Landscape Analysis for the Specimen Data Refinery

Walton, Stephanie; Livermore, Laurence; Bánki, Olaf; Cubey, Robert; Drinkwater, Robyn; Englund, Markus; Goble, Carole; Groom, Quentin; Kermorvant, Christopher; Madeira, Isabel Rey; Santos, Celia; Scott, Ben; Williams, Alan; Wu, Zhipeng

doi:10.3897/rio.6.e57602

Cited by 21 publications

(11 citation statements)

References 43 publications

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“…This Level 1 workflow provenance [68] can be expressed generally across workflow languages with minimal workflow engine changes, with the option of more detailed provenance traces as separate PROV artefacts in the RO-Crate as data entities. In the current development of Specimen Data Refinery 59 [122] these RO-Crates will document the text recognition workflow runs of digitised biological specimens, exposed as FAIR Digital Objects [38].…”

Section: Profile For Describing Workflowsmentioning

confidence: 99%

Packaging research artefacts with RO-Crate

Soiland‐Reyes

Sefton

Crosas

et al. 2022

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An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying “just enough” Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article11 https://w3id.org/ro/doi/10.5281/zenodo.5146227 is archived at https://doi.org/10.5281/zenodo.5146227.

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Section: Profile For Describing Workflowsmentioning

confidence: 99%

Packaging research artefacts with RO-Crate

Soiland‐Reyes

Sefton

Crosas

et al. 2022

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“…Tools of particular interest span the fields of computer vision, optical character recognition, handwriting recognition, named entity recognition and language translation. Workflow technologies from the ELIXIR Research Infrastructure [19], including Galaxy [20], Common Workflow Language [21], Research Object Crates (RO-Crates) [22,23] and WorkflowHub [24], and selected tools are integrated in a cloud-based workflow platform for natural history specimens-the 'Specimen Data Refinery' [1] that will become one of the main services to be offered by the planned DiSSCo research infrastructure [5]. The tools themselves, implemented with findable, accessible, interoperable, and reusable (FAIR) characteristics [25] are packaged into canonical workflow component libraries [26], rendering them reusable, and interoperable with one another.…”

Section: The Specimen Data Refi Nery: a Canonical Workfl Ow Framework...mentioning

confidence: 99%

“…While natural history collections are heterogeneous in size and shape, often they are mass digitized using standardised workflows [9,10,11,12,13]. In pursuit of higher throughput at lower cost, yet with higher accuracy and richer metadata, further automation will increasingly rely on techniques of object detection and segmentation, optical character recognition (OCR) and semantic processing of labels, and automated taxonomic identification and visual feature analysis [1,18].…”

Section: Wo Rkflows For Processing Specimen Images and Extracting Datamentioning

confidence: 99%

“…A key limiting factor in organising and using information from physical specimens curated in natural history collections is making that information computable ('machine-actionable') and extendable. More than 85% of available information currently resides on labels attached to specimens or in physical ledgers [1]. Label data are commonly transcribed manually with high cost and low throughput, rendering such a task constraining for many institutions at current funding levels.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2022

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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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“…The terms robotics, robot and automation are also now frequently applied to process automation using software, without any mechanical or physical components. Software automation is outside the scope of this paper, as it is addressed in related reports including the ICEDIG report on automated text digitisation (Owen et al 2020), and the SYNTHESYS+ Specimen Data Refinery (Walton et al 2020). Software automation is already in use across many aspects of digitisation, including image processing, batch quality control, barcode detection and image segmentation (Allan et al 2019, Hudson et al 2015.…”

Section: Definitions and Scopementioning

confidence: 99%

Research and development in robotics with potential to automate handling of biological collections

Hardy¹,

Walsum²,

Livermore³

et al. 2020

RIO

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This report investigates the current state of physical (mechanical) robotics, automated warehousing approaches and assistive technologies in relation to the storage, handling and processing (particularly digitisation) of natural history collections. Robotics can sound futuristic, however we provide case studies that show many and growing examples of physical automation in the natural history and cultural heritage sectors, including barcodes and conveyor belts for digitisation; robots that handle multiple vials for molecular and genetic work; robots for use in in display or exhibition contexts; and automated warehousing of library collections. We provide a non-exhaustive example of an end to end workflow of storage, retrieval and processing and discuss aspects of the tools and challenges relevant to these stages. The Distributed System of Scientific Collections (DiSSCo), a new Research Infrastructure for natural science collections, should build on this, leading a future programme of pilots that develop understanding of independent stages, and can be connected to make progress towards end-to-end solutions. Robots, or automated systems, excel at repetitive tasks, and are developing rapidly to be able to handle more complex object types, at lower cost. High volume, high variety of objects, and considerations such as fragility are not unique to the natural history sector - they apply for example to major retail operations - however natural history collections do offer some of the more extreme examples of these challenges, and in particular are not replaceable. Increased consistency of storage units is likely to be a critical factor in enabling automated handling in future, as well as looking at automation possibilities when new collections storage spaces are developed and built. Engagement with industry and subject matter experts has been patchy and again we recommend that DiSSCo help to ensure a joined up engagement with the right incentives in place, and with clear communication of requirements and challenges for shared R&D. When examining return on investment for particular automation, collections-holding institutions need to consider not only time and cost of automation compared to human labour, but wider factors including: health and safety such as physical environment and repetitve strain injury; security; quality and consistency of outputs; degree of criticality in response times (e.g. if digitising on demand); effective use of spaces; and freeing up staff to conduct other tasks. Purely software-based automation is outside the scope of this report, but is also in increasing use and has enormous potential, for example to transform the extraction of label and specimen data at scale from images. The challenges of managing and digitising collections at scale under DiSSCo are likely to require a combination of hardware and software automation approaches.

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Landscape Analysis for the Specimen Data Refinery

Cited by 21 publications

References 43 publications

Packaging research artefacts with RO-Crate

Packaging research artefacts with RO-Crate

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

Research and development in robotics with potential to automate handling of biological collections

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