Laurence Livermore

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About Laurence Livermore:

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SEEK ID: https://workflowhub.eu/people/219

Location: United Kingdom

ORCID: https://orcid.org/0000-0002-7341-1842

Joined: 8th Dec 2021

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DISSCo - Distributed System of Scientific Collections

The Distributed System of Scientific Collections is a new world-class Research Infrastructure (RI) for Natural Science Collections. The DiSSCo RI aims to create a new business model for one European collection that digitally unifies all European natural science assets under common access, curation, policies and practices that ensure that all the data is easily Findable, Accessible, Interoperable and Reusable (FAIR principles).

DiSSCo represents the largest ever formal agreement between natural ...

Teams: Specimen Data Refinery

Web page: https://www.dissco.eu/

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Specimen Data Refinery

The SDR is concerned with digitisation pipelines for digital access to natural history collections

The SDR integrate machine learning, Artificial Intelligence, and human approaches to extract, enhance, and annotate data from digital images and records at scale. Many collections-holding institutions still need to digitise the bulk of their collections. Digitisation takes time and resources. One of the major challenges in digitising massive collections is finding ways of ensuring high-quality ...

Space: DISSCo - Distributed System of Scientific Collections

Public web page: https://www.synthesys.info/

Start date: 1st Feb 2019

End date: 31st Dec 2023

Organisms: Not specified

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The Natural History Museum London

Country: United Kingdom

City: London

Web page: https://www.nhm.ac.uk/

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The Specimen Data Refinery: A Canonical Workflow Framework and FAIR Digital Object Approach to Speeding up Digital Mobilisation of Natural History Collections

Specimen Data Refinery, FAIR Computational Workflows

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A key limiting factor in organising and using information from physical specimens curated in natural science collections is making that information computable, with institutional digitization tending … to focus more on imaging the specimens themselves than on efficiently capturing computable data about them. Label data are traditionally manually transcribed today with high cost and low throughput, rendering such a task constrained for many collection-holding institutions at current funding levels. We show how computer vision, optical character recognition, handwriting recognition, named entity recognition and language translation technologies can be implemented into canonical workflow component libraries with findable, accessible, interoperable, and reusable (FAIR) characteristics. These libraries are being developed in a cloud- based workflow platform—the ‘Specimen Data Refinery’ (SDR)—founded on Galaxy workflow engine, Common Workflow Language, Research Object Crates (RO-Crate) and WorkflowHub technologies. The SDR can be applied to specimens’ labels and other artefacts, offering the prospect of greatly accelerated and more accurate data capture in computable form. Two kinds of FAIR Digital Objects (FDO) are created by packaging outputs of SDR workflows and workflow components as digital objects with metadata, a persistent identifier, and a specific type definition. The first kind of FDO are computable Digital Specimen (DS) objects that can be consumed/produced by workflows, and other applications. A single DS is the input data structure submitted to a workflow that is modified by each workflow component in turn to produce a refined DS at the end. The Specimen Data Refinery provides a library of such components that can be used individually, or in series. To cofunction, each library component describes the fields it requires from the DS and the fields it will in turn populate or enrich. The second kind of FDO, RO-Crates gather and archive the diverse set of digital and real-world resources, configurations, and actions (the provenance) contributing to a unit of research work, allowing that work to be faithfully recorded and reproduced. Here we describe the Specimen Data Refinery with its motivating requirements, focusing on what is essential in the creation of canonical workflow component libraries and its conformance with the requirements of an emerging FDO Core Specification being developed by the FDO Forum.

Authors: Alex Hardisty, Paul Brack, Carole Goble, Laurence Livermore, Ben Scott, Quentin Groom, Stuart Owen, Stian Soiland-Reyes

Date Published: 7th Mar 2022

Publication Type: Journal

DOI: 10.1162/dint_a_00134

Citation: Data Intelligence:1-19

Created: 25th Apr 2022 at 11:45, Last updated: 16th Jan 2023 at 13:34

Landscape Analysis for the Specimen Data Refinery

Specimen Data Refinery

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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 …

Authors: Stephanie Walton, Laurence Livermore, Olaf Bánki, Robert W. N. Cubey, Robyn Drinkwater, Markus Englund, Carole Goble, Quentin Groom, Christopher Kermorvant, Isabel Rey, Celia M Santos, Ben Scott, Alan Williams, Zhengzhe Wu

Date Published: 14th Aug 2020

Publication Type: Journal

DOI: 10.3897/rio.6.e57602

Citation: Walton S, Livermore L, Bánki O, Cubey RWN, Drinkwater R, Englund M, Goble C, Groom Q, Kermorvant C, Rey I, Santos CM, Scott B, Williams AR, Wu Z (2020) Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes 6: e57602. https://doi.org/10.3897/rio.6.e57602

Created: 8th Dec 2021 at 16:58, Last updated: 16th Jan 2023 at 13:34

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