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Preliminary analysis: Introduction of FAIR data in Denmark

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The Danish Agency for Science and Higher Education has commissioned Oxford Research and Højbjerre Brauer Schultz to carry out a preliminary analysis of the potential for implementing FAIR data in Denmark.
: Mar 01, 2018
: 978-87-93468-97-9
: 72

The purpose of the analysis is twofold:  to estimate the benefits and costs of implementing FAIR and to examine the barriers and opportunities in implementing FAIR data at the Danish research institutions.

Preliminary analysis: introduction of FAIR data in Denmark provides answers to the following questions:


  1. What costs and what benefits can be expected from the implementation of FAIR data – is it socio-economically a good case? 
  2. How do the main Danish stakeholders assess the potential of the FAIR data concept? What op-portunities and barriers do they see in terms of policy, organisation, technology, new hab-its/culture, time (for researchers) and service development in implementing FAIR data in Denmark?
  3. What are the key learnings from the implementation process of FAIR data in Germany in terms of policy, organisation, technology, infrastructure and change in research culture, and
    what benefits are achived/foreseen in Germany?

The analysis was done in second half of 2017. Two main methodological approaches have been used throughout the study:


  1. A systematic literature study of the costs and benefits of implementing FAIR data in Denmark
  2.  A systematic desk research of relevant studies, reports, articles and 36 in-depth qualitative interviews that focus on key stakeholders’ experiences and knowledge of the FAIR principles and their assessment of the potential for introducing FAIR data.

The analysis generally indicates there would be a positive socio-economic value from introducing FAIR data in Denmark in the long run. The analysis also suggests that the size of the benefit will largely depend on how successfully and to what extent FAIR is implemented, and to what extent FAIR data will significantly reduce the amount of time that researchers devote to data work.


last modified Mar 05, 2018