| Summary: | Columbia University Libraries recently embarked on a multi-phase project to migrate nearly 4,000 records describing over 70,000 linear feet of archival material from disparate sources and formats into ArchivesSpace. This paper discusses tools and methods brought to bear in Phase 2 of this project, which required us to look closely at how to integrate a large number of legacy finding aids into the new system and merge descriptive data that had diverged in myriad ways. Using Python, XSLT, and a widely available if underappreciated resource—the Google Sheets API—archival and technical library staff devised ways to efficiently report data from different sources, and present it in an accessible, user-friendly way,. Responses were then fed back into automated data remediation processes to keep the migration project on track and minimize manual intervention. The scripts and processes developed proved very effective, and moreover, show promise well beyond the ArchivesSpace migration. This paper describes the Python/XSLT/Sheets API processes developed and how they opened a path to move beyond CSV-based reporting with flexible, ad-hoc data interfaces easily adaptable to meet a variety of purposes.
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