Ontologies for increasing the FAIRness of plant research data. (arXiv:2309.07129v1 [cs.DL])

The importance of improving the FAIRness (findability, accessibility,
interoperability, reusability) of research data is undeniable, especially in
the face of large, complex datasets currently being produced by omics
technologies. Facilitating the integration of a dataset with other types of
data increases the likelihood of reuse, and the potential of answering novel
research questions. Ontologies are a useful tool for semantically tagging
datasets as adding relevant metadata increases the understanding of how data
was produced and increases its interoperability. Ontologies provide concepts
for a particular domain as well as the relationships between concepts. By
tagging data with ontology terms, data becomes both human and machine
interpretable, allowing for increased reuse and interoperability. However, the
task of identifying ontologies relevant to a particular research domain or
technology is challenging, especially within the diverse realm of fundamental
plant research. In this review, we outline the ontologies most relevant to the
fundamental plant sciences and how they can be used to annotate data related to
plant-specific experiments within metadata frameworks, such as
Investigation-Study-Assay (ISA). We also outline repositories and platforms
most useful for identifying applicable ontologies or finding ontology terms.