------------------------------------------------------------------------------------------------------------- This file was generated on 2024-11-11 by Adriana Günzel DOI: https://doi.org/10.60507/FK2/T0SWNU A GENERAL INFORMATION 1. Title of the dataset: Attic Black-Figure Kyathos in the Academic Art Museum Bonn (Inv. Nr. 57) 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, 53115 Bonn Email: aguenzel@uni-bonn.de 4. Date of data Collection: 2024-10-23 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: Bonn, Akademisches Kunstmuseum 8. Bibliography : Geominy, W., Das Akademische Kunstmuseum der Universität Bonn unter der Direktion von Reinhard Kekule (Amsterdam, 1989): PL.12 (PARTS) 9. Beazley-Archive: Vase Number: 16799, https://www.beazley.ox.ac.uk/record/8FBFF7C4-8A80-4492-A8A5-1AB7F8B55D8D B DATA & FILE OVERVIEW 1. File List: Kyathos_57 Kyathos_57_images (images used for reconstruction, distances) Kyathos_57_model Kyathos_57_highpoly (contains high-polygon model and base color texture) Kyathos_57_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.