Open data vs. FAIR data: A comparison

Open data and FAIR data are two important concepts in the field of data utilisation and provision, and their differences are a source of confusion. Although they have some similarities, they pursue different goals and are based on different principles.

Open vs FAIR Data - a comparison - NFDI4Chem

Open data

Open data is characterised by its free accessibility and usability. Ideally, it is available to everyone without restriction and free of charge for use, dissemination and further development. This can take place in various formats, e.g. via open data portals or APIs. The open data movement promotes transparency and collaboration in research and science by enabling the exchange of knowledge and the reproduction of results. It can also contribute to equal opportunities in science because access to data (e.g. in developing countries) is not dependent on financial resources.

FAIR data

In contrast, FAIR Data stands for data that is ‘findable, accessible, interoperable, and reusable’. FAIR Data focuses on ensuring that data is organised and documented in such a way that others can easily find, access, interact with and use it. This includes the use of standardised metadata standards, formats and protocols, clear and consistent data descriptions and the provision of persistent and unique identifiers for the data.

FAIR does not simply stand for openness: the ‘A’ in FAIR means ‘accessible under well-defined conditions’. There are legitimate reasons to protect data, e.g. privacy, national security and competitiveness. FAIR does not demand unrestricted access to data, but transparent conditions for access and re-use. (See


A key difference between Open Data and FAIR Data is that Open Data focuses on the unrestricted release of data, whereas FAIR Data ensures that this data is organised and documented in such a way that it is easily accessible and usable by others.

In the context of the growing role of AI in chemistry, the interoperability of big data for machines is particularly relevant, as LLM and neural networks are only as good as the data (sets) they are trained with. 

Both concepts play an important role in promoting transparency, innovation and collaboration in science. Although NFDI4Chem is a strong advocate of open data, our main focus is on ensuring that the data is FAIR. Because without fairness, open data loses its value.

Want to read more