AEOLIAN (Artificial intelligence for cultural organisations)
AEOLIAN (Artificial intelligence for cultural organisations)
批准号:
AH/V009443/1
负责人:
Lise Jaillant
金额:
$7.46万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --
中文摘要
我们如何打开对公众关闭的“黑暗”数字档案?人工智能(AI)在使大西洋两岸的用户更容易获得数字化和天生数字化的文化记录方面发挥了什么作用?AEOLIAN(面向文化组织的人工智能)专注于目前由于隐私问题、版权和其他问题而对研究人员和其他用户关闭的原生数字和数字化收藏。档案是用来利用的,而不是锁起来的。为了解锁文化资产,我们需要跨学科合作,利用最新技术。AEOLIAN汇集了数字人文主义者、计算机科学家、档案工作者和其他利益相关者,以改变目前隐藏的天生数字化和数字化馆藏的获取和使用方式。分析大量数据不能手工完成:自动化不再是一种选择,而是一种必要。人工智能可以通过敏感性审查来改善对非机密材料的访问,例如通过区分个人和商业电子邮件。AEOLIAN旨在解锁天生的数字和数字化收藏,并向大量用户开放。访问数字档案是必不可少的,但我们也需要预见到,有一天,原生数字记录将更容易访问。为了理解这些大量的数据,迫切需要新的方法,将传统的人文学科方法与数据丰富的方法相结合。因此,人文学者、计算机科学家、档案工作者和其他利益相关者之间的合作对于使档案更容易获取,以及设计分析大量数据的新方法至关重要。人工智能和机器学习为图书馆、档案馆和博物馆创造了机遇,但也带来了挑战。该项目将解决人文学科中更大的问题,包括当前关于人工智能和数字技术的辩论中心的伦理和社会考虑。AEOLIAN项目将产生以下研究成果:6个在线研讨会,这将导致创建一个理论家和实践者的国际网络,他们将使用天生的数字和数字化档案。5个美国和英国文化组织的案例研究。这些案例研究将形成一份100页的报告,供跨学科读者开放获取,概述未来研究的途径。2作为期刊专刊或编辑文集出版的文集。最终报告将基于对英国和美国特定藏品的5个案例研究和详细的访谈,提供一份天生数字化和数字化文化资产的路线图。至关重要的是,它还将为跨学科研究领域发展具体的想法,以解决获取数字文化资产的问题,这可能成为未来研究计划的基础。档案当然不是学术研究人员的专利。在线研讨会和网站将促进公众参与二十一世纪档案收藏性质的变化(从印刷到数字)这一主题。该网站将以所有研讨会参与者的演示材料、研讨会演示录像和案例研究的形式跟踪所有项目活动,然后将其纳入最终报告。相关的社交媒体将帮助我们与学术界、档案机构等感兴趣的各方建立联系。
英文摘要
How can we unlock "dark" digital archives closed to the public? What is the role of Artificial Intelligence (AI) in making digitised and born-digital cultural records more accessible to users, on both sides of the Atlantic? AEOLIAN (Artificial intelligence for cultural organisations) focuses on born-digital and digitised collections that are currently closed to researchers and other users due to privacy concerns, copyright and other issues. Archives are meant to be used, not locked away. In order to unlock cultural assets, we need to work across disciplines and harness the latest technology. AEOLIAN brings together Digital Humanists, Computer Scientists, archivists and other stakeholders to transform the access and use of born-digital and digitised collections which are currently hidden away. Analysing vast amounts of data cannot be done manually: automation is no longer a choice, it is a necessity. Artificial Intelligence can be used to improve access to non-confidential materials through sensitivity review, for example by distinguishing between personal and business emails. AEOLIAN aims to unlock born-digital and digitised collections and open them up to a large number of users. Access to digital archives is essential, but we also need to anticipate the moment when born-digital records will be more accessible. To make sense of this mass of data, new methodologies are urgently needed, combining traditional methods in the humanities with data-rich approaches. Collaborations between humanities scholars, computer scientists, archivists and other stakeholders are therefore essential to make archives more accessible, but also to design new methodologies to analyse huge amounts of data.AI and machine learning create opportunities, but also challenges, for libraries, archives and museums. The project will address larger questions in the humanities - including ethical and social considerations at the centre of current debates on AI and digital technologies.The AEOLIAN project will lead to the following research outputs:_6 online workshops , which will result in the creation of an international network of theorists and practitioners working with born-digital and digitised archives. _5 case studies of US and UK cultural organisations . These case studies will feed into an open-access 100-page report for an interdisciplinary audience outlining avenues for future research._2 collections of essays published as special issue of journal or edited collection. The final report will offer a roadmap on born-digital and digitised cultural assets, based on 5 case studies of specific collections in the UK and US and detailed interviews. Crucially, it will also develop specific ideas for interdisciplinary research areas to solve the issue of access to digital cultural assets, which could form the basis of future research initiatives.Archives are of course not reserved to academic researchers. The online workshops and the website will foster public engagement on the topic of the changing nature of archival collections (from print to digital) in the twenty-first century.The website will keep track of all the project activities in the form of presentation materials from all workshop participants, video recordings of workshop presentations, and case studies that will then feed into the final report. Associated social media will help us connect with interested parties - in academia, archival institutions and beyond.
期刊论文(10)
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会议论文
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DOI:
10.1145/3594727
发表时间:
2023-04
期刊:
ACM Journal on Computing and Cultural Heritage
影响因子:
2.4
作者:
[Gustavo Candela;J. Pereda;Dolores Sáez;Pilar Escobar;Alexander Sánchez;Andrés Villa Torres;Albert A. Palacios;Kelly S. Mcdonough;Patricia Murrieta-Flores]
通讯作者:
Gustavo Candela;J. Pereda;Dolores Sáez;Pilar Escobar;Alexander Sánchez;Andrés Villa Torres;Albert A. Palacios;Kelly S. Mcdonough;Patricia Murrieta-Flores
(Mis)Matching Metadata: Improving Accessibility in Digital Visual Archives through the EyCon Project
DOI:
10.1145/3594726
发表时间:
2023-05
期刊:
ACM Journal on Computing and Cultural Heritage
影响因子:
2.4
作者:
[Katherine Aske;Marina Giardinetti]
通讯作者:
Katherine Aske;Marina Giardinetti
DOI:
10.1108/jd-01-2022-0029
发表时间:
2023-02
期刊:
Journal of Documentation
影响因子:
2.1
作者:
[Dilawar Ali;Kenzo Milleville;S. Verstockt;N. van de Weghe;Sally Chambers;Julie M. Birkholz]
通讯作者:
Dilawar Ali;Kenzo Milleville;S. Verstockt;N. van de Weghe;Sally Chambers;Julie M. Birkholz
The Library Catalogue as Dataset: Exploring Data Science Approaches to Analyse Collections at Scale
图书馆目录作为数据集:探索大规模分析馆藏的数据科学方法
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[Havens L]
通讯作者:
Havens L
Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts
定位 Paradata:档案和记录保存环境中人工智能流程文档的概念框架
DOI:
10.1145/3594728
发表时间:
2023
期刊:
Journal on Computing and Cultural Heritage
影响因子:
--
作者:
[Cameron S]
通讯作者:
Cameron S
共 9 条
Unlocking our Digital Past with Artificial Intelligence (LUSTRE)
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批准号:AH/X003132/1
-
项目类别:Research Grant
-
资助金额:$10.28万
-
财政年份:2022
-
负责人:Lise Jaillant
-
依托单位:
EyCon (Visual AI and Early Conflict Photography)
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批准号:AH/W008408/1
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项目类别:Research Grant
-
资助金额:$12.88万
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财政年份:2022
-
负责人:Lise Jaillant
-
依托单位:
AURA (Archives in the UK/ Republic of Ireland & AI): Bringing together Digital Humanists, Computer Scientists & stakeholders to unlock cultural assets
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批准号:AH/V002341/1
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项目类别:Research Grant
-
资助金额:$3.08万
-
财政年份:2020
-
负责人:Lise Jaillant
-
依托单位:
Survival of the Weakest: Preserving and Analysing Born-Digital Records to Understand How Small Poetry Publishers Survive in the Global Marketplace
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批准号:AH/R00773X/1
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项目类别:Fellowship
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资助金额:$24.84万
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财政年份:2018
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负责人:Lise Jaillant
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依托单位:
海外基金