DATASET: Carnegie Museum of Art merged with Github provenances

DATASET: Provenance information gathered from the 2015 CMOA Github merged with information from the Carnegie Museum of Art online collections website

Description: This dataset contains publicly available information originally published online by the Carnegie Museum of Art which has been formatted by OAD as a CSV file for easy download and analysis with digital tools. Many of the artworks in the list also appear on the Nazi Era Provenance Internet Portal (NEPIP). For more recent updates or additional information concerning the artworks, please contact the Carnegie Museum of Art.

The CMOA collections dataset was published on Github by the Carnegie Museum of Art under a Creative Commons CC0 1.0 licence (no copyright). 

It has been enhanced with a NEW URL field in order to link to the CMOA museum website.

Date data retrieved: August 2021

AOD Version: 1.0

Format: CSV                           Click to DOWNLOAD CSV

https://docs.google.com/spreadsheets/d/e/2PACX-1vQwCWFQ13AB3EFsfXN84BxCMoWZ1keAT8-vxyTTlvEwZLQDuRqsxa5Ebrh0OdVOQfy79VdiB30Om6cm/pub?gid=1728520750&single=true&output=csv

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.

Tutorial for the Looted Art Detector: Using custom indicators

Looted Art Detector: Part 2 Using custom indicators

example with : ALIU Red Flag restorers

The user can analyse provenances for any names or words that seem interesting.

The list below contains the last names of art restorers who were investigated by the OSS Art Looting Investigation Unit for their role in the art market for Nazi-looted art.

Looted Art Detector Tool: Swiss GLAMHACK2021

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: 

Names of persecuted Austrian Jewish collectors

How to verify the provenance texts of artworks for names that might indicate a history of Nazi looting or persecution?

There are many potential sources and lists.

In this post, we look an official Austrian report from 2008 that contains names of Austrian Jewish collectors whose art collections were plundered by the Nazis.

Suspicious provenance errors: Manet's Mellon

Mrs Kurt Riezler (née Käthe Liebermann, daughter of Max Liebermann) was previously mis-identified as Liezler

 Why does it matter? Because her family was persecuted by the Nazis and she herself was a refugee.

EDOUARD MANET

The Melon, c. 1880

César Mange de Hauke

Who was César Mange de Hauke (1900-1965)?

What was the role of this French art dealer in bringing European artworks to America?

Which artists did he handle?
Who were his clients?

Why did the Art Looting Investigation Unit put him on the Red Flag List?

de Haucke, Cesar Monge. Paris, 14 rue du Cherche-Midi. Dealer active in Paris and New York before the war. Active in Paris during the occupation; in contact with Wuester, Haberstock and Hofer; documentary evidence in Unit files.

In this post we take a look at some of Hauke's (Haucke's) transactions in the Knoedler files at the Getty Provenance Index.

Permalink: https://www.openartdata.org/2018/12/cesar-mange-de-hauke-linked-open-data.html

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

Tracing Jewish Art Collectors and other #LostArtPeople by Place of Death

In our previous post, we introduced the idea of using Wikidata Queries to Trace Jewish art collectors and their collections.

In this post, we begin to look at how we might identify these Jewish individuals, many of whom have been not only forgotten but deliberately written out of history. 

What information or feature might help us to identify them?
What do we know about them?

• They died after 1932 
• They were connected to the arts in some way: as collectors, dealers, curators, historians, curators, museum directors, gallery owners, possibly as artists
• Their names might have appeared in exhibitions or catalogs as owners or lenders or donors or experts, or in books or articles as authors, in the provenance texts of collections, for example.

The above criteria is very large and not specific to Jewish collectors in the Nazi era.  How can we further narrow the criteria? One element that distinguishes the fates of Jewish individuals from others is how and where they died, and whether or not they were interned, spoliated or became refugees. 

What kind of markers can we look for in the data?

• Place of death
• Year of death
• Cause of death
• Place of internment
• Significant events like aryanization or arrest or deportation

All of the above correspond to "Properties" that are defined in Wikidata. Not all these properties have been updated for every Wikidata item. But they could be. 

A Wikidata Query can easily show us all the people who are known (in Wikidata) to have died or been interned in a Nazi camp or ghetto.

How to do this?

There were so many Nazi camps in so many countries (see below) that we look for a way to take each one into account without necessarily naming each one in a query.

One way is to use Wikidata's "instance of" (P31).

We can tell the query to list people who died in a place that is defined as any of the following things:

• Nazi concentration camp (Q328468)
• concentration camp (Q152081)
• extermination camp (Q153813)
• ghetto in Nazi-occupied Europe (Q2583015)

We might still miss a few of the camps (due to the crowdsourcing nature of Wikidata, not every item is coded in exactly the same way,) but this should be a good start.

There are several ways to do OR type queries in Wikidata.

We will use the very straight forward UNION. (Please do not hesitate to suggest better ways).

WHERE {
{ ?placedied wdt:P31 wd:Q328468.} UNION { ?placedied wdt:P31 wd:Q152081. } UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placediedwdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q2583015.}


To make the Wikidata query run faster and avoid time outs, we will first check that the place of death has been entered by someone into Wikidata.

?item wdt:P20 ?placedied.

Then, instead of specifying, as we did in the previous query, that we want to list the people who died in the specific Nazi concentration camp of Auschwitz-Birkenau

  ?item wdt:P20 wd:Q7341.  

we want to request people who died in any place coded as an instance of Q328468, Q152081, Q153813 or Q2583015

{ ?placedied wdt:P31 wd:Q328468.} UNION { ?placedied wdt:P31 wd:Q152081. } UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q2583015.}

This should include all the Nazi camps and ghettos listed at the bottom of this post

_______

SELECT DISTINCT ?item ?itemLabel ?pic ?datedied ?placediedLabel ?placedied ?child ?childLabel ?ownedby ?ownedbyLabel ?depicts ?depictsLabel ?depictedby ?depictedbyLabel ?countryLabel ?ownerof ?ownerofLabel ?spouse ?employer ?employerLabel ?spouseLabel ?mother ?motherLabel ?father ?fatherLabel ?sibling ?siblingLabel ?investby ?investbyLabel ?sigperson ?sigpersonLabel ?party ?partyLabel ?partner ?partnerLabel WHERE {

{ ?item wdt:P106 wd:Q1792450.} UNION { ?item wdt:P31 wd:Q1007870. } UNION { ?item wdt:P106 wd:Q173950.} UNION { ?item wdt:P921 wd:Q328376.} UNION { ?item wdt:P106 wd:Q10732476.} UNION { ?item wdt:P106 wd:Q446966.} UNION { ?item wdt:P106 wd:Q22132694.} UNION { ?item wdt:P106 wd:Q674426.}

SERVICE wikibase:label { bd:serviceParam wikibase:language "en" }

?item wdt:P20 ?placedied.  

{ ?placedied wdt:P31 wd:Q328468.} UNION { ?placedied wdt:P31 wd:Q152081. } UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q2583015.}

OPTIONAL  { ?item wdt:P18 ?pic. }

OPTIONAL { ?item wdt:P127 ?ownedby. }

OPTIONAL { ?item wdt:P570 ?datedied. }

OPTIONAL { ?item wdt:P20 ?placedied. }

OPTIONAL { ?item wdt:P180 ?depicts. }

OPTIONAL { ?item wdt:P921 ?plunder. }

OPTIONAL { ?item wdt:P1830 ?ownerof. }

OPTIONAL { ?item wdt:P108 ?employer. }

OPTIONAL { ?item wdt:P569 ?birth. }

OPTIONAL { ?item wdt:P40 ?child. }

OPTIONAL { ?item wdt:P214 ?VIAF_ID. }

OPTIONAL { ?item wdt:P19 ?place_birth. }

OPTIONAL { ?item wdt:P244 ?Library_of_Congress_authority_ID. }

OPTIONAL { ?item wdt:P227 ?GND_ID. }

OPTIONAL { ?item wdt:P245 ?ULAN_ID. }

OPTIONAL { ?item wdt:P26 ?spouse. }

OPTIONAL { ?item wdt:P27 ?country. }

OPTIONAL { ?item wdt:P3342 ?sigperson. }

OPTIONAL { ?item wdt:P102 ?party. }

OPTIONAL { ?item wdt:P1327 ?partner. }

OPTIONAL { ?item wdt:P1840 ?investby. }

OPTIONAL { ?item wdt:P25 ?mother. }

OPTIONAL { ?item wdt:P22 ?father. }

OPTIONAL { ?item wdt:P3373 ?sibling. }

OPTIONAL { ?item wdt:P1299 ?depictedby. }

OPTIONAL { ?item wdt:P39 ?position. }

FILTER (YEAR(?datedied) >= 1933 )

}

LIMIT 20000

_______

note: careful: the filter should read datedied "greater than or equal to" 1933. Sometimes the greater than symbol gets garbled on this blog

(Notice the word OPTIONAL? We are telling Wikidata to get the information if it is available but not to worry about it if it is not. Since we want to understand who these individuals were, the context of their lives and their relations with others, we have added much optional information to the query. This is not strictly speaking necessary, but possibly useful for future network analysis)
The Wikidata Sparql Query can present the results in many different ways.

As a table. 

As a graph. 

With photos

We can zoom in close to view certain specific elements that are hard to see when looking at lots of data.

We also get an idea of where data might be missing.

Try the query yourself.  It shows only those art people who died in a Nazi camp or ghetto. What one immediately notices is how many are missing. 

How to see what is present and what is absent will be the subject of our next post.

Link to Query
https://query.wikidata.org/#%0A%0ASELECT%20DISTINCT%20%3Fitem%20%3FitemLabel%20%3Fpic%20%3Fdatedied%20%3FplacediedLabel%20%3Fplacedied%20%3Fchild%20%3FchildLabel%20%3Fownedby%20%3FownedbyLabel%20%3Fdepicts%20%3FdepictsLabel%20%3Fdepictedby%20%3FdepictedbyLabel%20%3FcountryLabel%20%3Fownerof%20%3FownerofLabel%20%3Fspouse%20%3Femployer%20%3FemployerLabel%20%3FspouseLabel%20%3Fmother%20%3FmotherLabel%20%3Ffather%20%3FfatherLabel%20%3Fsibling%20%3FsiblingLabel%20%3Finvestby%20%3FinvestbyLabel%20%3Fsigperson%20%3FsigpersonLabel%20%3Fparty%20%3FpartyLabel%20%3Fpartner%20%3FpartnerLabel%20WHERE%20%7B%0A%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ1792450.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP31%20wd%3AQ1007870.%20%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ173950.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP921%20wd%3AQ328376.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ10732476.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ446966.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ22132694.%7D%20UNION%20%7B%20%3Fitem%20wdt%3AP106%20wd%3AQ674426.%7D%0A%0A%0ASERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22%20%7D%0A%3Fitem%20wdt%3AP20%20%3Fplacedied.%20%20%0A%7B%20%3Fplacedied%20wdt%3AP31%20wd%3AQ328468.%7D%20UNION%20%7B%20%3Fplacedied%20wdt%3AP31%20wd%3AQ152081.%20%7D%20UNION%20%7B%20%3Fplacedied%20wdt%3AP31%20wd%3AQ153813.%7D%20UNION%20%7B%20%3Fplacedied%20wdt%3AP31%20wd%3AQ153813.%7D%20UNION%20%7B%20%3Fplacedied%20wdt%3AP31%20wd%3AQ2583015.%7D%0AOPTIONAL%20%20%7B%20%3Fitem%20wdt%3AP18%20%3Fpic.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP127%20%3Fownedby.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP570%20%3Fdatedied.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP20%20%3Fplacedied.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP180%20%3Fdepicts.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP921%20%3Fplunder.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP1830%20%3Fownerof.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP108%20%3Femployer.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP569%20%3Fbirth.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP40%20%3Fchild.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP214%20%3FVIAF_ID.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP19%20%3Fplace_birth.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP244%20%3FLibrary_of_Congress_authority_ID.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP227%20%3FGND_ID.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP245%20%3FULAN_ID.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP26%20%3Fspouse.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP27%20%3Fcountry.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP3342%20%3Fsigperson.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP102%20%3Fparty.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP1327%20%3Fpartner.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP1840%20%3Finvestby.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP25%20%3Fmother.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP22%20%3Ffather.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP3373%20%3Fsibling.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP1299%20%3Fdepictedby.%20%7D%0AOPTIONAL%20%7B%20%3Fitem%20wdt%3AP39%20%3Fposition.%20%7D%0A%23%20FILTER%20%28YEAR%28%3Fbirth%29%20%3D%201860%20%26amp%3B%26amp%3B%20YEAR%28%3Fbirth%29%20%26lt%3B%3D%201990%29%0AFILTER%20%28YEAR%28%3Fdatedied%29%20%3E%3D%201933%20%29%0A%7D%0ALIMIT%2020000

Camps and ghettos listed in the Wikidata Query

SELECT ?placedied ?placediedLabel
WHERE
{
  { ?placedied wdt:P31 wd:Q328468.} UNION { ?placedied wdt:P31 wd:Q152081. } UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q153813.} UNION { ?placedied wdt:P31 wd:Q2583015.}
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}



(Short link to query and result - Try it!)