A lightweight approach to research object data packaging

Carragáin, Eoghan Ó; Goble, Carole; Sefton, Peter; Soiland‐Reyes, Stian

doi:10.5281/zenodo.3250687

Cited by 14 publications

(7 citation statements)

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“…For instance, in the discussion on supporting ad-hoc vocabularies 20 in RO-Crate, the community explored potential Linked Data solutions. The conventional wisdom in RDF best practices 21 is to establish a vocabulary with a new IRI namespace, formalised using RDF Schema 22 or OWL 23 ontologies. However, this may seem an excessive learning curve for non-experts in semantic knowledge representation, and the RO-Crate community instead agreed on a dual lightweight approach: (i) Document 24 how projects with their own Web-presence can make a pure HTML-based vocabulary, and (ii) provide a community-wide PID namespace under https://w3id.org/ro/terms that redirect to simple CSV files maintained in GitHub.…”

Section: Ensuring Simplicitymentioning

confidence: 99%

“…The initial concept of RO-Crate was formed at the first Workshop on Research Objects (RO2018 38 ), held as part of the IEEE conference on eScience. This workshop followed up on considerations made at a Research Data Alliance (RDA) meeting on Research Data Packaging 39 that found similar goals across multiple data packaging efforts [23]: simplicity, structured metadata and the use of JSON-LD.…”

Section: Peoplementioning

confidence: 99%

“…To address this gap, a new community came together [23] to develop RO-Crate -an approach to package and aggregate research artefacts with their metadata and relationships. The aim of this paper is to introduce RO-Crate and assess it as a strategy for making multiple types of research artefacts FAIR.…”

Section: Introductionmentioning

confidence: 99%

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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: Ensuring Simplicitymentioning

confidence: 99%

Section: Peoplementioning

confidence: 99%

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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%

“…Workflow objects are a specific subclass of research objects. RO-Crate [22,23] has been established as a community standard to practically achieve FAIR packaging of research objects with their structured metadata. Based on well-established Web standards, RO-Crate uses…”

Section: Fair Packaging Of Research/workflow Objects With Ro-cratementioning

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

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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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