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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Detalles 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
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/239620
Acceso en línea:http://hdl.handle.net/10261/239620
Access Level:acceso abierto
Palabra clave:Machine learning
Natural language processing
Natural history specimens
Data refinery
Data reconciliation
Semantic segmentation
Digitisation
Linked open data
Workflow management
Collections digitisation
Descripción
Sumario: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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