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Data from: Tuning Geometric Morphometrics: an R tool to reduce information loss caused by surface smoothing

Citation

Profico, Antonio et al. (2017), Data from: Tuning Geometric Morphometrics: an R tool to reduce information loss caused by surface smoothing, Dryad, Dataset, https://doi.org/10.5061/dryad.28631

Abstract

The application of Geometric Morphometrics has remarkably increased since 3D imaging techniques have become widespread, such as high-resolution computerised tomography, laser scanning and photogrammetry. Acquisition, 3D rendering and simplification of virtual objects produce faceting and topological artefacts, which can be counteracted by applying decimation and smoothing algorithms. Nevertheless, smoothing algorithms can have detrimental effects. This work aims at developing a method to assess the amount of information loss or recovery after the application of 3D surface smoothing. The method presented here is conceived to optimise the smoothing procedure for 3D surfaces used in Geometric Morphometrics. We implemented the method in a tool running in the r statistical environment. The tool requires one surface, one landmark set and one surface semilandmark set to estimate the best smoothing settings, including algorithm type, iteration and scale factor value. Additional parameters can be tuned by the user. We describe the method in detail, reporting the tool usage, including its main settable parameters. One example is provided as a further explanation of the method. Our method reduces the chances of losing information in Geometric Morphometrics applications and is a unique attempt of standardising a widespread, potentially damaging procedure. The tool represents an advance in the application of Geometric Morphometrics.

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