FAIR in practice: minimum metadata schema for bioinformatics analytics by machines

BACKGROUND: One pillar of FAIR principles adoption is Reusability by machines to enable for example more efficient data analytics in fields such as Bioinformatics. However, it is not clear to what extent current metadata exposed by datasets and tools in common repositories enable this. In practice, metadata often lacks in machine actionability due to incomplete standardised metadata and lack of ontological descriptions. RESULTS: In this work, we identified minimal metadata that is needed to improve the machine actionability of bioinformatics tools and proposed a schema to address current limitations. The schema consists of metadata properties for the identification, selection, validation, and execution of tools. We also aligned this metadata to the metadata of datasets, in order to improve their integration for analytics by machines. CONCLUSIONS: The identified minimal metadata improves the machine actionability of tools and data, and can be incorporated into platforms for tool and data sharing, and in FAIR infrastructures.