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Data from: Characterizing vocal repertoires - hard vs. soft classification approaches
负责人:
关键词:
Acoustic analysis chacma baboon Fuzzy Sets gradedness Unsupervised Clustering Vocal Repertoire
DOI:
doi:10.5061/dryad.8bn8p
摘要:
clustering methods are not well suited to capture such gradation within call types, we applied a fuzzy clustering algorithm. We found that this algorithm prov
Data from: The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis
负责人:
关键词:
close relatives;STRUCTURE software;family structure;subpopulation;molecular markers
DOI:
doi:10.5061/dryad.668tn571
摘要:
vation biology and evolutionary biology. Recently, unsupervised Bayesian clustering algorithms have been developed to detect a hidden population structure from genotypic data, assuming amo
Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets"" data-category="" data-cropid="" data-dimen="" data-id="E6637ADB-B053-4E9E-B1CE-C684E3A1D16E"> Supporting data for "Clusterflock: A Flocking Algorithm for Isolating Congruent Phylogenomic Datasets"
负责人:
关键词:
Software
DOI:
doi:10.5524/100247
摘要:
of required comparisons. In Clusterflock, we implement a flocking algorithm designed to find groups (flocks) of orthologous gene families (OGFs) that share
Data from: A simulation–based evaluation of methods for inferring linear barriers to gene flow
负责人:
Landguth, Erin
关键词:
Conservation Biology Conservation Genetics Ecological Genetics Habitat Degradation Population Genetics - Empirical
DOI:
doi:10.5061/dryad.3271mn17
摘要:
s. We evaluate two boundary–detection methods (Monmonier’s algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering
Data from: Stepwise Threshold Clustering: a new method for genotyping MHC loci using next-generation sequencing technology
负责人:
关键词:
Major histocompatibility complex;Next-generation sequencing;MHC;pyrosequencing;Gasterosteus aculeatus
DOI:
doi:10.5061/dryad.4fn4g
摘要:
-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criter
Data from: Resolving ambiguity of concatenation in multi-locus sequence data for the construction of phylogenetic supermatrices
负责人:
关键词:
multipartite matching;species delimitation;Supermatrix;BlastClust;graph theory;Coleoptera;Incongruence;data mining
DOI:
doi:10.5061/dryad.qp367
摘要:
algorithm was used in the initial clustering step, resulting in 11241 clusters or divergent singletons. Clusters were first used for name assignment
Data from: Complexity of possibly gapped histogram and analysis of histogram
负责人:
关键词:
Unsupervised Machine Learning;Macrostate;Data Mechanics;statistical mechanics;Hierarchical Clustering Algorithm
DOI:
doi:10.5061/dryad.bs632
摘要:
the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds
alleviate the problem
负责人:
关键词:
clustering algorithm;Danaus plexippus;STRUCTURE software;hierarchical structure;genetic differentiation;sampling scheme
DOI:
doi:10.5061/dryad.2d4m9
摘要:
implementing a Bayesian algorithm to identify groups of individuals at Hardy-Weinberg and linkage equilibrium. While the method is performing relative
Data from: Do cities represent sources, sinks or isolated islands for urban wild boar population structure?
负责人:
Stillfried, Milena
关键词:
urban ecology urban-rural-gradient movement barrier human-wildlife-conflict DIYABC STRUCTURE BAPS microsatellites Berlin hunting
DOI:
doi:10.5061/dryad.gn714
摘要:
built-up areas and the surrounding rural forests. We applied genetic clustering algorithms to analyse the population genetic structure of the urban boar. We use
Data from: Assessing the performance of DNA barcoding using posterior predictive simulations
负责人:
关键词:
biodiversity;clustering algorithms;Operational Taxonomic Units;model adequacy;substitution models;genetic distances
DOI:
doi:10.5061/dryad.pg470
摘要:
in model performance across taxonomic groups, clustering algorithms, genetic divergence thresholds and substitution models. Taken together, these results illustrate

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