摘要: 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#2;
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.
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期刊:
DATA INTELLIGENCE
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分类:
计算机科学
>>
计算机科学的集成理论
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引用:
ChinaXiv:202211.00437
(或此版本
ChinaXiv:202211.00437V1)
DOI:10.1162/dint_a_00134
CSTR:32003.36.ChinaXiv.202211.00437.V1
- 推荐引用方式:
Alex, Hardisty,Paul, Brack,Carole, Goble,Laurence, Livermore,Ben, Scott,Quentin, Groom,Stuart, Owen, Stian, Soiland-Reyes.(2022).The Specimen Data Refinery: A Canonical Workflow Framework and FAIR Digital Object Approach to Speeding up Digital Mobilisation of Natural History Collections.数据智能(英文).doi:10.1162/dint_a_00134
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