Abstract/Résumé/Resumen
Objective: This study aims to explore how computational archival science has been addressed in peer-reviewed publications, analyzing the integration of digital technologies in the management of digital archival collections and identifying the main trends, tools, and challenges associated. Methodology: A systematic literature review was conducted using the PRISMA protocol, with searches in the Web of Science, Scopus, Emerald, LISTA, Science Direct, and Springer Link databases. After a screening process, including title, abstract, and full-text analysis, 18 articles were selected. Bibliometric analysis were performed using VOSviewer software, mapping keyword co-occurrences and co-authorship relationships. Results: The review highlighted that computational archival science is a growing interdisciplinary field characterized by the use of artificial intelligence, machine learning, natural language processing, and data mining to manage and preserve digital records on a large scale. It identified the increasing demand for trained professionals and the need to integrate these technologies into educational curricula. Additionally, it was observed that the United States and the United Kingdom lead publications in this area. Conclusions: Computational archival science represents a necessary evolution to address the challenges of the digital environment, promoting accessibility and efficient processing of large volumes of data. The integration between archival science and computing enables new possibilities for exploring and using digital collections, expanding the impact of archival science in the digital age. Future research could focus on developing specific tools and further epistemological depth within the
field.
