Navigating Artificial Intelligence for Cultural Heritage Organisations
The question of how artificial intelligence and machine learning should be applied to data in libraries and other cultural institutions is a challenge shared by heritage professionals, computer scientists and digital humanities scholars. As the number of digitised and born-digital records grows...
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| Format: | Online |
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| Jezik: | engleski |
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UCL Press
2025
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| Teme: | |
| Online pristup: | https://library.oapen.org/handle/20.500.12657/103431 |
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| _version_ | 1869517758871371776 |
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| collection | Directory of Open Access Books |
| description | The question of how artificial intelligence and machine learning should be applied to data in libraries and other cultural institutions is a challenge shared by heritage professionals, computer scientists and digital humanities scholars.
As the number of digitised and born-digital records grows, archival practices are looking to automated systems to manage workloads and make cultural records more accessible. AI is playing a crucial role in data management systems within the cultural heritage sector, and information professionals are looking for ways to navigate current challenges and opportunities. Additionally, sector professionals and scholars are benefiting from the many new affordances and innovative research questions offered by using large-scale digital collections as data.
Navigating Artificial Intelligence for Cultural Heritage Organisations explores the innovative technologies and approaches to digitised and born-digital records within libraries and archives across the UK and US, and beyond. It brings together chapters from experts across the fields of digital humanities, computer science and information science, alongside professionals within the library and archival sector. The authors explore technologies being applied to digitised and born-digital records within libraries, archives and other heritage organisations, including innovative approaches in computer vision, Chat GPT, and user experience. The volume has been designed to reflect current and state-of-the-art technologies and innovations for the preservation and accessibility of digitised and born-digital records, to help navigate the future of AI for cultural heritage organisations. |
| format | Online |
| id | doab-20.500.12854ir-161227 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | UCL Press |
| publisherStr | UCL Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1612272025-06-12T05:01:06Z Navigating Artificial Intelligence for Cultural Heritage Organisations Jaillant, Lise Warwick, Claire Gooding, Paul Aske, Katherine Layne-Worthey, Glen Downie, J. Stephen artificial intelligence;cultural heritage;archives;machine learning;computer vision;digital humanities;digital technologies;preservation;accessibility;automated systems thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLZ Museology and heritage studies thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLC Library, archive and information management The question of how artificial intelligence and machine learning should be applied to data in libraries and other cultural institutions is a challenge shared by heritage professionals, computer scientists and digital humanities scholars. As the number of digitised and born-digital records grows, archival practices are looking to automated systems to manage workloads and make cultural records more accessible. AI is playing a crucial role in data management systems within the cultural heritage sector, and information professionals are looking for ways to navigate current challenges and opportunities. Additionally, sector professionals and scholars are benefiting from the many new affordances and innovative research questions offered by using large-scale digital collections as data. Navigating Artificial Intelligence for Cultural Heritage Organisations explores the innovative technologies and approaches to digitised and born-digital records within libraries and archives across the UK and US, and beyond. It brings together chapters from experts across the fields of digital humanities, computer science and information science, alongside professionals within the library and archival sector. The authors explore technologies being applied to digitised and born-digital records within libraries, archives and other heritage organisations, including innovative approaches in computer vision, Chat GPT, and user experience. The volume has been designed to reflect current and state-of-the-art technologies and innovations for the preservation and accessibility of digitised and born-digital records, to help navigate the future of AI for cultural heritage organisations. 2025-06-12T05:01:05Z 2025-06-12T05:01:05Z 2025-06-11T12:14:47Z 2025 book https://library.oapen.org/handle/20.500.12657/103431 9781800088351 9781800088368 9781800088382 https://directory.doabooks.org/handle/20.500.12854/161227 eng open access image/jpeg Attribution-NonCommercial 4.0 International https://library.oapen.org/bitstream/20.500.12657/103431/1/9781800088375.pdf UCL Press 10.14324/111. 9781800088375 10.14324/111. 9781800088375 29b9f0a3-1b0d-4bdd-99d7-b4d3432d7fcc 9781800088351 9781800088368 9781800088382 267 London open access |
| spellingShingle | artificial intelligence;cultural heritage;archives;machine learning;computer vision;digital humanities;digital technologies;preservation;accessibility;automated systems thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLZ Museology and heritage studies thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLC Library, archive and information management Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title | Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title_full | Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title_fullStr | Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title_full_unstemmed | Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title_short | Navigating Artificial Intelligence for Cultural Heritage Organisations |
| title_sort | navigating artificial intelligence for cultural heritage organisations |
| topic | artificial intelligence;cultural heritage;archives;machine learning;computer vision;digital humanities;digital technologies;preservation;accessibility;automated systems thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLZ Museology and heritage studies thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLC Library, archive and information management |
| topic_facet | artificial intelligence;cultural heritage;archives;machine learning;computer vision;digital humanities;digital technologies;preservation;accessibility;automated systems thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLZ Museology and heritage studies thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLC Library, archive and information management |
| url | https://library.oapen.org/handle/20.500.12657/103431 |