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Landscape Analysis for the Specimen Data Refinery

Author(s): Stephanie Walton , Laurence Livermore , Olaf Bánki , Robert Cubey , Robyn Drinkwater , Markus Englund , Carole Goble , Quentin Groom , Christopher Kermorvant , Isabel Rey , Celia Santos , Ben Scott , Alan Williams , Zhengzhe Wu

Publication date Created: August 14 2020

Publication date (Electronic): August 14 2020

Journal: Research Ideas and Outcomes

Publisher: Pensoft Publishers

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Abstract

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.

Related collections

Most cited references 34

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Ronald Chamberlain, Eduard Szöcs, Gavin Simpson … (2013)

All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that’s easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.

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Approaches to estimating the universe of natural history collections data

Arturo Ariño (2010)

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A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

Forrest Sheng Bao, Stephen Gang Wu, Eric Xu … (2007)

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Author and article information

Contributors

Stephanie Walton: (View ORCID Profile)

Laurence Livermore: (View ORCID Profile)

Olaf Bánki: (View ORCID Profile)

Robert Cubey: (View ORCID Profile)

Markus Englund: (View ORCID Profile)

Quentin Groom: (View ORCID Profile)

Isabel Rey: (View ORCID Profile)

Ben Scott: (View ORCID Profile)

Journal

Title: Research Ideas and Outcomes

Abbreviated Title: RIO

Publisher: Pensoft Publishers

ISSN (Electronic): 2367-7163

Publication date Created: August 14 2020

Publication date (Electronic): August 14 2020

Volume: 6

Article

DOI: 10.3897/rio.6.e57602

SO-VID: 73dc70e6-4fc3-4064-8eb8-af34de064745

Copyright © © 2020

License:

http://creativecommons.org/licenses/by/4.0/

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