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Managing and accessing web archives: Irish practitioners' perspectives.

Maria RyanDella KeatingJoanna Finegan
Published in: AI & society (2022)
This article provides practitioners' perspectives on preservation of the Irish web space by the National Library of Ireland (the NLI). The context of this work is outlined including the history of Ireland's national library, its role, resources and place in library, archive, cultural and digital preservation networks. The development of the NLI Web Archive is discussed within the wider context of the Library's mission and digital collecting and preservation policies, as well as international approaches to preserving the web. The article looks at how the NLI has developed its selective web archive over the past decade, and has grown the content and access to it as a way to mitigate against the absence of at-scale solutions. The unusual legal context in Ireland regarding legislative barriers to archiving the Irish web space at scale and the NLI's work for over a decade to change this situation are discussed as are the significant implications of the current legal situation for data loss and long-term access to Ireland's contemporary record. Distinctive Irish aspects of digital cultural heritage preserved in the NLI Web Archive collections are highlighted. The opportunities and challenges in developing outreach and access for the Web Archive are considered together with its relationship with the collecting activities of the Library's Born Digital Pilot Projects. This article will also discuss types of usage and user groups in relation to archived Irish web data. Potential for creative and imaginative uses of Irish web archive collections and data are also considered in relation to the Library's broader public learning and outreach programmes.
Keyphrases
  • general practice
  • primary care
  • randomized controlled trial
  • public health
  • big data
  • emergency department
  • risk assessment
  • clinical trial
  • preterm infants
  • data analysis
  • adverse drug
  • gestational age
  • deep learning