The table below shows provenance texts as published on AIC's website in August 2024.
Provenance gaps of over a thousand years are not uncommon.
Aug 30, 2024
Alsdorf in provenances of artworks in the Chicago Art Institute
James and Marilynn Alsdorf contributed many valuable artworks to the Art Institute of Chicago.
Aug 27, 2024
Loebl, Kleinberger and Sperling in NEPIP provenances in American Museums
Harry Sperling, the grandson of art dealer Franz Kleinberger, became head of the firm F. Kleinberger Galleries. His cousin, Allan Loebl, an Art Looting Investigation Unit Red Flag Name, was in charge of the Paris office during WWII.
Below are the provenances of some of the artworks that transited via Kleinberger on their way to US museums.
Aug 26, 2024
DATASET: Art Institute of Chicago Provenance texts for artworks created before 1945 and acquired after 1932
Dataset Name: Enhanced AIC Provenance Research Dataset
Description: This enhanced Provenance dataset has been constructed from information available on the public internet site of the Art Institute of Chicago
The dataset focuses on artworks created before 1945 and acquired after 1932.
It merges the list of artworks on the Nazi Era Provenance Internet Portal with provenance texts published on the AIC's detailed item pages, as well as other artworks not on NEPIP.
This dataset is intended to facilitate research into Holocaust-era provenance for scholars, art historians and families.
The original and best source of information concerning provenance remains the Art Institute of Chicago's public website.
Original data sources that were merged to create new dataset:
Original data sources that were merged to create new dataset:
- Art Institute of Chicago NEPIP list 2017
- Provenance texts published on the Art Institute of Chicago's public website in August 2024
Columns include: Source Url, Artist, Title, Date, Medium, Credit Line, AccNum, Provenance, Exhibitions, Dimensions, Publications, Created before 1945, Acquired after 1932, NEPIP
(Created before 1945, Acquired after 1932, NEPIP are boolean TRUE or FALSE)
Format: Google Sheets
View:
Download:
Publication Date: 26 August 2024
(see also: Alsdorf in provenances of the AIC)
Aug 21, 2024
Art Market Network Analysis with Wikidata Sparql Queries and Beyond
What might replicable data pipeline from #Wikidata #KG Query to Data Frame to Network Visualisation of owners of artworks passing through a specific network look like?
— Open Art Data | @openartdata.bsky.social (@OpenLinkArtData) August 21, 2024
Perls Family Network-Red
Links to other owners-Blue
Jupyter Notebook in Google CoLabs
👉https://t.co/2oyU0kmuqi pic.twitter.com/UdQhY8ZKVp
What might replicable data pipeline from #Wikidata #KG Query to Data Frame to Network Visualisation of owners of artworks passing through a specific network look like?
In the example below, we look at 27 artworks that passed through one of the members of the Perls art dealing dynasty or one of their companies.
The starting point is a Wikdata Query to retrieve the artworks known to have been owned by one of the Perls family, as well as the other known owners of the same artworks.
The information is retrieved from Wikidata, loaded into a Data Frame, then visualised with MatPlotLib.
The code is saved in a Jupyter Notebook and Shared publicly via Google Colab.
Anyone with a Google Account should be able to run the code simply by clicking on the arrows to the left of each code cell.
Try it and let me know if it works.
https://colab.research.google.com/drive/1f7V2SMzxkCmt2lbCS3l4ulotqUGghNAm#scrollTo=0sPcg-gWZOo3
Perls Family Network-Red
Links to other owners-Blue
Jupyter Notebook in Google CoLabs
Aug 19, 2024
Provenance patterns: visualising unreliability words in red
In this post, we look at another way of automating the analysis of hundreds of artworks at a time with reusable code.
"Unreliability" words have been automatically formatted in red.
The count refers to the number of artworks containing "unreliability" words for the same artist in the dataset.
Selected paintings with "unreliability" words such as "probably", "possibly", "presumably" and "likely".
The analysis and table were produced by Python code run in a Jupyter Notebook on a Mac.
The Notebook will be shared after a few more tests.
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