The original 2016 paper that defined the FAIR principles. Published in the journal Scientific Data.
A stakeholder-driven initiative that aims to implement the FAIR data principles, making data findable, accessible, interoperable, and reusable.
A guide for researchers from OpenAIRE.
A resource for the life sciences, with recipes to help make and keep data FAIR.
Information about data and metadata standards, databases and repositories, and data policies - helpful resources when thinking about making your data FAIR.
A module from the Mantra research data management training course.
An online course exploring six aspects of FAIR data practice: documentation, file formats, metadata, access to data, persistent identifiers, and data licences.
A project aiming to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle.
Online training courses, including:
An open space to post queries, answers, comments and events notifications pertaining to making data FAIR.
A research data management toolkit for the life sciences, offering best practice guidance on making data FAIR.
