From 7 to 15 April FAIRsFAIR project insiders and external stakeholders alike had the opportunity to immerse themselves fully in all things FAIR thanks to a series of workshops and meetings that gathered together more than 400 participants (60% from Universities and Research Performing Organisati
2021 marks five years since the publication of the FAIR data principles. With a wide range of opinions and commentary, this white paper looks at the real-world impact of FAIR, and considers what will be next for research data and open science.
The FAIR principles provide guidelines for the publication of digital resources such as datasets, code, workflows, and research objects aiming at making them Findable, Accessible, Interoperable, and Reusable(1). Amongst them, the I of the FAIR promotes interoperability and more specifically principle I2 suggests that metadata should use vocabularies that themselves follow the FAIR principles.
The Terms4FAIRskills project continued its engagement with the wider community with two more hack sessions on the 27th and 29th January 2021. Building on the work of the the last two hack sessions in December 2020, the project core team...
We interviewed FAIRsFAIR Champion Isabel Bernal to understand how CSIC is enabling open, reproducible science across multidisciplinary repositories.
This paper surveys existing approaches for improving environmental data access to facilitate more rapid data analyses in computational environments, and thus contribute to a more seamless integration of data and analysis. By analysing current state-of-the-art approaches and solutions being implemented by world‑leading environmental research infrastructures, we highlight the existing practices to interface data repositories with computational environments and the challenges moving forward.
This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group.
This paper presents a programmatic solution for assessing the FAIRness of research data. We describe the translation of the FAIR data principles into measurable metrics and the application of the metrics in evaluating FAIR compliance of research data through an open-source tool we developed.
Participants in the first in a series of hackathons helped refine the terms in the current version of the Terms4FAIRskill terminology by using them to annotate real world training materials.
On Thursday 26th of November the InfraEOSC-5 FAIR data and infrastructure Task Force, set-up by the projects in the INFRAEOSC-05-2018-2019 call: EOSC-synergy, EOSC-Pillar, EOSC-Nordic, NI4OS-Europe, ExPaNDS, FAIRsFAIR, and EOSCsecretariat.eu, organised a special edition of its usual monthly meeting to present to the European Commission the key work being carried out on the topic of FAIR data assessment and certification by the InfraEOSC-5 call funded initiatives. The outcomes of the session are presented in this report