Project deliverable Open Access
Burdett, Tony; Courtot, Mélanie; Xu, Fuqi; Gu, Wei; Satagopam, Venkata; Juty, Nick; Beyan, Oya; Ronzano, Francesco; Furlong, Laura; Emam, Ibrahim; Goble, Carole; Giessmann, Robert; Lynch, Nick
FAIRplus seeks to establish ‘FAIRification’ processes that can be used at scale to ensure FAIRness of IMI data. In order to establish, refine and validate FAIRplus FAIRification techniques, four pilot IMI datasets were selected in D1.1. This deliverable describes the outcomes achieved to date when FAIRifying these datasets using the newly established FAIRplus FAIRification process.
The FAIRified pilot datasets are now listed in the IMI data catalogue, along with an evaluation of their level of FAIR after FAIRification processes were applied. In this deliverable, we show that FAIRplus FAIRification processes generally increase the level of FAIR for pilot datasets, although no datasets became “completely FAIR” as a result. Level of FAIR is quantified by a series of FAIR indicators, established from a FAIR assessment conducted on each dataset before and after FAIRification. The results of these assessments are linked from the IMI data catalogue. All processes used during FAIRification are documented as recipes in the FAIR Cookbook.
D3.1 First phase exemplar IMI projects FAIRified.pdf