-------------------------------------------------------------------------------------------------------------

This file was generated on 2024-10-29 by Adriana Günzel

DOI:  https://doi.org/10.60507/FK2/LPOO1C

A GENERAL INFORMATION

1. Title of the dataset: Attic Black-Figure Amphora in the Academic Art Museum Bonn (Inv. 44)

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: 2023-07-07

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 : Archäologischer Anzeiger: 1935, 427-28, FIGS.14-15 (A, DRAWING OF B)

9. Beazley-Archive: Vase Number: 8955, https://www.beazley.ox.ac.uk/record/C3F40E49-A682-41A3-9FE5-83AD324A35E2

10. Painter

11. Potter 

B DATA & FILE OVERVIEW

1. File List: 
	
Amphora_44
	Amphora_44_images (images used for reconstruction, distances)
	Amphora_44_model
		Amphora_44_highpoly (contains high-polygon model and base color texture)
		Amphora_44_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.