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The Open Ontology-Based Interoperable Information Asset Metadata Schema (OIMS) is a flexible and extensible metadata schema designed to standardize and organize metadata for various information assets like datasets, documents, models, and publications, making them more accessible, transparent, and reusable.

OIMS addresses the challenge of managing data in agriculture’s data-driven landscape. It provides a flexible schema for standardizing and organizing metadata of diverse information assets like datasets, documents, and models. By establishing a common language for data description, OIMS improves accessibility and reusability. Specialized metadata often lacks standardization, leading to inconsistencies. OIMS tackles this by standardizing the metadata and meta-metadata structure, ensuring long-term flexibility.

OIMS is a comprehensive and structured approach to managing and organizing valuable information assets. Designed with a strong foundation in ontology, OIMS ensures efficient organization and retrieval of data resources within complex information systems. By leveraging standardized metadata schema, OIMS promotes interoperability, consistency, and clarity across various data domains. Its adaptable and extensible structure allows for seamless integration into a wide range of applications, empowering organizations to harness the full potential of their information assets and drive data-driven decision-making processes.

We started out back in 2017 trying to make messy socio-economic data interoperable, as part of the community of practice on socio-economic data that was part of the now defunct CGIAR Platform for Big Data in Agriculture.

We recently published version 2.4. This latest version is fully compatible with all FAIR standards.

The OIMS schema consists of a header section containing information about underlying metadata schemas and descriptors, while the content section enables consistent representation of metadata components. OIMS is being utilized by the CGIAR Platform for Big Data in Agriculture and the One CGIAR initiative on Foresight, enhancing the democratization of foresight analysis.

Practical applications of OIMS include conversion tools for structured metadata files and analysis tools for improved asset retrieval and comprehension. Conversion tools transform metadata fields from various formats into OIMS-compatible JSON files, enabling versatility. Analysis tools leverage OIMS-compatible metadata for enhanced access and transparency.

OIMS contributes to an inclusive open science model in agriculture by establishing a standardized metadata structure. This enables seamless integration and utilization of information assets across scientific domains. The presentation concludes by highlighting ongoing development efforts and the potential of OIMS to revolutionize accessibility, transparency, and usability in agriculture. We cannot under-estimate the importance of rich metadata for open databases in agriculture. OIMS serves as a powerful tool, improving accessibility and transparency. Its standardized meta-metadata structure enables effective data integration and utilization, fostering collaboration and innovation in agricultural research.

Further reading

Kruseman, G.; Laporte, M.A. 2023. OIMS basic documentation: Technical documentation of the OIMS Base self-describing metadata schema, the high-level OIMS structure and their validation schemas. OIMS version 2.4. 113 p.

Kruseman G. 2022.A Flexible, Extensible, Machine-Readable, Human-Intelligible, and Ontology-Agnostic Metadata Schema (OIMS). Frontiers in Sustainable Food Systems vol. 6.