FAIR data advanced use cases(Publicatie)From principle to practice in the Netherlands The purpose of this report is to build and share expertise on the implementation of FAIR data policy in the Netherlands (Findable, Accessible, Interoperable and Reusable). 6 use cases describe how people from different scientific domains are implementing FAIR in their policies and practice, or how they plan to do so in the future.
The FAIR data principles are gaining momentum, both in the Netherlands and internationally. The purpose of this report is to build and share expertise on the implementation of FAIR data policy in the Netherlands. For SURF, it is important to gain a better picture of the best way to support researchers who want to make their data FAIR. The 6 use cases in this report describe how different actors from different domains are implementing FAIR in their policies and practice, or how they plan to do so in the future.
FAIR is a simple message
The idea that data needs to be Findable, Accessible, Interoperable and Reusable is a simple message which appeals to many. The 15 international FAIR principles were published in 2016. They serve as a guideline for preparing research data for reuse under clearly described conditions by both people and machines. They are intentionally principles and not standards.
Standards, tools and training
Various organisations and disciplines are developing standards, tools and training based on their own interpretation of the FAIR principles. Some domains have already done a great deal of work on this, although not always under the FAIR banner. Other domains do not traditionally use large quantities of research data and are at an earlier stage.
Research data management
The use cases in this report are based on interviews with people involved. They discuss the extent to which they have already been working with research data management before the term FAIR was introduced, what they see as the advantages of FAIR, what is currently being done to stimulate the FAIR work method within their domains, what difficulties they face and what is happening in terms of interdisciplinary developments. The future of FAIR is also discussed: what resources do researchers and research supporters need (in terms of long-term data storage, among other things) and what further details and support are needed?
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