Expanding FAIR solutions across EOSC
FAIR-IMPACT focuses on expanding FAIR solutions across the EOSC, building on the results of FAIRsFAIR and other relevant projects and initiatives. Its ambition is to enable a FAIR supporting EOSC together with scientific communities and relevant stakeholder groups, and to take steps towards realising the ambition of a web of Open Science. FAIR-IMPACT will contribute to transforming the way researchers share and exploit research outputs within and across research disciplines, and to the facilitation of scientific multi-disciplinary cooperation. With its focus on increasing FAIRness, it will contribute to improving public trust and reproducibility in science.
The project will identify proven domain solutions and facilitate uptake of these solutions across scientific domains and for different types of research output, as well as support the implementation of the solutions in the FAIRCORE4EOSC project. These aim at the overall FAIRification of various research objects from assigning and managing identifiers, describing them with shared and common semantics, to making them interoperable and reusable, as well as the challenge of projecting the FAIR principles to other types of research objects such as software. FAIR-IMPACT meets these challenges through three work packages which identify and adapt candidate approaches, tools and solutions suitable for wider adoption, and two work packages focusing on interoperability, adoption and support. Scientific communities are included in the consortium as integrated use case partners. This will ensure that viable and tested solutions from one domain can be piloted in others, and help to achieve wider uptake, adoption, implementation of, and compliance with the FAIR principles. As the project unfolds, additional support mechanisms will be introduced.
CSC will especially focus on the work with PID service providers, researchers and infrastructures to meet user needs, align with EOSC policy and maximise uptake of persistent identifiers enabling FAIR born data and research reproducibility.