0%

Secondary (value-added) databases

Secondary databases extract information from primary data to answer questions such as which genes are expressed under specific conditions and how gene expression differs between conditions. The added value comes from: data processing, additional annotation, mapping to standardised vocabularies or ontologies (such as Experimental Factor Ontology) and analysis to extract gene expression profiles and other results from primary data.

Mapping metadata terms to controlled vocabularies is a core activity in curation, enabling efficient search of datasets by keywords, and is crucial for data sharing across research contexts.