Linked Data at UChicago Libraries

Linked Data

Background Exploration and Goals

Current Linked Data Environment

  • Internal Data Management: New York Public Library
    B.I.L.L.I. Bibliographic Identifiers for Library Location Information
    Crosswalk unique, homegrown subject heading systems to LC with linked data.
     
  • Producing Linked Data: Library of Congress
    Chronicling America
    Promoting linked data from newspapers for visualizations, mashups, and other projects.
     
  • Consuming Linked Data: Fundación Ignacio Larramendi
    Polymath Virtual Library
    Making a virtual collection and adding semantics and links to data from large data aggregators.
     
  • Collaborations with Other Institutions: Includes UCLA, Indiana University, Johns Hopkins, and the Library of Congress
    Sheet Music Consortium
    A collaboration across institutions adds linked data to its portal.
     
  • Serendipity: Pratt School of Information Science
    Linked Jazz
    Aiming to reveal the relationships between people, organizations and places in the world of jazz with linked data.
     

Potential Projects

  • Chopin Early Editions
  • Targeted Searches for Reference
  • Connections between dissertations
  • Middle Eastern Postcards
  • Archival Photofiles
  • American Environmental Photographs
  • Speculum
  • Manage Authorities (Local)
  • Add linked data to University-produced video
  • Add identifiers to bib records
  • OLE/FOLIO engagement around Linked Data
  • Serendipitous discovery in user interfaces
  • Mint identifiers of local interest and publish

Goals

  • Get our feet wet
  • Start with a small database
  • Ideally clean data
  • Expand what we know to larger dataset

Generate Ideas & Brainstorming

Outline Ideas

  • Idea Name
  • Description
  • Target Audience(s)
  • Responsible Department(s)
  • Technically, how?
  • Benefit / Main Takeaway

Transform Ideas

  • Local Management
  • Producing Data
  • Consuming Data
  • Collaborating
  • Discovering

Prioritize Ideas

Factors for Evaluation

  • Organizational value (match with Library strategy)
  • Technical value (reusability to other projects)
  • Who is willing to commit?
  • Is there already and ecosystem to plug into?
  • How much can we reuse versus must create new?

Results

Voted down to one project: MEPA

  • Small image database
  • Well curated data
  • Flexibility in staging data levels
  • High use collection that is “invisible”
  • Experience to expand to other data objects