Docs/fair

docs/data_life_cycle.rst

@@ -31,3 +31,20 @@ III) Stores
 Using the streamlined data set collections from (II), we built a computationally efficient data interface for machine learning on such large distributed data set collection, thus improving **Usage** (step 5):
 Specifically, this interface is optimised for out-of-core observation-centric indexing in scenarios that are typical to machine learning on single-cell data.
 Read more in our guide to data stores :ref:`distributed_data_rst`.
+
+FAIR data
+=========
+
+FAIR_ data is a set of data management guidelines that are designed to improve data reuse and automated access
+(see also the original publication of FAIR_ for more details).
+The key data management topics addressed by FAIR_ are findability, accessibility, interoperability and reusability.
+Single-cell data sets are usually public and also adhere to varying degrees to FAIR_ principles.
+We designed sfaira so that it improves FAIR_ attributes of published data sets beyond their state at publication.
+Specifically, sfaira:
+
+- improves **findability** of data sets by serving data sets through complex meta data query.
+- improves **accessibility** of data sets by serving streamlined data sets.
+- improves **interoperability** of data sets by streamlining data using versioned meta data ontologies.
+- improves **reusability** of data sets by allowing for iterative improvements of meta data annotation and by shipping usage critical meta data.
+
+.. _FAIR: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/