Clarify and strengthen the recommendations

[band]

Three suggestions regarding the action plan based on my previous work on technical standards and specifications.

1. Adopt IEFT RFC 2119 (https://www.ietf.org/rfc/rfc2119.txt) as source of definitions for the terms "should", "need", etc. I think this will clarify recommendations and support developing metrics. (It may also make some of those conversations more drawn out.)

2. Try to describe the properties and attributes of FAIR objects in the present tense. So write "Data are assigned a unique and persistent identifier ...." rather than "Data should be assigned ...." Stronger sentence and fewer words.

3. There are a large number of recommendations. Can a phased action plan be suggested in each area? Resources are limited; what recommendations are to be implemented first?

[asconrad]

It is an impressive and comprehensive document! I suppose it mainly adresses the political level, there is still a way to go before I could, say pick which topics to prioritize in the work plan for the National Data Management Forum for 2019. That could be a next step, maybe to extract implementation paths for the specific types of stakeholders.

[hollydawnmurray]

F1000 position: Upvote on point 3 above. Along the same vein, a plan for periodic review and reporting to qualify uptake as well as identify potential barriers would be a welcome addition to this work. Given the comprehensive and innovative approach planned here, it may also be worth starting with a pilot project, where, for e.g. a subset these recommendations are imposed, to allow any potential issues and challenge to emerge and be resolved – prior to introducing the plan more broadly.

[pkdoorn]

Like others have said, 34 recommendations is rather a lot. Implicit in my reaction is a certain priority, and I would make these priorities more explicit in the final version. In my view, operationalizing the FAIR principles in concrete metrics (#9) is an important first step, from which recommendations for improving the definitions (#1) will follow.
The further down I read through the recommendations, the more I got the feeling that I had already read something quite similar earlier on. Hence a couple of suggestions to combine/link/integrate recommendations. I did not go through the list systematically, but it makes sense to group, for example, all the recommendations related to costs/funding (#5, #6, #32, #33).

[gtoneill]

Fantastic work by the members of the EC Expert Group on Fair Data on all the recommendations and opening up the consultation process to interested stakeholders via Github and Google Docs. Some general suggestions for the recommendations which support some previous comments: (1) 34 recommendations is a lot and there is much overlap between the recommendations > merge as many overlapping recommendations as possible (2) The recommendations are simply numbered and are not further categorised > group recommendations together under common themes such as 'FAIR Principles', 'Policies', 'Funding', 'Interoperability', 'Rewards/Incentives', 'Training/Support', and 'Repositories/Service Providers' (3) The recommendations are not prioritised and there are no time-frames proposed > identify which recommendations have high, medium, or low priority and a realistic proposed time-frame for each recommendation to be implemented (4) The link with EOSC is not fully clear in the recommendations > make potental relations between recommendations and EOSC much clearer either in the recommendations or in the final report. Eurodoc is happy to give further input!

[etothczifra]

DARIAH-ERIC position: Fantastic work indeed! We strongly agree with prioritization needs though. Also, in addition to the current recommendations, best practices or examples of successful cooperation between the different service providers would be welcome. For instance, to create a dataset require alliance between the data creator and the data repository. Enabling the relevant actors to connect and improve together to act as a coordinated network of providers for data-related services with clear division of roles (e.g. collaboration between services with primary focus on facilitating reuse with institutions focusing on long-term archiving) could increase both cost-efficiency of FAIR data management and transparency of the service landscape.