Landscape Analysis for the Specimen Data Refinery

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and ar...

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Detalhes bibliográficos
Autores: Walton, Stephanie, Livemore, Laurance, Bánki, Olaf, Cubey, Robert W.N., Drinkwater, Robyn, Englund, Markus, Globe, Carole, Groom, Quentin, Kermorvant, Christopher, Rey Fraile, Isabel, Santos, Celia M., Scott, Ben, Williams, Alan R., Wu, Zhengzhe
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/239620
Acesso em linha:http://hdl.handle.net/10261/239620
Access Level:acceso abierto
Palavra-chave:Machine learning
Natural language processing
Natural history specimens
Data refinery
Data reconciliation
Semantic segmentation
Digitisation
Linked open data
Workflow management
Collections digitisation
Descrição
Resumo:This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens.

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