Open Data is not enough. Knowledge should be provided in a 'fair' manner, i.e., findable, accessible, interoperable, and reusable.

The FAIR Data principles were published in 2016 and have since become a globally recognised standard for the provision of digital knowledge resources. The goal is to create and provide knowledge resources that can be processed and reused in a versatile manner.

The principles are a central strategic tool in research funding. However, they are also gaining increasing importance in the education and culture sectors.


The FAIR Data principles advocate for a "common language of knowledge representation". This aims to ensure interoperable processing and reusability. However, in most fields, such a common language of knowledge representation is far from being achieved, and many researchers wonder whether it is at all possible or necessary. What options are there to make research data interoperable without having to agree on a common language of knowledge representation?


A brief look into the relevant archives and repositories shows that research data are still being stored in formats that are not interoperably usable. The balance sheet after 8 years of FAIR Data is staggering.

The problem is not with findability and accessibility. The issue lies in creating interoperable and reusable educational and research data. What practical solutions exist, and how can they be implemented in the day-to-day operation of an educational or research institution?



The fundamental problem: In many areas, there is still a lack of tools with which digital content can be processed in a 'fair' way. Which tools are suitable for FAIR-compliant processing of educational and research data? And what workarounds exist for tools that are not inherently so?