Table 1.

Examples of standards and tools that researchers may use to make their data FAIR

Example	Description
Research Data Alliance https://www.rd-alliance.org/	A global, community-driven initiative to build social and technical infrastructure for open sharing and reuse of data, with several working groups in a number of disciplines
FAIRsharing http://www.fairsharing.org	A searchable, interconnected registry of data standards, databases, and data policies across many research areas, allowing researchers to discover relevant repositories that meet their requirements [16]
The FAIR Cookbook https://faircookbook.elixir-europe.org	A collection of practical ‘recipes’ that provide guidance on the operational steps of FAIR data management, from creating unique, persistent identifiers to declaring data’s permitted uses [32]
FAIRassist.org https://fairassist.org	A repository aiming to offer personalized guidance to discover FAIR standards and other resources such as the Data Stewardship Wizard
FAIR Data Self Assessment Tool https://ardc.edu.au/resource/fair-data-self-assessment-tool/	Self-assessment tool from the Australian Research Data Commons that allows users to assess how FAIR their research dataset is by answering simple questions
ELIXIR Research Data Management Kit (RDMKit) https://rdmkit.elixir-europe.org/	Provides a set of best practices and guidelines for FAIR RDM across several life science domains, and journal research data policies [33]
OpenAIRE https://www.openaire.eu	Provides resources for researchers for the management and interoperability of data
Minimum Information about a Cardiac Electrophysiology Experiment (MICEE) [31]	An example of minimum reporting standards for recording, annotating, and reporting data from cardiac electrophysiology experiments
‍FAIRsFAIR Data Policy Checklist https://www.fairsfair.eu/sites/default/files/FsF_Structured_Policy_Descriptions_17022022.pptx.pdf	The FAIRsFAIR FAIR Data Policy Checklist and related structured policy description template provide support for the creation of structured policy documents at the project, institutional, and community level, helping policymakers to assess whether elements of their data policies are FAIR-enabling