FAIR is an acronym for Findable, Accessible, Interoperable and Reusable. Each word represent principles for ways to handle research data, so the data can be found, accesed, understood and reused by others.   

FAIR is a continuum – meaning you can't be either FAIR or not FAIR. You can start by working with a few of the principles and later dive more into the remaining principles. 

Remember that FAIR data stands for "as open as possible and as closed as necessary”. Meaning that it’s not possible making all data available for the public, but it is still very much relevant working with the principles.  

• Increased visibility and impact: Your research becomes more accessible, interoperable and transparent which can lead to increased exposure, recognition, citations and create opportunities for new collaborations.      
• Improved data quality: The FAIR principles helps promote better data documentation and organization witch leads to increased data quality. 
• Time-saving: By making data easier to find and reuse, researchers saves time and resources that otherwise would have been used for duplicating existing data. 
• Awareness on compliance: Working with the FAIR principles helps ensure that your data handling practices comply with international and institutional standards.