How solid is an art provenance text? Analysis of a Degas sculpture at the NGA

"Probably", "possibly", "apparently", "proposes" and "allegedly" in texts for Degas' Dancer Adjusting the Shoulder Strap of Her Bodice, original wax 1880s/1890s, cast 1920/1949 at the NGA

Reportedly found in - a sampling of interesting provenances

What does "reportedly found in" mean when it appears in a provenance text for an artworks or antiquity?

Below are a few sample texts.

(Texts published by museums are indicated by color.)

Comparing 1961 and May 30 2019 provenances for Manet's La Sultane in the Bührle collection

How many times did the provenance change?

Word/Phrase	1961 Catalogue Count	2019 Bürhle website Count
dealer	1	0
silberberg	1	9
?	0	2
art market	0	1
by	0	7
rosenberg	0	5
might	0	1
until	0	1
no documents	0	1
was never really and completely owned by Silberberg	0	1

Edouard Manet

Young Woman in Oriental Garb

ca. 1871

Art Provenance Texts as Data: Restitution Histories in Auction Catalogs

Adolph Menzel, restituted to the heirs of Walter Westfeld

When Nazi-looted artworks are restituted, families are often obliged to sell at least some of the artworks. For this they turn to auction houses. As a result, the provenances published by auction houses may provide valuable insights into the art market networks that handled the looted art until the time of restitution. 

Below is a selection of several provenances published online by Sotheby's. Some of the provenance texts indicate that artworks put up for auction had previously been restituted to the families of persecuted collectors. 

For the artworks that have been restituted, the provenances - before the restitution - offer clues to how the resale market for Nazi-looted art worked. Through which hands did the artworks pass? Do any names recur? 

How to use information in the provenance texts of Nazi looted art that has been restituted to find other Nazi-looted artworks

This Nazi-looted painting was restituted in 2016. 
https://www.lostart.de/de/Verlust/526702

Often, when a painting is restituted, it is the conclusion of a long and arduous process of archival research to establish the itinerary of the painting and the different actors involved in its looting (or sale, or transfer, or translocation). 

What happens if we take the NAMES that appear in the provenance AFTER an artwork has left the possession of the persecuted Jewish owner and plug them in to some powerful digital tools to check other provenance texts for their presence?

Could this application of digital tools provide clues that lead to other Nazi-looted artworks?

Unnecessary mysteries

Signs that provenance research has gone off the rails include:

• extremely long texts that leave the reader so confused that he/she concludes that there is no such thing as knowledge
• massive use of words indicating uncertainty or unknowability
• omission of crucial information that is available and which provides important context
• mention of names which are known to be either persecuted Jewish collectors, Red Flag dealers of Nazi looted art, or associates with forgers WITHOUT mentioning that that's who they are
• excessive speculation
• false information

In this series of posts, we will examine a few remarkable examples of needlessly confusing provenance texts published by major museums. 

 

Detector Tool Tutorial: ranking by number of Red Flag and Restitution Case Names

This post, in the educational series on the experimental digital tool for analysing provenance texts, demonstrates Ranking by Red Flag Names (photo: Albright-Knox).

Work in progress. Feedback welcome.

Looted Art Detector Tool: Swiss GLAMHACK2021

Note: 12NOV2024 The Looted Art Detector is undergoing an update on Pythonanywhere and will be back in service soon

TRY IT!

Objective: Identify high priority artworks for provenance research

Description: Online Free Digital Tool

URL: https://artdata.pythonanywhere.com

Approach: Automatic text analysis using frequency counts

note: The frequency counts target textual indicators of UNCERTAINTY, UNRELIABILITY,  or ANONYMITY, as well as the possible presence of RED FLAG names related to NAZI-looted art, forced sales and duress sales. 

The resulting calculations do not signify that an artwork is looted. They simply quantify observations concerning the text for further analysis.

How it works: 

Let's run 1000 NEPIP provenances that contain Munich through the Looted Art Detector

In the previous post we gathered one thousand provenances of artworks listed (for the most part) by American museums on the Nazi Era Provenance Internet Portal that contain the word "Munich" or "München" in the provenance text.

In this post, using the Looted Art Detector developed at the Swiss Glamhack2020 and Glamhack2021, we rank artworks that contain a mention of Munich according to the criteria of "Uncertainty". 

DATASET: Text Analysis Challenge - Detect Looted Art - GLAMhack2020

Download  Provenance Texts Dataset: CSV 

Text Analysis Challenge: Detect Looted Art.

Help Automate Analysis, Flagging and Ranking of Museum Art Provenance Texts by the Probability of a Hidden History

The Question: How to sift through the millions of objects in museums to identify top priorities for intensive research by humans? 

The Goal: Automatically Classify and Rank 60,000+ art provenance texts by probability that further research will turn up a deliberately concealed history of looting, forced sale, theft or forgery. 

The Challenge: Analyse texts quickly for Red Flags, quantify, detect patterns, classify, rank, and learn. Whatever it takes to produce a reliable list of top suspects

For this challenge several datasets will be provided.

1) DATASET: 60,000+ art provenance texts for analysis

(example)

https://www.harvardartmuseums.org/collections/object/296887	[Pierre Matisse Gallery, New York, New York], by 1932, to M. Gutmann, 1936. Maurice Wertheim, by 1937, bequest, to Fogg Art Museum, 1951.;NOTE: Provenance derived from "Degas to Matisse: The Maurice Wertheim Collection," John O'Brian, Harry N. Abrams, New York, 1988.	1951.76
https://www.harvardartmuseums.org/collections/object/229045	Private Collector, Paris, Said to have been bought directly from artist, 1918.;Maurice Wertheim, Purchased from the Valentine Gallery, 1937, Bequest to Fogg Art Museum, 1951.	1951.52
https://www.harvardartmuseums.org/collections/object/229044	Paul Guillaume, Paris, France, 1925, 1935. By 1930, per caption of photo, Paul Guillaume dining room dated c. 1930, Georgel 2006;Maurice Wertheim, 1937, Bequest to Fogg Art Museum, 1951.	1951.51
https://www.harvardartmuseums.org/collections/object/229043	Unidentified owner, Paris, sold, [through Hôtel Drouot, Paris, June 16, 1906, no 30.]. Dr. Alfred Wolff, Munich, (1912). Sir Michael Sadler, England, (1912). [De Hauke & Co., New York], sold, to A. Conger Goodyear, New York, (1929-1937) sold, [through Wildenstein && Co., New York];to Maurice Wertheim, New York (1937-1951) bequest, to Fogg Art Museum, 1951.	1951.49

2) DATASET: 1000 Red Flag Names

(example)

Bignou, Etienne	Bignou	Etienne	Etienne Bignou
Billiet, Director	Billiet	Director	Director Billiet
Binder, Dr. Moritz Julius	Binder	Dr. Moritz Julius	Dr. Moritz Julius Binder
Bing Collection	Bing Collection		Bing Collection
Birtschansky, Zacharie	Birtschansky	Zacharie	Zacharie Birtschansky
Bisson, E.	Bisson	E.	E. Bisson
Blanc, Pierre	Blanc	Pierre	Pierre Blanc
Bleye, Willi	Bleye	Willi	Willi Bleye
Bloch, Dr. Vitale	Bloch	Dr. Vitale	Dr. Vitale Bloch
Bloch-Bauer Collection	Bloch-Bauer Collection		Bloch-Bauer Collection
Blot	Blot		Blot
Bode, Dr.	Bode	Dr.	Dr. Bode
Bodenschatz, General Karl	Bodenschatz	General Karl	General Karl Bodenschatz
Boedecker, Alfred	Boedecker	Alfred	Alfred Boedecker
Boehler, Julius, Jr.	Boehler	Julius	Julius Boehler
Boehler, Julius, Sr.	Boehler	Julius	Julius Boehler
Boehm, Dr. Franz	Boehm	Dr. Franz	Dr. Franz Boehm
Boehmer, Bernhard	Boehmer	Bernhard	Bernhard Boehmer

3) DATESET:  Red Flag Words or Phrases

(example)

flaguncertainty	flaganonymity	flagpuncutation	flagmove	flagreliability
likely	private collector	?	transfer	telephone
probably	anonymous	[	removed	to at least
possibly	art market			until at least
maybe	unidentified			by 19
?	unknown			before 19
	property of a European collector			according to
	private collection
	property of a lady
	anon.

You're the doctor and the texts are your patients! Who's in good health and who's sick? How sick? With what disease? What kind of tests and measurements can we perform on the texts to help us to reach a diagnosis? What kind of markers for should we look for? What kind of patterns? 

What digital methods can we use - and put into practice during the Hackathon - to "diagnose" these texts and prioritize them for "treatment" (ie, additional provenance research)?

• IDENTIFY Red Flag Names and Words in each Text?
• COUNT Red Flag Names and Words in each Text?
• CHARACTERIZE each Text (number of words? sentiment? completeness v gaps? other features to be identified that may be useful)?
• ANALYZE for patterns, links and networks?
• CALCULATE probability that the provenance conceals a Nazi-era history that will prove problematic if investigated in detail
• RANK according to urgency for further in-depth provenance research 

Download  Provenance Texts Dataset: CSV 

Download ALIU INDEX of Red Flag Names: CSV

(Voyant-Tools Whitelist containing names: 

keywords-14ca7131716f24c62c6529fcc143bbd2  )

Download Words Flags to be counted in provenances: CSV 

What might a successful result look like?

• A list of 50 provenances from the DATASET ranked most likely to conceal looted art
• A color-coded evaluation of each provenance (RED, ORANGE, GREEN) by likelihood of concealing looted art
• Instructions how to analyze the provenances with the tools, functions or code to use  (for example, how to use Voyant-Tools to count all the Red Flag Names and inject the result back into the spreadsheet)
• Ideas for going further....

Triage: "assignment of degrees of urgency to wounds or illnesses to decide the order of treatment of a large number of patients or casualties."

Link to Glamhack2020 project for participants
https://hack.glam.opendata.ch/project/7

Help us to test and improve and test and improve the code!

Link to Code on Github 

https://github.com/parisdata/GLAMhack2020

Issues to resolve:
- While the word list counts and general name extraction seem to work pretty well, reconciliation with the list of 1000 Red Flag Names still needs work. To test: A tighter tolerance combined with a more complete listing of alias might help.
- The extraction of transaction years after 1900 gives interesting though incomplete indicators. The decision to exclude dates in parentheses (as they are sometimes biographical dates and not transaction dates) needs to be reviewed and refined. 
(note: The texts are from multiple sources applying multiple formats and deliberately entered exactly as is)

Results RAW (with uncorrected errors for analysis)

Results CSV RAW