A digital ecosystem for the multidisciplinary study of Notre-Dame de Paris.
• The Notre Dame fire highlighted the crucial role of digital data in heritage science, necessitating comprehensive multidisciplinary data interpretation. • State-of-the-art analysis identified gaps in semantics, memory, data correlation, and the balance between technology and human activity. • An i...
| Publicado en: | Journal of Cultural Heritage Vol. 65; pp. 206 - 210 |
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| Formato: | Artículo |
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Elsevier B.V.
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=175500137&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 175500137 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 12962074 KK8 jtl: Journal of Cultural Heritage issn: 12962074 maglogo: N pubinfo: dt: Jan2024 vid: 65 pid: 467 pub: Elsevier B.V. artinfo: ui: 175500137 10.1016/j.culher.2023.09.011 ppf: 206 ppct: 4 formats: tig: atl: A digital ecosystem for the multidisciplinary study of Notre-Dame de Paris. aug: au: De Luca, Livio affil: Modèles et simulations pour l'Architecture et le Patrimoine - UMR 3495 MAP, Centre National de la Recherche Scientifique - Ministere de la Culture, Campus du CNRS (Bâtiment US) - 31, Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France su: Digital technology Mary, Blessed Virgin, Saint Notre-Dame de Paris (Cathedral) Multidimensional databases Computer science Evidence gaps Cultural property Data integration Cathedrals sug: subj: Digital technology Mary, Blessed Virgin, Saint Notre-Dame de Paris (Cathedral) Multidimensional databases Computer science Evidence gaps Cultural property Data integration Cathedrals keyword: Computational modelling Digital ecosystem Digitisation Heritage science ab: • The Notre Dame fire highlighted the crucial role of digital data in heritage science, necessitating comprehensive multidisciplinary data interpretation. • State-of-the-art analysis identified gaps in semantics, memory, data correlation, and the balance between technology and human activity. • An initiative was launched to establish a digital ecosystem that represents both the tangible object and the evolving knowledge surrounding it. • The initiative integrates digital humanities, heritage science, and computer science, placing emphasis on an ecosystem tailored for human-led data collection and multi-dimensional data integration. • The Digital Data Working Group tackled challenges in digitisation and modelling, with a primary focus on studies of the wooden roof and collapsed vaults. The 2019 fire of Notre-Dame de Paris was not only a significant event in its history, but also provided a unique platform for multidisciplinary studies and digital data analysis. At the initiative of the CNRS and the Ministry of Culture, a large-scale collaboration is currently underway to manage and analyse the extensive data generated in connection with the restoration of the cathedral. The role of digital data is crucial for understanding complex multidisciplinary studies, especially in the field of cultural heritage. A digital data working group, composed of thirty scientists from 12 laboratories, is focused on documenting and organising this collective experience to serve both the immediate needs of restoration and the long-term goals of cultural heritage research. Key challenges include data traceability, knowledge modelling, multidimensional analysis and the need to address gaps in semantic understanding, memory, data correlation and technology. To address these challenges, a novel method is proposed that combines the digital representation of material heritage with the evolving knowledge about it. By collaborating with the wider scholarly community involved in Notre Dame's scientific action, the aim is to introduce new methods in heritage research and bridge the gap between the humanities and digitally-driven scholarship. This initiative also intersects with two primary scientific vectors: the technological approach to data collection and the generation and analysis of semantically enriched datasets. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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