Wikidata:GLAM/Metropolitan Museum of Art/Tools

Dashboards for The Metropolitan Museum of Art Open Access project

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Other tools for reporting on The Met images and content:

Image Tool Description
SPARQL Queries Interesting SPARQL queries to demonstrate Met works in Wikidata
Structured Data on Commons Structured Data on Commons now allows for precision search using Wikibase style statements. For example, on Commons you can find all Met artworks that depict a cat, according to The Met's contributed tagging data using this query string:

haswbstatement:P195=Q160236 haswbstatement:P180=Q146[P459=Q106429444]

Try the search on Commons

More advanced searches can be found on the SDC subpage

Interesting SDC SPARQL queries (need to be logged in):

  • Commons images from The Met depicting swords, clustered by department. (query)
  • Commons images of Met ukiyo-e Japanese prints, clustered by depicted concepts (query)
GLAMorgan On-demand metrics tool that takes a Wikicommons category and analyzes which Wikipedia articles use the media files, and reports back the most used images and a list of articles. Since it is run "live" on Commons for any given year and month, it can take minutes to generate a report.
Baglama2 report A regular monthly report showing the most used Commons media across all Wikipedia editions. Run for large-sized cultural institutions, with each report stored and ready for quick access.
Met Open Access report on PAWS A series of SPARQL queries to help show the most popular artworks, painters, depiction statements for Met objects in Wikidata. It also has timelines and maps showing more info about the most popular artists.
Met Tagging Initiative Reports Statistics around the Met Tagging Initiative use in Wikimedia projects:
Open Tasks to help with Worklists to help with:
Knowledge Graphs Visualize what collections and related properties look like in graph form using Wikidata Query.
Wikidata Game - Depiction example of bridge.gif
Wikidata Game - Depicts Wikidata Distributed Game created to confirm matching P180 (depicts) candidates, generated via an AI/machine learning algorithm that has been trained on Met artworks. Primarily paintings and landscape painting features (mountain, bridge, horse, tree, et al)
Crosswalk databases Crosswalk databases and stats for Met content:

Constituent ID databasees Overview of Met constituent ID process

Databases that work with Met constituent IDs