Ontologies for increasing the FAIRness of plant research data

TL;DR

This paper reviews how ontologies can enhance the FAIRness of plant research data by improving metadata annotation, interoperability, and reuse, especially in complex omics datasets.

Contribution

It identifies key plant-specific ontologies and metadata frameworks, providing guidance on their application to improve data sharing and integration.

Findings

Identifies relevant ontologies for plant research data.

Highlights repositories for ontology discovery.

Explains ontology application within metadata frameworks.

Abstract

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.