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FAIR Data Principles

FAIR Data

Image Attribution: SangyaPundir, CC BY-SA 4.0.

Introduction​

The FAIR Data Principles were published after being defined at the Lorentz Workshop in 2014. These principles serve as guidelines to both those who publish data, e.g. researchers, as well as those who preserve data, e.g. repository or archive service providers. This is an important distinction, highlighting that a researcher’s data can only be as FAIR as the infrastructure and services available to them.

Keeping in mind the long-term goal of making scientific data reusable to others by creating systematic order in the ever-growing mound of data arising from research, the FAIR Data Principles place a strong focus on machine-readable (meta)data. While humans must be able to find and understand the published datasets as well, computers will be tasked with sifting through large amounts of data and determining which datasets are relevant to the intended purpose and how they can be reused.

Reusing well-curated research data, makes researchers’ tasks easier, enabling them to build upon previous research. Furthermore, data reuse has the potential for large stores of data to be mined for machine-learning applications. For example, large amounts of chemical synthesis data could lead to the virtual development of new synthesis methods or the discovery of new compounds.

Info:

In chemistry, the deposition of crystallographic data in a standardized file format (CIF) into a repository such as the Cambridge Structural Database (CSD) with the Cambridge Crystallographic Data Centre (CCDC) or the Inorganic Crystal Structure Database (ICSD) with FIZ Karlsruhe, required by many leading journals in the chemical community, is the primary example of the FAIR Data Principles in action. To broaden the scope of such standards, NFDI4Chem is working towards creating an infrastructure for FAIR data in Germany while training researchers in using the available infrastructure to ensure their data is as FAIR as can be.

On the following pages, we answer the questions: What makes data FAIR? What do researchers and those who provide data preservation services need to consider?

Sources and further information​