Besides discussing the technical, scientific and cultural implications of making biological data FAIR and the different approaches to data FAIRification, the event presented the key outcomes foreseen in the FAIRplus project:

• The FAIR cookbook, an open, comprehensive resource with ‘recipes’ for making different types of life science data FAIR, presented by Philippe Rocca-Serra from the University of Oxford’s e-Research Centre.
• The FAIR Capability and Maturity Model, which will document a set of guidelines to find the optimum level of FAIR data implementation depending on the needs and resources in each organisation, presented by Oya Beyan from Fraunhofer FIT.
• The FAIRplus FAIRification process and the first four FAIRified datasets from selected Innovative Medicines Initiative (IMI) projects, presented by Tony Burdett from EMBL-EBI.
• The FAIRplus Fellowship Programme, a comprehensive FAIR data training for EFPIA members, SMEs and academics, presented by Andreas Pippow from Fraunhofer FIT.

All presentations focused on hands-on approaches, highlighting the numerous technical and scientific decisions to be taken when adopting and implementing FAIR data principles.

Learn more about the event:

• News release
• Presentation slides
• Photos from the event
• Full programme
• Programme of the Flash talk session
• Video recording of the keynote speech by Carole Goble
   

The FAIRplus General Assembly took place on 30-31 January 2020 in Hinxton, UK. Representatives from 22 FAIRplus consortium partners reviewed the activities and first results achieved in 2019 and discussed their plans for the future.

Topics included the selection of the next batch of datasets to be FAIRified by the FAIRplus Squad teams, the development of the FAIR cookbook, and the plans for the FAIRplus Fellowship Programme.

The participants also received feedback from the Ethics Advisory Board and the Scientific and Industry Advisory Board, following their review of the first year of FAIRplus activities.
 

The FAIR cookbook is a comprehensive resource collating ‘recipes’ for making different types of life science data FAIR. Developed in Work Package 2 by experts at the Oxford eResearch Centre, the cookbook will help researchers and data managers to learn:

• How to FAIRify a number of exemplar datasets
• How to use levels and indicators of FAIRness
• How to use the different tools to assess and improve FAIRness of data

The FAIR cookbook is open source with anyone invited to contribute. Find out more at https://fairplus.github.io/the-fair-cookbook.
 

Data from the first four pilot projects selected for early FAIRification are now available in the IMI FAIR Data Catalogue. 

These are the first results of the FAIRplus Squad teams who worked on the FAIRification of these data throughout 2019. Based on their experience, they developed a detailed FAIRification process which will be used in 2020 and beyond to FAIRify further datasets (see below).

In 2019, four IMI projects datasets were selected to be piloted for FAIRification by Work Package 1, led by Phill Gribbon (Fraunhofer IME) and David Henderson (Bayer AG). A further nine IMI datasets have now been identified with the potential to generate high societal impact upon FAIRification, an important goal of FAIRplus.

The work of the consortia which generated the datasets covers the range from environmental toxicity assessment methods through to identification of new targets relevant to neurodegenerative diseases. The projects involved are:

• UltraDD
• UBIOPRED
• IMIDIA
• RHAPSODY
• EQIPD

• ABIRISK 
• EBISC I 
• EBISC II
• iPIE 

The FAIRplus squad teams drive the development of the FAIRification processes, working in an iterative manner. The teams meet face-to-face once every three months to review the outputs from the current release cycle and plan the subsequent one. The fourth such meeting took place in January 2020 in Hinxton, UK.

During this meeting the Squads looked back at the past year to refine their processes through retrospective analysis, and identified possible threats to the project. One significant outcome was to reorganize squads to embed EFPIA partners more closely with academic partners. The teams also identified upcoming challenges in scaling up the FAIRification process in meeting the FAIRplus goal to FAIRify datasets from 20 IMI projects. 

In 2020 the Squad teams will shift their approach from a dataset-driven to a more process-centric one, focusing on building reusable components through which they can run multiple datasets. Three use cases have been prioritised as targets for the next sprint, all centred on ontological information capture and maintenance. Initial processes to address these news use cases will be reviewed during the next F2F meeting in April 2020 in Barcelona.

Here you can view the latest deliverables submitted and published online. Between November 2019 and February 2020, four public deliverables have been published:

• First phase Exemplar IMI projects FAIRified
• FAIRplus Handbook and project monitoring
• Selection criteria and guidelines for data sources from IMI projects and EFPIA internal databases
• First FAIR Innovation and SME event

In addition, the alpha version of the FAIR cookbook is available for use on GitHub.

In total, 12 deliverables have been submitted from the start of the project until February 2020.