10
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Archives, linked data and the digital humanities: increasing access to digitised and born-digital archives via the semantic web

      Archival Science
      Springer Science and Business Media LLC

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Mass digitisation and the exponential growth of born-digital archives over the past two decades have resulted in an enormous volume of archives and archival data being available digitally. This has produced a valuable but under-utilised source of large-scale digital data ripe for interrogation by scholars and practitioners in the Digital Humanities. However, current digitisation approaches fall short of the requirements of digital humanists for structured, integrated, interoperable, and interrogable data. Linked Data provides a viable means of producing such data, creating machine-readable archival data suited to analysis using digital humanities research methods. While a growing body of archival scholarship and praxis has explored Linked Data, its potential to open up digitised and born-digital archives to the Digital Humanities is under-examined. This article approaches Archival Linked Data from the perspective of the Digital Humanities, extrapolating from both archival and digital humanities Linked Data scholarship to identify the benefits to digital humanists of the production and provision of access to Archival Linked Data. It will consider some of the current barriers preventing digital humanists from being able to experience the benefits of Archival Linked Data evidenced, and to fully utilise archives which have been made available digitally. The article argues for increased collaboration between the two disciplines, challenges individuals and institutions to engage with Linked Data, and suggests the incorporation of AI and low-barrier tools such as Wikidata into the Linked Data production workflow in order to scale up the production of Archival Linked Data as a means of increasing access to and utilisation of digitised and born-digital archives.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Linked Data - The Story So Far

              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Big Data. A Revolution That Will Transform How We Live, Work and Think

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Archival Science
                Arch Sci
                Springer Science and Business Media LLC
                1389-0166
                1573-7500
                September 2022
                December 27 2021
                September 2022
                : 22
                : 3
                : 319-344
                Article
                10.1007/s10502-021-09381-0
                445e6b5d-bbb3-4774-84bc-a4dde7f1b63b
                © 2022

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

                History

                Comments

                Comment on this article