Showing posts with label Digital Art History. Show all posts
Showing posts with label Digital Art History. Show all posts
Dec 7, 2022
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?
May 11, 2022
Tracking looted art by examining the names in between. Example: MEISSNER
In this post, we look in art provenances that contain Meissner, a name that has appeared in connexion with Nazi-looted art.
Dec 19, 2021
Art collectors and the Holocaust: itineraries from birth to death
Henri Rieger, born in Sered, died in Theresienstadt
Henri Hinrichsen, born in Hamburg, died in Auschwitz
Rosa Oppenheimer, born in Berlin, died in Auschwitz
Ernst Pollack, born in Vienna, died in Theresienstadt concentration camp
Margarete Mauthner, born in Berlin, escaped Nazi Germany to die thousands of miles away in Johannesburg South Africa
Maria Altmann, born in Vienna, escaped Nazi Austria and died thousands of miles away in Los Angeles, California.
Click on the Play Arrow
https://public.flourish.studio/visualisation/8168334/
****
How was this interactive data visualization created?
Note: this map only shows birth and death places, not the many other places these art collectors lived.
1) Gather the birth and death places of the selected art collectors and dealers, along with coordinates, latitude and longitude. (Wikidata Query)
https://w.wiki/4a4s
#Places of Death or Birth (with coordinates long and lat) for Art historians dealers collectors museum directors restorers
SELECT DISTINCT ?place ?placeLabel ?coord ?lat ?long
WHERE
{
{?item wdt:P106 wd:Q1792450} UNION {?item wdt:P106 wd:Q173950} UNION {?item wdt:P106 wd:Q10732476} UNION {?item wdt:P106 wd:Q674426} UNION {?item wdt:P106 wd:Q22132694} UNION {?item wdt:P106 wd:Q2145981}
?item (wdt:P20|wdt:P19) ?place.
?place wdt:P625 ?coord.
?place p:P625 ?statement.
?statement psv:P625 ?coordinate_node .
?coordinate_node wikibase:geoLatitude ?lat .
?coordinate_node wikibase:geoLongitude ?long .
SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". } # Helps get the label in your language, if not, then en language
}Download the result of the query. This will provide the location data file for Flourish.
2) Gather the names, descriptions, birth and death places and dates (as well as additional information) about the art collectors.
Link to Wikidata query used in this data visualisation with birth and death places
3) Select a data visualization template in Flourish
Selected template Connections globe4.0.0
4) Upload two data files into Flourish, a file with the geographical names and locations, and a file with the birth and death places of the art collectors and dealers selected
5) Format (still in progress) and publish
Sep 2, 2021
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
Aug 12, 2021
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.
Jul 21, 2021
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.
Jul 18, 2021
Looted Art Detector Tool: Swiss GLAMHACK2021
Objective: Identify high priority artworks for provenance research
Description: Online Free Digital Tool
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:
May 1, 2021
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.
Mar 19, 2021
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
Sep 3, 2020
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?
Who were his clients?
Why did the Art Looting Investigation Unit put him on the Red Flag List?
In this post we take a look at some of Hauke's (Haucke's) transactions in the Knoedler files at the Getty Provenance Index.
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.
Jun 5, 2020
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
2) DATASET: 1000 Red Flag Names
(example)
3) DATESET: Red Flag Words or Phrases
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
(Voyant-Tools Whitelist containing names:
keywords-14ca7131716f24c62c6529fcc143bbd2 )
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 Githubhttps://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)
Apr 30, 2020
Tracing Jewish Art Collectors and other #LostArtPeople by Place of Death
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!)
Subscribe to:
Posts (Atom)