not used in the publication<strong>. </strong></em>Source:<em><strong> </strong></em>Custom.</li> <li><em>forest_mask.gpkg</em>: A forest/non-forest mask of the Basque Country. <em>Not used in the publication</em>. Source:<em><strong> </strong></em>Custom.</li> <li><em>hyperspectral.zip: </em>Hyperspectral remote sensing data used to extract reflectance values on the tree level. Source:<em><strong> </strong></em>Custom.</li> <li><em>plot-locations.gpkg: </em>Spatial location of the plots used in the study. Source:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data-corrected.zip: </em>Corrected in-situ data containing defoliation information on the tree level. A correction of the spatial location was applied by the creators of the data. Source:<em><strong> </strong></em>Custom.</li> <li><em>tree-in-situ-data.zip: </em>First version of in-situ data containing defoliation information on the tree level.<em> Not used in the publication. </em>Source:<em><strong> </strong></em>Custom.</li> </ul> <p><strong>Licenses</strong></p> <p>All files are licensed under CC BY 4.0.</p>
Data set for "Dynamic perceptual feature selectivity in primary somatosensory cortex upon reversal learning"
<p>This repository contains the data used to generate the figures and well as the main codes that were used for analyses.</p>
Spatially anonymized data from: Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds
<p>This dataset consists of spatially anonymized movement data as well as environmental covariate data to estimate energy landscape step selection selections for migratory golden eagles that summer in Alaska.</p> <ol> <li>Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality.</li> <li>Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el‐SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle <em>Aquila chrysaetos</em>, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014 to 2017.</li> <li>Fitted el‐SSFs indicated eagles had a strong general preference for south‐facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el‐SSFs also provided evidence that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower‐paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions.</li> <li>Regardless of the behavioural mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have previously been documented to affect the ecology of terrestrial species (e.g. large mammals) by modifying individuals' movement patterns; our work shows that these effects on movement extend to avian taxa.</li> </ol>
Data from:Identification of landscape features influencing gene flow: how useful are habitat selection models?
Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modelling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider their explicit assumptions. We calculated pairwise genetic distances among 301 Alaskan Dall's sheep (Ovis dalli dalli) using an intensive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important seasonally (rugged terrain, mid-range elevation) were not influential to breeding dispersal. This work underscores that consideration of both habitat selection and landscape genetics models in developing conservation strategies will ensure resources are managed to meet both the immediate survival needs of a species and allow for long-term genetic connectivity.
Data from: Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Marburg virus evolution
Background. The Marburg virus (MARV) has a negative-sense single-stranded RNA genome, belongs to the family Filoviridae, and is responsible for several outbreaks of highly fatal hemorrhagic fever. Codon usage patterns of viruses reflect a series of evolutionary changes that enable viruses to shape their survival rates and fitness toward the external environment and, most importantly, their hosts. To understand the evolution of MARV at the codon level, we report a comprehensive analysis of synonymous codon usage patterns in MARV genomes. Multiple codon analysis approaches and statistical methods were performed to determine overall codon usage patterns, biases in codon usage, and influence of various factors, including mutation pressure, natural selection, and its two hosts, Homo sapiens and Rousettus aegyptiacus. Results. Nucleotide composition and relative synonymous codon usage (RSCU) analysis revealed that MARV shows mutation bias and prefers U- and A-ended codons to code amino acids. Effective number of codons analysis indicated that overall codon usage among MARV genomes is slightly biased. The Parity Rule 2 plot analysis showed that GC and AU nucleotides were not used proportionally which accounts for the presence of natural selection. Codon usage patterns of MARV were also found to be influenced by its hosts. This indicates that MARV have evolved codon usage patterns that are specific to both of its hosts. Moreover, selection pressure from R. aegyptiacus on the MARV RSCU patterns was found to be dominant compared with that from H. sapiens. Overall, mutation pressure was found to be the most important and dominant force that shapes codon usage patterns in MARV. Conclusions. To our knowledge, this is the first detailed codon usage analysis of MARV and extends our understanding of the mechanisms that contribute to codon usage and evolution of MARV.
Validating and Confirming Crucial Service Quality Attributes to Airline Customers' Recommendations: A Feature Selection Approach
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Feature selection in an interactive search-based PLA design approach
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Selective area epitaxy of GaAs: the unintuitive role of feature size and pitch
<p>Selective area epitaxy (SAE) provides the path for scalable fabrication of semiconductor nanostructures in a device-compatible configuration. In the current paradigm, SAE is understood as localized epitaxy and is modelled by combining planar and self-assembled nanowire growth mechanisms. Here we use GaAs SAE as a model system to provide a different perspective. First, we provide evidence of the significant impact of the annealing stage in the calculation of the growth rates. Then, by elucidating the effect of geometrical constraints on the growth of the semiconductor crystal, we demonstrate the role of adatom desorption and resorption beyond the direct-impingement and diffusion-limited regime. Our theoretical model explains the effect of these constraints on the growth, and in particular why the SAE growth rate is highly sensitive to the pattern geometry. Finally, the disagreement of the model at the largest pitch points to non-negligible multiple adatom recycling between patterned features. Overall, our findings point out the importance of considering adatom diffusion, adsorption and desorption dynamics in designing the SAE pattern to create predetermined nanoscale structures across a wafer. These results are fundamental for the SAE process to become viable in the semiconductor industry.</p>
Data from: Genomic analysis of codon usage shows influence of mutation pressure, natural selection, and host features on Marburg virus evolution
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Data from:Identification of landscape features influencing gene flow: how useful are habitat selection models?
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Spatially anonymized data from: Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds
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Automated Selection and Ordering of Clip Sequences for Music Videos based on Tonal Tension and Visual Features
<p>Dataset, Videos, and Code of the article sent to EvoMusart2025 entitled "Automated Selection and Ordering of Clip Sequences for Music Videos based on Tonal Tension and Visual Features".</p>
Impact of Molecular Pathological And Clinical Features for Adjuvant Treatment Selection in Endometrial Cancer: Asian Registry
ClinicalTrials.gov study NCT07354087. IPD Sharing: NO. Countries: 1. Publications: 0.
Check of Optical Features and Accuracy of the Zeiss ZO Lens After Selection and Calculation With OKULIX
ClinicalTrials.gov study NCT00842959. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Identification of biomarkers that distinguish chemical contaminants using a gradient feature selection method
GEO Series GSE19662. Rattus norvegicus. 531 samples. Type: Expression profiling by array.
DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection
<p>Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse. Extract and place them in the <em>data </em>folder, and there will be two two folders <em>human </em>and <em>mouse </em>containing necessary data for training and testing.</p>
Bożepole Małe 14, feature 589, selection 2
Fragmenty naczyń ceramicznych (cykl łużycko-pomorski) z ob. 589 na stan. 14 w miejscowosci Bożepole Małe, woj. pomorskie. Zabytki z badań Archeobaltica (https://www.archeobaltica.pl/). Selected potsherds (lusatian/pomeranian cycle - late Bronze Age/early Iron Age) from feature 589 on Bożepole Małe 14 (Pomerania, N Poland) archaeological site. Excavation by Archeobaltica (https://www.archeobaltica.pl/). More from this feature: 1. https://skfb.ly/6V9EP 2. https://skfb.ly/6UWzw Source: Objaverse 1.0 / Sketchfab
Feature selection in an interactive search-based PLA design approach dataset
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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.