A cost analysis of transcription systems

Walton, Stephanie; Livermore, Laurence; Dillen, Mathias; Smedt, Sofie De; Groom, Quentin; Koivunen, Anne; Phillips, Sarah

doi:10.3897/rio.6.e56211

Cited by 11 publications

(7 citation statements)

References 19 publications

(4 reference statements)

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“…If appropriate, records can then be uploaded to public repositories. Therefore, the goal of an automated transcription tool is to automate the most time‐consuming parts of the transcription process—typing and validating label content—to produce formatted data (rows in a spreadsheet) that closely matches or exceeds the quality of human transcription (Walton et al, 2020).…”

Section: Llm Capabilitymentioning

confidence: 99%

Herbarium specimen label transcription reimagined with large language models: Capabilities, productivity, and risks

Weaver,

Ruhfel,

Lough

et al. 2023

American J of Botany

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Section: Llm Capabilitymentioning

confidence: 99%

Herbarium specimen label transcription reimagined with large language models: Capabilities, productivity, and risks

Weaver,

Ruhfel,

Lough

et al. 2023

American J of Botany

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“…Much of the data about specimens is stored on their handwritten, typed or printed labels or in registers/ ledgers [33]. Direct manual transcription into local databases with manual georeferencing is the primary method used today to capture this data.…”

Section: Wo Rkflows For Processing Specimen Images and Extracting Datamentioning

confidence: 99%

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

et al. 2022

Self Cite

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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.

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“…In conjunction with this costbook analysis, Walton et al (2020) analysed the cost of transcribing label data in the digitisation process. Aside from RBINS, who reported an average cost per item for transcription of €4.85 PPS, no other institutions have broken out transcription costs as a separate line item.…”

Section: 9 Transcription Costsmentioning

confidence: 99%

Costbook of the digitisation infrastructure of DiSSCo

Hardisty¹,

Livermore²,

Walton³

et al. 2020

RIO

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There has been little work to compare and understand the operating costs of digitisation using a standardised approach. This paper discusses a first attempt at gathering digitisation cost information from multiple institutions and analysing the data. This paper has been written: for other digitisation managers who want to breakdown and compare project costs; as a potential baseline for future digitisation projects; as a starting point for prioritising research and development to reduce digitisation costs.

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A cost analysis of transcription systems

Cited by 11 publications

References 19 publications

Herbarium specimen label transcription reimagined with large language models: Capabilities, productivity, and risks

Herbarium specimen label transcription reimagined with large language models: Capabilities, productivity, and risks

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

Costbook of the digitisation infrastructure of DiSSCo

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