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FAIRplus offers the Fellowship programme to educate future FAIR experts who will in turn, transfer the FAIRplus knowledge to other institutions, thus acting as knowledge multipliers. By pooling people from academia with expert partners from SMEs and EFPIAs, and leveraging on the content of the FAIR Cookbook and its authors, the programme provides an opportunity for researchers and practitioners to make data FAIR. The purpose of the Fellowship programme is to train eligible PhD students and/or postdoc level individuals together with industry partners (from the EFPIA and SME network) so that they get a better understanding of what FAIR means for their industries when FAIR is FAIR enough why FAIR data is so essential to today’s life science industry how to transform or initiate a FAIRification process in their organisations how to advise internal departments and teams to make their data FAIR An increased knowledge transfer for FAIR data transformation will naturally lead to a wider sharing of best practices, greater opportunities for innovation, and more insights. The fellowship programme is open for applicants from three different professional groups: EFPIA partners SME partners and affiliated academics, e.g. PhD students and postdocs.
FAIRplus, programme, fellowship, curriculum, FAIRification of data, FAIR
FAIRplus, programme, fellowship, curriculum, 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). | 1 | |
| 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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