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.
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The resulting calculations do not signify that an artwork is looted. They simply quantify observations concerning the text for further analysis.
How it works:
- The user uploads a CSV file that contains provenance texts
note: The uploaded CSV can contain other information as well - urls, titles, artists, etc. The only requirement is that it also contain one Column with the provenance texts.
The program will ask the user to enter the name of the column that contains the provenance text.
- The Provenance Text Analyser calculates the number of times certain words appear in each provenance text and downloads a CSV named results.csv
note: The results.csv file contains all the original information uploaded by the user PLUS additional columns word counts for can contain other information as well - urls, titles, artists, etc. The only requirement is that it also contain one column with the provenance texts.
The program will ask the user to enter the name of the column that contains the provenance text.
- The user opens this enhanced CSV in his/her preferred spreadsheet or other tool and sorts and filters the results to obtain a priority list of artworks most likely to have problematic provenances
Instructions/suggestions are provided to help the user sort the results.
1) Recommendation: Create an "Uncertainty Index" and sort in descending order
Uncertainty index = Uncertainty Flags/Word Count
Explanation: The artworks with the highest concentration of words like "probably, "likely", "maybe", "possibly" and "?" show high uncertainty about the provenance.
- The user has the option of uploading his/her own custom list of key indicators (words and names to count)
Explanation: The list of key words to count is a work in progress. Users are encouraged to download the existing list and add their own words and names.
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This online tool uses code developed during the Swiss GLAMHACK2021 by Rae Knowler,
building on code developed during the Swiss GLAMHACK2020.
FAQ: TBD
PythonAnywhere: https://artdata.pythonanywhere.com
Documentation
Github: https://github.com/parisdata/2021GLAMHACK
developer Glamhack2021: Rae Knowler
Glamhack2020: https://www.openartdata.org/2020/06/art-provenance-dataset-text-analysis.html
