Envisaging a global infrastructure to exploit the potential of digitised collections
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.26/48471 |
Resumo: | 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. |
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Envisaging a global infrastructure to exploit the potential of digitised collectionsmachine learningfunctional traitsspecies identificationbiodiversity, specimenscomputer visionTens 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.This work was supported by European Cooperation in Science and Technology (COST) as part of the Mobilise Action CA17106 on Mobilising Data, Experts and Policies in Scientific Collections. Heliana Teixeira was supported by CESAM - FCT/MCTES UIDB/50017/2020+UIDP/50017/2020. Renato Panda was supported by Ci2 - FCT/MCTES UIDP/05567/2020. Elizabeth Ellwood is supported by the National Science Foundation (DBI 2027654). This work was also facilitated by the Research Foundation – Flanders research infrastructure under grant number FWO I001721N, the BiCIKL (grant agreement No 101007492) and SYNTHESYS+ (grant agreement No 823827) projects of the European Union’s Horizon 2020 Research and Innovation action.Pensoft PublishersRepositório ComumGroom, QuentinDillen, MathiasAddink, WouterAriño, Arturo H.Bölling, ChristianBonnet, PierreCecchi, LorenzoEllwood, Elizabeth R.Figueira, RuiGagnier, Pierre-YvesGrace, OlwenGüntsch, AntonHardy, HelenHuybrechts, PieterHyam, RogerJoly, AlexisKommineni, Vamsi KrishnaLarridon, IsabelLivermore, LaurenceLopes, Ricardo JorgeMeeus, SofieMiller, JeremyMilleville, KenzoPanda, RenatoPignal, MarcPoelen, JorritRistevski, BlagojRobertson, TimRufino, Ana C.Santos, JoaquimSchermer, MaartenScott, BenSeltmann, KatjaTeixeira, HelianaTrekels, MaartenGaikwad, Jitendra2023-12-13T13:20:37Z2023-11-302023-12-13T13:10:22Z2023-11-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/48471eng1314-2828cv-prod-343102610.3897/bdj.11.e109439info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-14T14:26:42Zoai:comum.rcaap.pt:10400.26/48471Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:42:29.743725Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Envisaging a global infrastructure to exploit the potential of digitised collections |
title |
Envisaging a global infrastructure to exploit the potential of digitised collections |
spellingShingle |
Envisaging a global infrastructure to exploit the potential of digitised collections Groom, Quentin machine learning functional traits species identification biodiversity, specimens computer vision |
title_short |
Envisaging a global infrastructure to exploit the potential of digitised collections |
title_full |
Envisaging a global infrastructure to exploit the potential of digitised collections |
title_fullStr |
Envisaging a global infrastructure to exploit the potential of digitised collections |
title_full_unstemmed |
Envisaging a global infrastructure to exploit the potential of digitised collections |
title_sort |
Envisaging a global infrastructure to exploit the potential of digitised collections |
author |
Groom, Quentin |
author_facet |
Groom, Quentin Dillen, Mathias Addink, Wouter Ariño, Arturo H. Bölling, Christian Bonnet, Pierre Cecchi, Lorenzo Ellwood, Elizabeth R. Figueira, Rui Gagnier, Pierre-Yves Grace, Olwen Güntsch, Anton Hardy, Helen Huybrechts, Pieter Hyam, Roger Joly, Alexis Kommineni, Vamsi Krishna Larridon, Isabel Livermore, Laurence Lopes, Ricardo Jorge Meeus, Sofie Miller, Jeremy Milleville, Kenzo Panda, Renato Pignal, Marc Poelen, Jorrit Ristevski, Blagoj Robertson, Tim Rufino, Ana C. Santos, Joaquim Schermer, Maarten Scott, Ben Seltmann, Katja Teixeira, Heliana Trekels, Maarten Gaikwad, Jitendra |
author_role |
author |
author2 |
Dillen, Mathias Addink, Wouter Ariño, Arturo H. Bölling, Christian Bonnet, Pierre Cecchi, Lorenzo Ellwood, Elizabeth R. Figueira, Rui Gagnier, Pierre-Yves Grace, Olwen Güntsch, Anton Hardy, Helen Huybrechts, Pieter Hyam, Roger Joly, Alexis Kommineni, Vamsi Krishna Larridon, Isabel Livermore, Laurence Lopes, Ricardo Jorge Meeus, Sofie Miller, Jeremy Milleville, Kenzo Panda, Renato Pignal, Marc Poelen, Jorrit Ristevski, Blagoj Robertson, Tim Rufino, Ana C. Santos, Joaquim Schermer, Maarten Scott, Ben Seltmann, Katja Teixeira, Heliana Trekels, Maarten Gaikwad, Jitendra |
author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Groom, Quentin Dillen, Mathias Addink, Wouter Ariño, Arturo H. Bölling, Christian Bonnet, Pierre Cecchi, Lorenzo Ellwood, Elizabeth R. Figueira, Rui Gagnier, Pierre-Yves Grace, Olwen Güntsch, Anton Hardy, Helen Huybrechts, Pieter Hyam, Roger Joly, Alexis Kommineni, Vamsi Krishna Larridon, Isabel Livermore, Laurence Lopes, Ricardo Jorge Meeus, Sofie Miller, Jeremy Milleville, Kenzo Panda, Renato Pignal, Marc Poelen, Jorrit Ristevski, Blagoj Robertson, Tim Rufino, Ana C. Santos, Joaquim Schermer, Maarten Scott, Ben Seltmann, Katja Teixeira, Heliana Trekels, Maarten Gaikwad, Jitendra |
dc.subject.por.fl_str_mv |
machine learning functional traits species identification biodiversity, specimens computer vision |
topic |
machine learning functional traits species identification biodiversity, specimens computer vision |
description |
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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-13T13:20:37Z 2023-11-30 2023-12-13T13:10:22Z 2023-11-30T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.26/48471 |
url |
http://hdl.handle.net/10400.26/48471 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1314-2828 cv-prod-3431026 10.3897/bdj.11.e109439 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Pensoft Publishers |
publisher.none.fl_str_mv |
Pensoft Publishers |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136325751799808 |