The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016). FAIR data allows reuse of data and enables the computers to find and use data. There are several metrics to define the FAIRness of data :
TO BE FINDABLE####
F1. (meta)data are assigned a globally unique and eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a searchable resource.
F4. metadata specify the data identifier.
TO BE ACCESSIBLE:
A1 (meta)data are retrievable by their identifier using a standardized communications protocol.
A1.1 the protocol is open, free, and universally implementable.
A1.2 the protocol allows for an authentication and authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no longer available.
TO BE INTEROPERABLE:
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles.
I3. (meta)data include qualified references to other (meta)data.
TO BE RE-USABLE:
R1. meta(data) have a plurality of accurate and relevant attributes.
R1.1. (meta)data are released with a clear and accessible data usage license.
R1.2. (meta)data are associated with their provenance.
R1.3. (meta)data meet domain-relevant community standards.
- creating and implementing FAIR data metrics in Java
- assess and produce the metadata with standard vocabularies
- integrate it with the RDFUnit tool (https://github.com/AKSW/RDFUnit)
- project will increase the FAIRness of the DBpedia
- Read the papers: