Everybody in the TARDIS (Teams of Archival Research Data Information Specialists): Collaborative Efforts to Save Historical Data
Bethany Anderson Mikala Narlock Poppy Townsend Sandi Caldrone Sarah Fox Shannon Farrell
Why bring Doctor Who and the TARDIS into a discussion about data in archives? One, because it’s fun, and two, because this is a story about time travel, collaboration, and rescue. For decades, the Doctor has been traveling through time to perform quirky and creative rescue missions. Like historical datasets, each adventure presents unique challenges, but one thing remains constant: the Doctor works best with others, and the same is true of data rescue, which requires expertise in archives, data, and the related subject matter. Discovering usable, historical data in an archives can feel as delightful and magical as the best episodes of Doctor Who. It can also be as tedious and frustrating as the worst episodes, but like all good science fiction, even the worst bits pose illuminating questions.

How do current concepts of data and scholarship compare to historical ones? How do we find data that wasn’t labeled as data? How do we find data that wasn’t *thought of* as data? Once we find archival data, how do we transform it for reuse and analysis? What can historical data teach us about current best practices for documentation and vice versa?  Our research questions and knowledge structures determine what data will be acquired, how it will be arranged and analyzed, and also through what mechanisms it will be interpreted in the archives. Understanding all of this is integral to understanding how the data can be reused.

This panel discussion will bring together archivists, librarians, and data service specialists to share their experiences finding, stewarding, and using data in archives - the successes, frustrations, discoveries, pitfalls, and unanswered questions. Together, we will explore possible solutions to the challenges of bridging data science and archives such as: identifying search terms that might indicate data; inferring missing context about standard research practices at the time data was collected; finding data in unexpected places like annotations and correspondence; working across datasets while preserving document order and fragile materials; extracting data from handwritten narrative text, symbols, and drawings; working through historical challenges like species names that change over time; and, making human-readable data machine-readable.
information management principles from document to data