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  5. Envisaging a global infrastructure to exploit the potential of digitised collections.

Envisaging a global infrastructure to exploit the potential of digitised collections.

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Resource type
Journal article
Creator (person)
Groom, Quentin
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Dillen, Mathias
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Addink, Wouter
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Ariño, Arturo H.
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Bölling, Christian
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Date published
November 30, 2023
Abstract

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

Organisational unit
Science
Project(s)
Priority 4: Accelerated Taxonomy
Funder
Funder nameISNIAwards
European Cooperation in Science and Technology, Belgium
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Mobilise Action CA17106 on Mobilising Data, Experts and Policies in Scientific Collections
Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro, Portugal / Fundação para a Ciência e a Tecnologia, Portugal / Ministério da Ciência, Tecnologia e Ensino Superior, Portugal
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CESAM - FCT/MCTES UIDB/50017/2020+UIDP/50017/2020
Fundação para a Ciência e a Tecnologia, Portugal / Ministério da Ciência, Tecnologia e Ensino Superior, Portugal
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Ci2 - FCT/MCTES UIDP/05567/2020
National Science Foundation, United States
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DBI 2027654
Fonds Wetenschappelijk Onderzoek, Begium
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Grant no. FWO I001721N
Horizon 2020, European Union
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BiCIKL (grant agreement No 101007492) - SYNTHESYS+ (grant agreement No 823827)
Journal title
Biodiversity Data Journal
Volume
11
Article number
e109439
Publisher
Pensoft Publishers
Place of publication
Sofia, Bulgaria
ISSN
1314-2836
eISSN
1314-2828
Date accepted
October 24, 2023
Official URL
https://doi.org/10.3897/bdj.11.e109439
Related URL
https://bdj.pensoft.net/article/109439/
Rights statement
In Copyright
Licence
CC BY 4.0
DOI
10.3897/bdj.11.e109439
Alternate identifier
e109439
Keywords
Specimens
Digitized collections
Species identification
Functional traits
Machine learning
Computer vision
Biodiversity
Additional information
IF = 1.55 (2022-2023)
Managed by the British Library and supported by the AHRC

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