Registering information (metadata) about your published articles is necessary so that others can find and read about them. This applies to research data, too - in order to make sure that others can locate and read about your research data, it is important that you share metadata about the data. Publishing metadata about your data helps you increase the visibility and transparency in your research and is one of the first steps in making your data FAIR. To find out more about how and where to register metadata, click here.
For more tips on how to begin the process of making your data FAIR, please see below.
Before making your data FAIR, you should take time to consider how to manage the data in the research life cycle. "Good data management is a necessary precursor", enabling "data to be created which is fit for sharing and reuse. Many decisions taken in the planning and management phases of research affect the potential for data to be made FAIR and/or open. These can include research project roles and responsibilities, consent agreements, data ownership" (Higman et al., 2019, p. 5). "By working from a foundation of effective RDM [Research Data Management]", you "can then consider what is an appropriate level of FAIRness and openness for the individual data set, taking into account factors such as content type, access condition, research project constraints and disciplinary practices" (ibid.).
A good time to begin thinking about your data and how to make it FAIR is in the initial stages of the research project, e.g. when writing the DMP (Data Management Plan). The DMP is a document that can help you with the general aspects of research data management - i.e. how to plan the collection, storing, process, sharing and disposing of your data. But the DMP can also be an opportunity for you to reflect on how and when to make the data FAIR in the research life cycle. Find out more about DMPs here.
The FAIR framework is not intended as a one-size-fits-all approach to research data but rather as a set of general guidelines, which can be adapted across research fields. The idea is that researchers can make their research data "as open as possible, as closed as necessary" by applying the FAIR principles in the specific context of their individual research area and discipline. It is also important to keep in mind that researchers should always work with FAIR in a way that takes into account any legal and ethical aspects of the given research project and data. Ultimately, this means that FAIR data come in many different shapes and sizes.
Even if you do not have permission to make your data publicly available (e.g. when working with personal information), you can still comply with some of the FAIR principles and, in turn, make your data FAIR - for example, by sharing metadata about your data in a data repository and attaching a persistent identifier to the metadata. In this way, you make sure that the data comply with the principles of Findable. To read more about Findable, click here.