What does Computational Archival Science primarily focus on?

Computational Archival Science is a field that integrates computational methods and practices into the management and preservation of archives. It emphasizes applying computational thinking to enhance archival processes such as the organization, discovery, and accessibility of digital records. This approach harnesses technologies such as data analysis, machine learning, and automated workflows to innovate and improve how archival materials are managed in a digital context.

The focus on computational methods reflects a shift in the archival profession towards embracing technology to meet the challenges of digital information. This enables archivists to work more effectively with large datasets and to understand the implications of data curation, preservation strategies, and user engagement in the digital age.

In contrast, the other options emphasize aspects of archival practice that do not align with the primary focus of Computational Archival Science. While preserving physical archival materials, developing software, and creating physical storage systems are important components of the broader field of archiving, they do not encapsulate the core idea of integrating computational thinking and approaches that define Computational Archival Science.