------------------------------------------------------------------------------------------------------------- This file was generated on 2024-12-03 by Adriana Günzel DOI: https://doi.org/10.60507/FK2/EI83SL A GENERAL INFORMATION 1. Title of the dataset: Attic Red-Figure Pelike in the Academic Art Museum Bonn (Inv. Nr. 75) 2. Brief description of the research project and its aims: 3. Author Information A. Investigator Contact Information Name: Lang, Matthias Institution: BCDH, Universität Bonn Address: Maximilianstr. 22, 53111 Bonn Email: Matthias.Lang@uni-bonn.de 4. Date of data Collection: 2022-07-07 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 : Corpus Vasorum Antiquorum: BONN, AKADEMISCHES KUNSTMUSEUM 1, 15, PL.(13) 13.1-4 9. Beazley-Archive: Vase Number: 202455, https://www.beazley.ox.ac.uk/record/8C35DB45-975D-4F15-9A32-9D0BB1C36BE3 10. Painter: Painter of the Munich Amphora B DATA & FILE OVERVIEW 1. File List: Pelike_75 Pelike_75_images (images used for reconstruction, distances) Pelike_75_model Pelike_75_highpoly (contains high-polygon model and base color texture) Pelike_75_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 (RealityCapture) 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, Adriana Günzel, Philippe Pathé Previewers originally developed by QDR and maintained at https://github.com/GlobalDataverseCommunityConsortium/dataverse-previewers. Feedback and contributions welcome.