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The concept of Findable, Accessible, Interoperable and Re-usable (FAIR) data has become widely disseminated since it was first put forward by Wilkinson et. al. in 2016. A recent example of this is the Beijing Declaration of Research Data from the International Science Council.1 The FAIRplus project has the aim to improve the FAIRness of at least 20 IMI projects, as well as of internal datasets from EFPIA companies. FAIRification opens up the data for re-use by making them findable and machine-readable under clear governance rules about access to sometimes sensitive data. Task 4.4 of the FAIRplus project aims to engage with key policy makers in national and European organisations across all relevant sectors to help ensure a conducive environment for the FAIRification of industry and IMI data in general and help academic participants to provide a stable foundation for the long-term storage of these data.
Engagement, Policy makers, FAIRplus, FAIRification of data, FAIR
Engagement, Policy makers, FAIRplus, FAIRification of data, FAIR
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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