RDM, open and FAIR

In order to make full use of the potential of research data, it is necessary to include them in the research eco-system as Findable, Accessible, Interoperable and Reusable as possible (Force 11, 2014; Wilkinson et al., 2016; GoFAIR, n.d.). The FAIR principles consist of 15 facets (GoFAIR, n.d.). The main thing is that research data should not only be FAIR for people, but also for computers/machines. The FAIR principles are now an integral part of the data management landscape and form the basis of the construction plan for the European Open Science Cloud.

No.

In an article by Mons et al. (2017) you can read the following explanation:  

The 'A' in FAIR stands for 'Accessible', accessible under well-defined circumstances. There may be legitimate reasons not to make research data available in open access, such as protecting the privacy of research participants. FAIR is about making research data findable by man and machine by:

• enrichting the data in such a way that value of the data for reuse is clear;
• explicitly - with a clear, preferably machine-readable licence - indicating the conditions under which data may be reused; 
• giving clear instructions on how to quote in case of reuse.

None of these principles requires research data to be 'open' or 'free'. FAIR is about clarity and transparency about the conditions for access and reuse. 

Data can therefore be FAIR and not open.

The other way around, data can also be open but not FAIR.

FAIR data management is the set of decisions and measures taken during the life cycle of research data to deliver research data as FAIR as possible. What is important here is the choice of a data license, the data format, the recording of metadata and data documentation, naming conventions, etc. These aspects are all discussed in the following chapters of Essentials 4 Data Support.

FAIR is a brilliant acronym and this is one of the reasons why FAIR is an integral part of the data management landscape. By the way, they are not the only data principles in circulation. In addition to the FAIR acronym, there is also the FACT acronym - Fair, Accurate, Confidential, Transparent - as described on the Digital Society website (n.d.).

The role of data archives in making data FAIR is very large. In this course we focus on researchers and what they can do to prepare data for FAIRness. But without a data infrastructure built on FAIR principles, researchers can't do very much. The recommendations for creating FAIR data often focus on both target groups: researchers and data archives. This can be confusing. 

The Parthenos Guidelines to FAIRify data management and make data reusable (Parthenos, 2018) make a distinction between the measures per target group. So that those who fit the shoe can also put it on.

One of the recommendations of the European Commission (2017) to make data FAIR is to publish it in a reliable data archive that adheres to the FAIR principles. How such a data archive exactly manages this is not something that a researcher needs to know exactly.  

Guides

• Have a look at these concise guides (Erdman, et al., 2019)with "Top 10 FAIR data and Software Things" that can be used by the research community:
  • to understand FAIR in different contexts;
  • as a starting point for discussions about FAIR. An easily accessible guide is the one about Data Management Support.  
• Have a look at the cases in the report 'FAIR Data Advanced Use Cases: from principles to practice in the Netherlands' (Imming, 2018).

Flyers:

• PARTHENOS Guidelines to FAIRify data management and make data reusable (PARTHENOS, 2018). 
• ‘How FAIR are your data?’ checklist (Jones & Grootveld, 2017).

Fancy some magic? Have a look at 'A FAIRy tale' (Hansen, et al., 2018).

• RDM Support at Utrecht University has written a guide with the title 'How to make your data FAIR' (Utrecht University, n.d.). On the one hand, this is a guide with which you can learn more about how to make data FAIR, but it is also an example of how you could give shape to data support surrounding this theme. By creating information material and making it available.
• Employees of Wageningen University & Research write blogs about FAIR data and FAIR data stewardship (E.g. Wageningen University & Research, 2016 & 2019). 

The report 'FAIR data in practice' (Allen & Hartland, 2018) provides insight into the actual situation regarding FAIR data management in the UK. The following findings are central (Bruce & Cordewener, 2019): 

• There is a lack of knowledge and understanding of what FAIR means and what the benefit of FAIR data management could be for research; 
• This lack of knowledge is unevenly distributed across disciplines and stakeholders;
• Change is needed and investment needs to be made in training researchers;
• This requires coordination of activities and policy development at interdisciplinary, national and international levels.

UK researchers see the FAIR data principles as:

• Going beyond open access;
• A useful concept in bringing together different aspects of best practices for data management;  
• Useful for supporting the reuse of research data via various types of data licenses. 

The report also offers a number of recommendations to best align existing tools and resources with FAIR.