AutoFAIR-A portal for automating FAIR assessments for bioinformatics resources

Bonello, Joseph; Cachia, Ernest; Alfino, Nigel

doi:10.1016/j.bbagrm.2021.194767

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Once a digital resource has been improved to where it is findable and has machine-readable metadata, integration with the F-UJI tool will yield additional insights regarding the machine-readability of metadata related to interoperability and reusability. This approach combining the advantages of manual and automated approaches is supported by the findings from assessment tool evaluations (Gehlen et al 2022;Krans et al 2022) and has been adopted in other domains (Bonello, Cachia and Alfino 2022).…”

Section: Discussionmentioning

confidence: 96%

Advancing FAIR Agricultural Data: The AgReFed FAIR Assessment Tool

Bahlo

2024

Data Science Journal

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The FAIR principles provide guidance for improving the findability, accessibility, interoperability and reuse of research data and other digital objects. The Agricultural Research Federation (AgReFed) has developed a FAIR implementation consisting of policies, resources and tools to enable FAIR agricultural data in Australia. It prescribes minimum acceptable data standards and an associated set of FAIR metrics for agricultural research data. Existing FAIR assessment tools were examined and found to have limitations in serving the purposes of AgReFed.The AgReFed FAIR assessment tool addresses these needs with novel features to help improve FAIRness of datasets and other digital resources. By providing the ability to assess and subsequently re-assess datasets while also enabling users to add comments, it facilitates building work lists that support and document the improvement in FAIRness. Other innovative features include customisable FAIR metrics, storage of assessment results, a versioning system and inbuilt help resources. In addition to userfriendly reporting of AgReFed standards compliance, the integrated automated F-UJI FAIR assessment API provides a supplementary set of machine-readable FAIR scores.The AgReFed FAIR Assessment Tool has been deployed for public use and released as open source to help data custodians enact FAIR principles in domains and data communities within and beyond Australian agriculture.

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Section: Discussionmentioning

confidence: 96%

Advancing FAIR Agricultural Data: The AgReFed FAIR Assessment Tool

Bahlo

2024

Data Science Journal

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“…In addition, as a research project evolves new versions of a given output may get published and so do associated metadata and supplementary materials, meaning that their FAIRness degree may change, too. Although there are some manual services to measure the FAIRness level 14 , 15 , their use is discouraged since they can not address the evaluation of many digital objects quickly. On the contrary, automatic tools should be capable of providing a set of measurements or results based on the provided indicators, showing how FAIR principles can be better adopted.…”

Section: Introductionmentioning

confidence: 99%

FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture

Aguilar Gómez,

Bernal

2023

Sci Data

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The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository.

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FAIR health data in the national and international data space

Waltemath,

Beyan,

Crameri

et al. 2024

Bundesgesundheitsbl

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ZusammenfassungGesundheitsdaten haben in der heutigen datenorientierten Welt einen hohen Stellenwert. Durch automatisierte Verarbeitung können z. B. Prozesse im Gesundheitswesen optimiert und klinische Entscheidungen unterstützt werden. Dabei sind Aussagekraft, Qualität und Vertrauenswürdigkeit der Daten wichtig. Nur so kann garantiert werden, dass die Daten sinnvoll nachgenutzt werden können.Konkrete Anforderungen an die Beschreibung und Kodierung von Daten werden in den FAIR-Prinzipien beschrieben. Verschiedene nationale Forschungsverbünde und Infrastrukturprojekte im Gesundheitswesen haben sich bereits klar zu den FAIR-Prinzipien positioniert: Sowohl die Infrastrukturen der Medizininformatik-Initiative als auch des Netzwerks Universitätsmedizin operieren explizit auf Basis der FAIR-Prinzipien, ebenso die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten oder das Deutsche Zentrum für Diabetesforschung.Um eine FAIRe Ressource bereitzustellen, sollte zuerst in einem Assessment der FAIRness-Grad festgestellt werden und danach die Priorisierung für Verbesserungsschritte erfolgen (FAIRification). Seit 2016 wurden zahlreiche Werkzeuge und Richtlinien für beide Schritte entwickelt, basierend auf den unterschiedlichen, domänenspezifischen Interpretationen der FAIR-Prinzipien.Auch die europäischen Nachbarländer haben in die Entwicklung eines nationalen Rahmens für semantische Interoperabilität im Kontext der FAIR-Prinzipien investiert. So wurden Konzepte für eine umfassende Datenanreicherung entwickelt, um die Datenanalyse beispielsweise im Europäischen Gesundheitsdatenraum oder über das Netzwerk der Observational Health Data Sciences and Informatics zu vereinfachen. In Kooperation mit internationalen Projekten, wie z. B. der European Open Science Cloud, wurden strukturierte FAIRification-Maßnahmen für Gesundheitsdatensätze entwickelt.

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AutoFAIR-A portal for automating FAIR assessments for bioinformatics resources

Cited by 4 publications

References 10 publications

Advancing FAIR Agricultural Data: The AgReFed FAIR Assessment Tool

Advancing FAIR Agricultural Data: The AgReFed FAIR Assessment Tool

FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture

FAIR health data in the national and international data space

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