Data sharing benefits scientific advancement by promoting transparency, encouraging collaboration, accelerating research, and driving better decision-making. In fields such as public health, data sharing is proven to be critical in response to emergencies such as outbreaks of infectious diseases. As data practitioners, are there guidelines we can follow while preparing our data for sharing? The answer is yes!

The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published in Scientific Data in 2016, are a set of guiding principles proposed by a consortium of scientists and organizations to support the reusability of digital assets. These principles have since been adopted by research institutions worldwide. The guidelines are timely as we see the unprecedented volume, complexity, and speed in creation of data.

"How do I make my data FAIR?" you might ask. To make data findable, data and supplementary materials should have sufficiently detailed descriptive metadata as well as a unique and persistent identifier such as a digital object identifier (DOI). To be accessible, the metadata and data should be understandable to both humans and machines, and data should be stored in a trusted repository. To be interoperable, metadata should use a formal, accessible, shared, and broadly applicable language for knowledge representation, such as agreed-upon controlled vocabularies. To be reusable, data and collections should have a clear usage license and provide accurate information on provenance. The GO FAIR initiative has provided a more in-depth explanation of FAIR principles.

You might be wondering how FAIR data benefit data creators and users. As humans increasingly rely on computational support from machines, FAIR data can enable computational systems to find, access, interoperate, and reuse data with no or minimal human intervention. On a personal level, the FAIR Data Principles provide a data management framework to help researchers manage their data assets. Additionally, by sharing data that are FAIR, researchers facilitate knowledge discovery and increase the chance of possible collaboration, which are beneficial especially for early-career researchers.

Are you ready to GO FAIR with your data? Before you do, be mindful that FAIR data and open data are two distinct concepts. In the fields of medicine and public health, where patient-level data and personally identifiable information (PII) are often involved, data can be FAIR but not open to protect privacy and confidentiality. In this case, consider making the metadata publicly available and supplement information about the conditions for accessing and reusing the data itself.