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Category: Computing Cultural Heritage in the Cloud

An abstract design of dots and the name of the event LC Collections as Data Concluding Computing Cultural Heritage in the Cloud

Computational Approaches to Library of Congress Collections as Data – Concluding the CCHC initiative

Posted by: Laurie Allen

Please join us as we conclude the Computing Cultural Heritage in the Cloud (CCHC) grant, awarded to the Library of Congress in 2019 by the Mellon Foundation. At the event, we will describe the lessons of the grant, designed to help us investigate a model for enabling discovery, investigation, and visualization of Library materials in …

Why Experiment: Machine Learning at the Library of Congress  

Posted by: Laurie Allen

Why Machine Learning? Everyone at the Library of Congress wants the materials we steward and the services we offer to be useful for as many people as possible. It’s why we do what we do! And across the Library, staff have long relied on technological innovations to enable people to use our materials to become …

digitized scan of historic map of austria-hungary

Check out: The Secret Life of GeoTIFFs

Posted by: Eileen J. Manchester

n October 2022, the outcomes of a 2015 experiment to geo-reference 4,998 digitized maps of the Austro-Hungarian empire were shared with the public at the Computing Cultural Heritage in the Cloud Data Jam. Check out this recent post on World's Revealed, the Library's Geography and Maps blog, to learn more about the resulting GeoTIFF files enable access to digitized historical maps.

A list of seven principles for adopting machine learning derived from LC Labs experimentation, reports, and user feedback.

Grounding iterative experimentation with LC Labs: CCHC and Machine Learning

Posted by: Meghan Ferriter

Across the last five years, LC Labs experiments have integrated sundry perspectives and disciplines to connect people, practice, and history; from making collections more legible and discoverable through volunteer crowdsourcing efforts with Beyond Words and By the People, to developing frameworks for ethically engaging people when adopting machine learning with Humans in the Loop, to …