Implementing FAIR Digital Objects in the EOSC-Life Workflow Collaboratory

Goble, Carole; Soiland‐Reyes, Stian; Bacall, Finn; Owen, Stuart; Williams, Alan; Eguinoa, Ignacio; Droesbeke, Bert; Leo, Simone; Pireddu, Luca; Rodríguez-Navas, Laura; Fernández, José Ma; Capella-Gutiérrez, Salvador; Ménager, Hervé; Grüning, Björn; Serrano-Solano, Beatriz; Ewels, Philip; Coppens, Frederik

doi:10.5281/zenodo.4605654

Cited by 37 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Workflows are further described with authors, license, diagram previews and a listing of their inputs and outputs. RO-Crates can thus be used for interoperable deposition of workflows to WorkflowHub, but are also used as an archive for downloading workflows, embedding metadata registered with the WorkflowHub entry and translated workflow files such as abstract Common Workflow Language (CWL) [36] definitions and diagrams [56].…”

Section: Profile For Describing Workflowsmentioning

confidence: 99%

“…RO-Crate acts therefore as an interoperability layer between registries, repositories and users in Work-flowHub. The iterative development between WorkflowHub developers and the RO-Crate community heavily informed the creation of the Bioschemas [58] profile for Computational Workflows, 54 which again informed the RO-Crate 1.1 specification on workflows 55 and led to the RO-Crate Python library [41] and WorkflowHub's Workflow RO-Crate profile, 56 which, in a similar fashion to RO-Crate itself, recommends which workflow resources and descriptions are required. This co-development across project boundaries exemplifies the drive for simplicity and for establishing best practices.…”

Section: Profile For Describing Workflowsmentioning

confidence: 99%

See 1 more Smart Citation

Packaging research artefacts with RO-Crate

Soiland‐Reyes

Sefton

Crosas

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Profile For Describing Workflowsmentioning

confidence: 99%

Section: Profile For Describing Workflowsmentioning

confidence: 99%

Packaging research artefacts with RO-Crate

Soiland‐Reyes

Sefton

Crosas

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…openDS is essential to future FAIR digitization of natural history collections and to Digital Specimens as self-standing digital objects on the Internet, amenable to computer processing. It contributes to the new transformative generation of FAIR infrastructure and applications based on Digital Object Architecture that is planned for the Distributed System of Scientific Collections (DiSSCo) [6,24,29] European research infrastructure.…”

Section: Co Mputable Digital Specimens As a Kind Of Fair Digital Objectmentioning

confidence: 99%

“…The performance of the SDR will be evaluated against these specimen types, eventually using several thousand different specimen and label images. This is in anticipation of SDR becoming part of the pivotal technology to achieve high rates of mass FAIR digitization expected through the planned DiSSCo research infrastructure [6,24,29].…”

Section: The Fdo and Cwfr Approach In The Specimen Data Refinerymentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract