.. _FAIR quality metrics:

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FAIR Guiding Principles
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The definitions and descriptions of the fair principles and guidelines are presented in detail by Mark Wilkinson et al. in the journal "Nature" 
and can be found online: `Nature`_ 

.. _Nature: https://www.nature.com/articles/sdata201618

The FAIR principles are based on the urgent need to improve the infrastructure for scientific data. These principles are an attempt to make data automatically discoverable 
by machines and thus increase its usability by individuals.

This page provides definitions and help regarding the four principles Findability, Accessibility, Interoperability, and Reusability in the FAIR-Universe. These principles serve as a roadmap for data producers and publishers, aiding them in overcoming challenges and 
optimizing the added value derived from contemporary scholarly digital publishing. It is crucial to note that these principles extend beyond traditional 'data' and encompass 
algorithms, tools, and workflows that contribute to the generation of data. Whether it's data or analytical pipelines, the application of these principles is essential for all
scholarly digital research objects. Transparency, reproducibility, and reusability are ensured when every component of the research process is made available.

.. include:: FAIR_principles/01_Findable.rst

.. include:: FAIR_principles/02_Accessible.rst

.. include:: FAIR_principles/03_Interoperable.rst

.. include:: FAIR_principles/04_Reusable.rst