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

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.

自高分辨率计算机断层扫描（high-resolution computerised tomography）、激光扫描（laser scanning）以及摄影测量法（photogrammetry）等三维成像技术普及以来，几何形态测量学（Geometric Morphometrics）的应用规模显著增长。对虚拟对象进行采集、三维渲染与简化的过程会产生面片化效应与拓扑伪影，可通过应用降采样（decimation）与平滑算法进行抵消。然而，平滑算法本身也可能带来不利影响。本研究旨在开发一种方法，用于评估三维表面平滑处理后信息丢失或恢复的程度。

本文提出的方法旨在为几何形态测量学中所使用的三维表面优化其平滑处理流程。我们将该方法集成至一款运行于R统计环境中的工具内。该工具仅需输入一组表面模型、一组地标点集（landmark set）以及一组表面半地标点集（surface semilandmark set），即可估算出最优的平滑处理参数，包括算法类型、迭代次数与缩放因子数值。用户还可对其余参数进行自定义调整。我们详细阐述了该方法，并对工具的使用方法（包括核心可调参数）进行了说明。此外，本文还提供了一则示例，以进一步阐释该方法的应用细节。

本方法可降低几何形态测量学应用中出现信息丢失的概率，同时也是首个针对这一应用广泛但存在潜在危害的流程进行标准化的尝试。该工具的问世，为几何形态测量学的应用研究带来了新的进展。