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Morphological variability or inter-observer bias? A methodological toolkit to improve data quality of multi-researcher datasets for the analysis of morphological variation.

Dominik SchüßlerMarina B BlancoNicola K GuthrieGabriele M SgarlataMelanie DammhahnRefaly ErnestMamy Rina EvasoaAlida HasiniainaDaniel HendingFabien JanBarbara le PorsAlex MillerGillian OlivieriAndo N RakotonanaharySolofomalala Jacques RakotondranaryRomule RakotondravonyTantely RalantoharijaonaVeronarindra RamananjatoBlanchard RandrianambininaNancia N RaoelinjanakolonaEmilienne RasoazanabaryRodin M RasoloarisonDavid W RasolofosonSolofonirina RasoloharijaonaEmmanuel RasolondraibeSam Hyde RobertsHelena TeixeiraTobias van ElstSteig E JohnsonJörg U GanzhornLounès ChikhiPeter M KappelerEdward E LouisJordi SalmonaUte Radespiel
Published in: American journal of biological anthropology (2023)
We highlighted the usefulness of large multi-researcher datasets for testing ecological hypotheses after correcting for inter-observer biases. Using genus-wide tests, we outlined generalizable patterns of morphological variability across all mouse lemurs. This new methodological toolkit aims to facilitate future large-scale morphological comparisons for a wide range of taxa and applications.
Keyphrases
  • rna seq
  • climate change
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  • machine learning
  • human health
  • single cell
  • deep learning