Supplementary dataset for AutoScan3D: Photogrammetric workflow, 3D cranial models, landmark configurations, and reproducibility analyses for geometric morphometrics

This dataset provides the complete supplementary materials associated with the study “A low-cost automated photogrammetric system (AutoScan3D) for 3D human cranial digitization: metric accuracy, landmark repeatability and geometric morphometric shape-space fidelity.” Current limitations in low-cost photogrammetric systems include insufficient transparency regarding data processing workflows, limited access to raw and processed three-dimensional models, and a lack of standardized resources for evaluating landmark repeatability and morphometric fidelity. To address this gap, the present dataset ensures full reproducibility of the digitization and analysis pipeline implemented for cranial geometric morphometrics. The dataset includes: (i) a detailed photogrammetric workflow implemented in Agisoft Metashape, covering image acquisition, alignment, dense point cloud generation, mesh construction, and texture mapping; (ii) high-resolution three-dimensional cranial models in .obj format, preserving complete surface geometry for morphometric analysis; (iii) a curated set of anatomical cranial landmarks, including identifiers, three-dimensional coordinates, and standardized anatomical definitions based on homology criteria; (iv) landmark coordinate files exported in .fcsv format from 3D Slicer (ALPACA module), structured for direct use in geometric morphometric pipelines; (v) R scripts developed for data processing and statistical analysis, including inter-landmark distance matrix generation, Bland–Altman analysis, coefficient of variation, intraclass correlation coefficient (ICC), and graphical outputs; and (vi) quantitative repeatability analyses, providing precision error metrics (SD, CV), ICC values, and 95% confidence intervals for assessing the reliability and stability of the digitization process. Collectively, these materials provide a transparent, reproducible, and scalable framework for evaluating the geometric accuracy and morphometric fidelity of low-cost photogrammetric systems. This dataset contributes to advancing methodological standardization in digital morphology and supports future comparative, educational, and research applications in anatomical sciences.