Author

Thomas H. G. Ezard

National Oceanography CentreORCID 0000-0001-8305-6605Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:imaging· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:multiple species· 1 Research Focus:phylogenetics / evolution· 1 Approach:statistical model· 1

Frequent Co-Authors

Anjali GoswamiiD· 2 shared James M. MulqueeneyiD· 2 shared Alex Searle‐BarnesiD· 1 shared Anieke BrombacheriD· 1 shared Marisa Sweeney· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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2 shared papersEvolutionary Biology

Anjali GoswamiORCID

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2 papers

Recent·Most cited

Evolutionary BiologyBMC Ecology and EvolutionPeer-reviewedApr 26, 2025

Assessing the application of landmark-free morphometrics to macroevolutionary analyses

James M. Mulqueeney, Thomas H. G. Ezard, Anjali Goswami

This study compares landmark-free morphometric methods with traditional geometric morphometrics across 322 mammals, showing landmark-free approaches better differentiate taxonomic groups.

13 citationstop 1%

multiple speciesimaging

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Evolutionary BiologyRoyal Society Open SciencePeer-reviewedJun 1, 2024

How many specimens make a sufficient training set for automated 3D feature extraction?

James M. Mulqueeney, Alex Searle‐Barnes, Anieke Brombacher…·Thomas H. G. Ezard

This study determines the minimum number of training images needed for accurate 3D feature extraction using deep learning and evaluates the effectiveness of data augmentation.

13 citations

imagingdeep learning

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