Contents for analysing data

A proper understanding of the data is an essential

This step analyses the data to support the

As a preparation of the FAIRification itself, this step also includes checking if some FAIR features already are existing in the data such as persistent identifiers. If the data are extensive, running

The second step is to analyze the data to prepare for subsequent FAIRification (e.g., improving interoperability) and is within the pre-FAIRification phase of the workflow. This process may include: 1) investigating the data in whatever form(s) it is available (specified in Step 1) and checking whether both the data representation (format) and the meaning of the data elements (the data semantics) are clear and unambiguous, and 2) checking whether the data already contain FAIR features, such as persistent unique identifiers for data elements [14] (FAIR principle F1 [1]) by e.g., using FAIRness assessment tooling [2, 3, 4]. It is evident that this step is tightly connected with Step 1 since e.g., selecting a relevant subset of the data and defining driving user questions(s) are highly relying on being familiar with the data.