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Table 2.

Index to FAIR Action Plan Recommendations.

Concepts for FAIR implementationFAIR cultureFAIR ecosystemSkills for FAIRIncentives and metrics for FAIR data and servicesInvestment in FAIR
1. Define FAIR for implementation 4. Develop Interoperability frameworks 7. Support semantic technologies 10. Professionalise data science & stewardship roles 12. Develop metrics for AIR Digital Objects 14. Provide strategic and coordinated funding 
2. Implement a model for FAIR Digital Objects 5. Ensure data management via DMPs 8. Facilitate automated processing 11. Implement curriculum frameworks and training 13. Develop metrics to certify FAIR services 15. Provide sustainable funding 
3. Develop components of a FAIR ecosystem 6. Recognise & reward FAIR data & stewardship 9. Certify FAIR services Above line = priority recommendations 
16. Apply FAIR broadly 18. Cost data management 22. Use information held in DMPs Below line = supporting recommendations 25. Implement and monitor metrics 27. Open EOSC to all providers but ensure services are FAIR 
17 Align and harmonise FAIR and Open Data policy 19. Select and prioritise FAIR digital objects 23. Develop components to meet research needs  26. Support data citation and next generation metrics  
 20. Deposit in Trusted Digital Repositories 24. Incentivise research infrastructures to support FAIR data    
 21. Incentivise reuse of FAIR data     
Concepts for FAIR implementationFAIR cultureFAIR ecosystemSkills for FAIRIncentives and metrics for FAIR data and servicesInvestment in FAIR
1. Define FAIR for implementation 4. Develop Interoperability frameworks 7. Support semantic technologies 10. Professionalise data science & stewardship roles 12. Develop metrics for AIR Digital Objects 14. Provide strategic and coordinated funding 
2. Implement a model for FAIR Digital Objects 5. Ensure data management via DMPs 8. Facilitate automated processing 11. Implement curriculum frameworks and training 13. Develop metrics to certify FAIR services 15. Provide sustainable funding 
3. Develop components of a FAIR ecosystem 6. Recognise & reward FAIR data & stewardship 9. Certify FAIR services Above line = priority recommendations 
16. Apply FAIR broadly 18. Cost data management 22. Use information held in DMPs Below line = supporting recommendations 25. Implement and monitor metrics 27. Open EOSC to all providers but ensure services are FAIR 
17 Align and harmonise FAIR and Open Data policy 19. Select and prioritise FAIR digital objects 23. Develop components to meet research needs  26. Support data citation and next generation metrics  
 20. Deposit in Trusted Digital Repositories 24. Incentivise research infrastructures to support FAIR data    
 21. Incentivise reuse of FAIR data     
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