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Innovator in Residence Program

The Application Period is closed for the FY21 Innovator in Residence. Subscribe to the Labs Newsletter, follow us on Twitter @LC_Labs External, or check this page to receive updates on selected Innovators and the next application period.

About the Program

We've established a broad Innovator-in-Residence Program to support innovative and creative uses of our collections that showcase how the Library relates to and enriches the work, life, and imagination of the American people.

This competitive program seeks services of artists, journalists, researchers, teachers, and others willing to imagine and prototype examples of creative, innovative, and novel uses of Library of Congress digital collections in an artwork, visualization, application, or other publicly available tool, service, or exhibit.

It is anticipated that to produce these examples a variety of specialized and advanced skills will be required; some of these skills include data visualization, computer programming, data management, and others that are gained only after achieving a high degree of expertise and training. These skills must also be combined with demonstrated ability and experience working and communicating in a professional setting for multiple stakeholders. The kinds of services we are seeking from this Innovator-in-Residence do not yet exist at the Library of Congress. The residency should result in one-of-a-kind projects, artworks, and other unique and innovative design features that are not obtainable from existing or other professional or technical services.

To see the full announcement for FY20 and FY21, visit the contract posting External

Prior Innovators

  • FY18-19 Jer Thorp - applied the idea of serendipity to the scale of LC collections through the podcast "Artist in the Archive" and a suite of applications
  • FY20 Brian Foo - created the application Citizen Dj to enable the public to discover and create from LC free to use sound collections.
  • FY20 Benjamin Charles Lee - created a way for users to explore visual content from historic newspapers in the Chronicling America collection using machine learning.
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