Data Science projects in Marriott Library

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Data Science projects in Marriott Library

When	Thursday, January 23, 2020, 12:15 PM – 1:30 PM (MT)
Where	MEB 3147

Abstract

At research intensive universities, libraries have traditionally supported data science activities in various ways including acquiring datasets that researchers need, hosting workshops and training sessions on data science tools, and offering data support tools for creation of persistent identifiers(dois), etc. At Marriott Library, in addition to supporting data science programs on campus, we have embarked on a suite of data science projects to add value to our culturally-rich collections. Our efforts are focused on enriching our collection data, and making this collection data available for computational use (collections as data [1]) so that developers, scientists, and digital humanists can programmatically interact with the data in myriad ways and undertake projects related to data mining & text analysis, advanced visualizations, and geospatial analysis. In this presentation, I will talk about two specific projects - Utah Digital Newspapers [2] and machine learning meets archives [3] - to highlight these efforts.

Utah Digital Newspapers (UDN): Marriott Library was an early pioneer in digitizing newspapers and making the content available to historians, researchers, and lifelong learners. UDN program has been operating since 2002 and is recognized as one of the leaders in newspaper digitization in the United States. We have continued to partner with universities, colleges, state agencies, county and city libraries, and other agencies to digitize, deliver, and archive historical newspaper collections; As of 2019, UDN has well over 22.5 million newspaper articles and 3.5 million pages in the repository platform. In this presentation, we will talk about the importance of looking at collections as data, our API work with UDN and demonstrate the usefulness of this approach with specific examples.

Machine learning meets archives: Metadata is the bedrock of library archives and Digital Library systems, as it helps in users discovering the unique content in various collections housed in digital libraries. But creating metadata is a time-intensive process. We are working with machine learning algorithms to generate descriptive metadata for digital images. I will share the results of our work, and also lessons learned from working with digital library data.

Speaker

Tags: machine learning

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