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Daniel W. A. Noble

Researcher at Australian National University

Publications -  95
Citations -  3117

Daniel W. A. Noble is an academic researcher from Australian National University. The author has contributed to research in topics: Biology & Population. The author has an hindex of 22, co-authored 82 publications receiving 1868 citations. Previous affiliations of Daniel W. A. Noble include University of Guelph & Garvan Institute of Medical Research.

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Meta-evaluation of meta-analysis: ten appraisal questions for biologists

TL;DR: Ten questions are aimed at enabling a biologist to evaluate whether a biological meta-analysis embodies ‘mega-enlightenment’, a “mega-mistake”, or something in between.
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Developmental temperatures and phenotypic plasticity in reptiles: a systematic review and meta-analysis.

TL;DR: The highly heterogeneous nature of the effects the authors observe, along with a large amount of unexplained variability, indicates that the shape of reaction norms between phenotype and temperature,Along with ecological and/or experimental factors, are important when considering general patterns
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Nonspecific DNA binding of genome-regulating proteins as a biological control mechanism: Measurement of DNA-bound Escherichia coli lac repressor in vivo

TL;DR: The results fit a quantitative model for operon regulation in which nonspecific DNA-repressor complexes play a key role in determining basal and constitutive levels of gene expression.
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Unisexual salamanders (genus Ambystoma) present a new reproductive mode for eukaryotes

TL;DR: It is found that the genetic makeup of the unisexuals is highly variable and that unisexual individuals share microsatellite alleles with sexual individuals within populations, and suggests kleptogenesis as a new unisexual reproductive mode that is used by these salamanders.
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Nonindependence and sensitivity analyses in ecological and evolutionary meta‐analyses

TL;DR: This work argues that exploring the effects of procedural decisions in a meta‐analysis and statistical assumptions using sensitivity analyses are extremely important in assessing the impact of nonindependence, and provides pragmatic solutions for dealing with nonindependent study designs, and for analysing dependent effect sizes.