An automated solution for measuring the progress toward FAIR research data

Devaraju, Anusuriya; Huber, Robert

doi:10.1016/j.patter.2021.100370

Cited by 36 publications

(27 citation statements)

References 25 publications

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“…Findability, accessibility, interoperability, and reusability (FAIR) principles are also important 48 and should be explained to patients. Ethical and legal issues around the General Data Protection Regulation (GDPR), right to explanation, 49 and duty of care can also be explained to patients in RAG meetings.…”

Section: Discussionmentioning

confidence: 99%

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies

et al. 2022

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“…Naturally, this can be best tested with an algorithmic framework. Indeed, the plethora of FAIRness claims and assessment tools led to stating the FAIR principle more precisely on the one hand 57 , and the development of automated tools on the other hand 2 , 21 , 24 , 58 .…”

Section: Methodsmentioning

confidence: 99%

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

Schwanitz

Wierling

Biresselioğlu

et al. 2022

Sci Rep

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With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.

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“…Since then, several assessment methodologies and tools have been proposed to manually or automatically evaluate to what extent data or different research objects adhere to the FAIR Principles. For instances, FAIRdat, FAIR metrics [3], FAIRshake [2], F-UJI [3], or FAIRchecker [4]. Only one specific tool for ontologies called FOOPS!…”

Section: Context and Motivationsmentioning

confidence: 99%

O’FAIRe: Ontology FAIRness Evaluator in the AgroPortal Semantic Resource Repository

Amdouni

Bouazzouni

Jonquet

2022

The Semantic Web: ESWC 2022 Satellite Events

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O'FAIRe, the Ontology FAIRness Evaluator, is a methodology to automatically assess to which level a semantic resource or ontology respects the FAIR Principles. This paper describes the online tool implementing O'FAIRe within the AgroPortal ontology repository, through 61 questions/tests, among 80% are based on the ontology metadata description. For a specific ontology or a group of semantic resources, O'FAIRe web service outputs both global and detailed scores (normalized) against the 15 FAIR Principles. O'FAIRe results are visualized and explained with new specific user-friendly interfaces (such as the FAIRness wheel) in order to help AgroPortal users improve the FAIRness of their resources. O'FAIRe is currently implemented in three different public ontology repositories as they offer the required metadata descriptions. In the future, we will deploy the service in other OntoPortal repositories.

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An automated solution for measuring the progress toward FAIR research data

Cited by 36 publications

References 25 publications

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies

Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies

Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data

O’FAIRe: Ontology FAIRness Evaluator in the AgroPortal Semantic Resource Repository

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