------------------------------------------------------------------------------------------------------------- This file was generated on 2025-10-10 by Adriana Günzel DOI: https://doi.org/10.60507/FK2/C3NOUU A GENERAL INFORMATION 1. Title of the dataset: Terracotta figurine of a woman with a goose in the Museum of the University of Tübingen (Inv. Nr. 5685) 2. Brief description of the research project and its aims: 3. Author Information A. Investigator Contact Information Name: Günzel, Adriana Institution: Institut für Archäologie und Kulturanthropologie, Abteilung Klassische Archäologie, Universität Bonn Address: Römerstraße 164, 53111 Bonn Email: aguenzel@uni-bonn.de 4. Date of data Collection: 2024-03-19 5. Information about funding sources that supported the collection of the data: Universität Bonn 6. Language of the dataset: English 7. Geographic location of data collection: Tübingen, Museum Alte Kulturen 8. Bibliography : unpublished B DATA & FILE OVERVIEW 1. File List: Terracotta_figurine_woman_goose_5685 Terracotta_figurine_woman_goose_5685_images (images used for reconstruction, distances) Terracotta_figurine_woman_goose_5685_model Terracotta_figurine_woman_goose_5685_highpoly (contains highpolygon model and diffuse map) Terracotta_figurine_woman_goose_5685_gameready (contains gameready model, diffuse map, ambient occlusion map, normal map, metallness map, roughness map) 2. Are there multiple versions of the dataset? no C SHARING/ACCESS INFORMATION 1. Was data derived from another source? no 2. Licenses/restrictions placed on the data: no D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Model was generated with photogrammetry (Agisoft Metashape) Cleaning was done in Blender Texture Baking was done in Substance Designer and Painter 2. Methods for processing the data: Can be opend in any 3D-software package 4. People involved in sample collection, processing, analysis and/or submission: Liza-Marie Peters, Matthias Lang, Philippe Pathé Previewers originally developed by QDR and maintained at https://github.com/GlobalDataverseCommunityConsortium/dataverse-previewers. Feedback and contributions welcome.