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Data from: A novel integrative method for measuring body condition in ecological studies based on physiological dysregulation
负责人:
关键词:
body condition health state physiological dysregulation Multivariate distance Mahalanobis distance metabolic rate
DOI:
doi:10.5061/dryad.mf0ns
摘要:
’ physiological state based on Mahalanobis distance computed from physiological biomarkers. We previously demonstrated the validity of this approach for studying ageing
Data from: High-throughput H295R steroidogenesis assay: utility as an alternative and a statistical approach to characterize effects
负责人:
关键词:
H295R;high-throughput screening;steroidogenesis;toxcast;Mahalanobis distance
DOI:
doi:10.5061/dryad.385j7
摘要:
s was computed for 654 chemicals to indicate the magnitude of effects on the synthesis of 11 hormones. The maximum mean Mahalanobis distance (maxmMd) values wer
Data from: pcadapt: an R package to perform genome scans for selection based on principal component analysis
负责人:
关键词:
Bioinfomatics\/Phyloinfomatics;Landscape Genetics;Adaptation;Population Genetics - Empirical
DOI:
doi:10.5061/dryad.8290n
摘要:
h statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans availa
Data from: Behavioral responses of individual blue whales (Balaenoptera musculus) to mid-frequency military sonar
负责人:
Southall, Brandon L.
关键词:
behavioral response blue whale context endangered species marine mammal sonar
DOI:
doi:10.5061/dryad.d0mv3dh
摘要:
gm and quantitative change-point analyses using Mahalanobis distance statistics. Both methods identified clear changes in some conditions. More than 50% of blue whales in deep
Data from: Composite measures of selection can improve the signal-to-noise ratio in genome scans
负责人:
关键词:
composite signals of selection;meta-analysis;genome-wide associations;minimum covariance determinant;Mahalanobis distance
DOI:
doi:10.5061/dryad.bp11m
摘要:
multiple tests, including multivariate distance measures and methods for combining P-values. We evaluate these methods on (i) simulated landscape genetic data

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