Automating Digital Preservation with Archivematica's API and Python

Preserving large datasets by Archivematice becomes increasingly less effective using its web-based interface. In this study, we would like to highlight an automation application tool implemented by us in Python by leveraging the RESTful API coming with Archivematica. The automated workflow encompasses pre-processing of digital objects to ensure consistency and accuracy of digital objects to be preserved, batch uploading, unpacking, checksum verification and/or generation, format identification, normalization, metadata extraction and creation, and creation of Submission Information Packages (SIPs), Archival Information Packages (AIPs) and Dissemination Information Packages (DIPs). It also enable us to monitor the progress, status and resource usages of batch submitted digital preservation jobs, allowing real-time computer resource budgeting and cleaning and job scheduling. This approach was applied to preserve the Sir Samuel Steele Collection curated and digitized by the University of Alberta Library (UAL). It is evident that this automation approach not only enhances operational efficiency but also ensures consistency and accuracy through the reduction of frequent human intervention, which is both time-consuming and error prone.