------------------------------------------------------------------------------------------------------------- This file was generated on 2024-12-10 by Adriana Günzel DOI: https://doi.org/10.60507/FK2/D5ZDUK A GENERAL INFORMATION 1. Title of the dataset: Attic Black-Figure Bowl in the Academic Art Museum Bonn (Inv. 51a) 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: 2024-06-13 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 : Beazley, J.D., Attic Black-Figure Vase-Painters (Oxford, 1956): 200.7 9. Beazley-Archive: Vase Number: 302584, https://www.beazley.ox.ac.uk/record/E7E90072-0632-40E5-A20B-81125489A9B4 10. Painter: Wraith Painter B DATA & FILE OVERVIEW 1. File List: Bowl_51a Bowl_51a_images (images used for reconstruction, distances) Bowl_51a_model Bowl_51a_highpoly (contains highpolygon model and diffuse map) Bowl_51a_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.