Supplementary dataset for equine hoof shape analysis: morphometric variables, planimetric assessment and geometric morphometric outputs associated with navicular syndrome

This dataset provides the complete supplementary material supporting the study “Navicular Syndrome in Equine is reflected in hoof shape, not size: a geometric morphometric approach.” It contains four structured components integrating traditional morphometry and geometric morphometric outputs derived from a controlled sample of equine forelimbs with and without radiographically confirmed Navicular Syndrome (NS). The first component comprises detailed definitions of morphometric variables obtained from lateral and solar views of the hoof capsule, including angular, linear, proportional, and perimeter measurements grounded in established parameters of equine hoof conformation. The second component includes planimetric variables derived from stereological point-counting procedures, quantifying the proportional distribution of solar surface compartments (sole, frog, and paracuneal sulci) based on the Delesse principle. The third and fourth components correspond to geometric morphometric outputs, specifically the covariance matrix and principal component (PC) scores generated after Generalized Procrustes Analysis (GPA) of landmark configurations. These data capture shape variation independent of size, position, and orientation, and constitute the basis for multivariate analyses including PCA, CVA, clustering, and modularity assessment.本数据集为研究《马舟骨综合征（Navicular Syndrome, NS）体现在蹄形而非蹄尺：基于几何形态测量学的研究》提供完整补充材料。本数据集包含四个结构化组成部分，整合了传统形态测量学与几何形态测量学（geometric morphometric）分析结果，其样本为经影像学确诊患有与未患有舟骨综合征的马前肢受控样本。 第一部分包含从蹄囊外侧观与底面观获取的形态测量变量的详细定义，涵盖基于公认马蹄形态参数建立的角度、线性、比例及周长测量指标。第二部分包含通过体视学点计数程序获取的平面测量变量，基于德莱塞原理（Delesse principle）量化蹄底面分区（蹄底、蹄叉及蹄楔旁沟）的比例分布情况。 第三与第四部分对应几何形态测量学分析结果，具体为标志点构型经广义Procrustes分析（Generalized Procrustes Analysis, GPA）后生成的协方差矩阵与主成分（Principal Component, PC）得分。此类数据可捕获与尺寸、位置及方位无关的形态变异，为包括主成分分析（Principal Component Analysis, PCA）、典范变量分析（Canonical Variate Analysis, CVA）、聚类分析及模块化评估在内的多变量分析提供基础。