Connected Open Source Software

Machine-actionable metadata for research artifacts is key to realize the FAIR Guiding Principles.

In recent years, the scientific community has turned to the controlled vocabulary schema.org and efforts based on this vocabulary, e.g. Codemeta, Bioschemas and the Machine-actionable Software Management Plan Metadata Schema, aim to improve the metadata descriptions of research software.

In addition, some efforts on (semi)automatic metadata extraction, mostly from GitHub, have emerged. However, none of the current efforts provide a high metadata coverage due to the multiple sources to be considered and harmonized.

The Connected Open Source Software project (ConnOSS) targets at providing

a consistent and comprehensive metadata extraction and publication infrastructure that will serve humans and machines alike. Leveraging both existing approaches and machine learning based approaches, it will showcase software production by research groups with consistent, harmonized, and enriched machine-actionable metadata; thus, improving the visibility and FAIRness of research software. Furthermore, it will allow registries and aggregators to harvest the metadata and create additional value.