FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Smedt, Koenraad De; Koureas, Dimitrios; Wittenburg, Peter

doi:10.3390/publications8020021

Cited by 86 publications

(35 citation statements)

References 22 publications

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“…A resulting process provenance record is identified with one unique, hash-based identifier in the revision history, and can subsequently be used by authorized actors to automatically retrieve required components and re-execute the processing, irrespective of whether the original compute infrastructure is available 24 . This potential for full computational reproducibility of arbitrary processing steps not only increases the trustworthiness of the research process per se, but permits structured investigations of result variability, and furthermore provides the means to rerun any analysis on new data or a updated analysis components.…”

Section: Resultsmentioning

confidence: 99%

FAIRly big: A framework for computationally reproducible processing of large-scale data

Wagner¹,

Waite²,

Wierzba

et al. 2021

Preprint

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Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the framework's performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

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Section: Resultsmentioning

confidence: 99%

FAIRly big: A framework for computationally reproducible processing of large-scale data

Wagner¹,

Waite²,

Wierzba

et al. 2021

Preprint

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“…A non-exclusive list of FAIR-supporting infrastructures include: beacons [23,24], used primarily for discovering and sharing of genomic data; a federated semantic metadata registry framework [25], which also provides a potential model for population-based patient registries including CRs [26]; the MOLGENIS data platform for data sharing [27]; the Apache Atlas data governance and metadata framework [28]; the European Open Science Cloud (EOSC) interoperability framework [29]; and the FAIR digital object framework [30]. The way in which the FAIR digital object concept is able to support data interoperability, particularly with reference to EOSC, has been discussed in [31].…”

Section: Semantic Interoperabilitymentioning

confidence: 99%

Dotting the “i” of Interoperability in FAIR Cancer-Registry Data Sets

Nicholson¹,

Giusti²,

Neamţiu³

et al. 2022

Biomedical Engineering

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To conform to FAIR principles, data should be findable, accessible, interoperable, and reusable. Whereas tools exist for making data findable and accessible, interoperability is not straightforward and can limit data reusability. Most interoperability-based solutions address semantic description and metadata linkage, but these alone are not sufficient for the requirements of inter-comparison of population-based cancer data, where strict adherence to data-rules is of paramount importance. Ontologies, and more importantly their formalism in description logics, can play a key role in the automation of data-harmonization processes predominantly via the formalization of the data validation rules within the data-domain model. This in turn leads to a potential quality metric allowing users or agents to determine the limitations in the interpretation and comparability of the data. An approach is described for cancer-registry data with practical examples of how the validation rules can be modeled with description logic. Conformance of data to the rules can be quantified to provide metrics for several quality dimensions. Integrating these with metrics derived for other quality dimensions using tools such as data-shape languages and data-completion tests builds up a data-quality context to serve as an additional component in the FAIR digital object to support interoperability in the wider sense.

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“…Also mentioned is the "the atomic entity for a FAIR ecosystem", the FAIR Digital Object (European Commission 2018b). These FAIR Digital Objects, as explained in detail by De Smedt et al (2020) are self-contained, typed, machine-actionable data packages unambiguously identified with persistent identifiers.…”

Section: Persistent Identifiers and Fair For Open Digital Sciencementioning

confidence: 99%

A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo)

Hardisty¹,

Addink²,

Glöckler³

et al. 2021

RIO

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Persistent identifiers (PID) to identify digital representations of physical specimens in natural science collections (i.e., digital specimens) unambiguously and uniquely on the Internet are one of the mechanisms for digitally transforming collections-based science. Digital Specimen PIDs contribute to building and maintaining long-term community trust in the accuracy and authenticity of the scientific data to be managed and presented by the Distributed System of Scientific Collections (DiSSCo) research infrastructure planned in Europe to commence implementation in 2024. Not only are such PIDs valid over the very long timescales common in the heritage sector but they can also transcend changes in underlying technologies of their implementation. They are part of the mechanism for widening access to natural science collections. DiSSCo technical experts previously selected the Handle System as the choice to meet core PID requirements. Using a two-step approach, this options appraisal captures, characterises and analyses different alternative Handle-based PID schemes and the possible operational modes of use. In a first step a weighting and ranking the options has been applied followed by a structured qualitative assessment of social and technical compliance across several assessment dimensions: levels of scalability, community trust, persistence, governance, appropriateness of the scheme and suitability for future global adoption. The results are discussed in relation to branding, community perceptions and global context to determine a preferred PID scheme for DiSSCo that also has potential for adoption and acceptance globally. DiSSCo will adopt a ‘driven-by DOI’ persistent identifier (PID) scheme customised with natural sciences community characteristics. Establishing a new Registration Agency in collaboration with the International DOI Foundation is a practical way forward to support the FAIR (findable, accessible interoperable, reusable) data architecture of DiSSCo research infrastructure. This approach is compatible with the policies of the European Open Science Cloud (EOSC) and is aligned to existing practices across the global community of natural science collections.

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FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Cited by 86 publications

References 22 publications

FAIRly big: A framework for computationally reproducible processing of large-scale data

FAIRly big: A framework for computationally reproducible processing of large-scale data

Dotting the “i” of Interoperability in FAIR Cancer-Registry Data Sets

A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo)

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