What does this mean?
Humans should be able to exchange and interpret each other’s data (so preferably do not use dead languages). But this also applies to computers, meaning that data that should be readable for machines without the need for specialised or ad hoc algorithms, translators, or mappings. Interoperability typically means that each computer system at least has knowledge of the other system’s data exchange formats. For this to happen and to ensure automatic findability and interoperability of datasets, it is critical to use (1) commonly used controlled vocabularies, ontologies, thesauri (having resolvable globally unique and persistent identifiers, see F1) and (2) a good data model (a well-defined framework to describe and structure (meta)data).
Examples
- The RDF extensible knowledge representation model is a way to describe and structure datasets. You can refer to the Dublin Core Schema as an example.
- OWL
- DAML+OIL
- JSON LD
- See example data models for Predicted gene-disease associations from text mining and Tissue gene expression.
- See data models from EBI in the ‘documentation’ links on this page http://www.ebi.ac.uk/rdf/
Links to Resources