What are "computational methods" in the context of digital archives?

In the context of digital archives, "computational methods" refer to techniques that leverage computational tools and technologies to analyze, process, and manage digital collections effectively. This can include a variety of applications such as data mining, machine learning, natural language processing, and algorithmic analysis of digital assets, which facilitate better organization, retrieval, and utilization of archived materials.

These methods enable archivists and researchers to handle large volumes of data more efficiently, identify patterns, and extract valuable insights from digital collections that might be cumbersome to tackle through traditional, manual means. By using computational methods, archival institutions can enhance preservation strategies, improve access to records, and innovate in how they present and interpret digital materials for users.

Other options, such as standard practices for physical record-keeping, pertain to traditional archiving methods that do not incorporate digital tools. Strategies for increasing manual data entry accuracy focus on processes rather than technological implementation, while basic online search techniques for metadata do not encompass the broader analytical capabilities that computational methods offer in managing digital archives.