Ontologies for increasing the FAIRness of plant research data

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.Comment: 34 pages, 4 figures, 1 table, 1 supplementary tabl

Improving Metadata Collection and Aggregation in Plant Phenotyping Experiments with MIAPPE Wizard and DataPLANT

As part of the BioHackathon Germany 2022, we hereby report on the success of the two projects “MIAPPE Wizard: Enabling easy creation of MIAPPE-compliant ISA metadata for Plant Phenotyping Experiments” and “DataPLANT - Facilitating Research Data Management to combat the reproducibility crisis”. Shortly before the actual hackathon, it became apparent to the participants that close coordination between the projects would be very beneficial. Both projects aimed to improve the process of collecting and aggregating metadata on plant experiments, but with different approaches