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、激光扫描及摄影测量法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、三维渲染及简化操作时，会产生面化伪影与拓扑伪影，可通过抽稀算法与平滑算法予以抑制。不过，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优化几何形态测量学中三维表面的平滑处理流程。我们将该方法集成至一款运行于R统计环境中的工具中。 该工具需输入一组三维表面、一组标志点（landmark）集与一组表面半标志点（semilandmark）集，以估算最优平滑参数设置，包括算法类型、迭代次数与缩放因子取值。用户还可对其余参数进行自定义调整。我们对该方法进行了详细阐述，并说明了工具的使用方式，包括其主要可调整参数。此外，本文还提供了一个示例以进一步阐释该方法。 本方法可降低几何形态测量学应用场景中信息丢失的概率，亦是针对这一应用广泛却可能造成损害的流程开展标准化的首次尝试。该工具为几何形态测量学的应用带来了新的进展。