Motivation

Researchers need easier and more automated ways to create FAIR-compliant metadata. Existing tools lack coverage and consistency; ConnOSS aims to address these gaps using machine learning while supporting good research practices. The FAIR principles emphasize the importance of machine-actionable metadata for improving research quality, transparency, and reproducibility. However, researchers developing software often lack the time or expertise to manually produce comprehensive metadata and therefore seek automated, low-effort solutions. Current tools and schemas (e.g., CodeMeta, Bioschemas, maSMP) only partially cover metadata elements and face challenges with inconsistency and limited automation. There is also a clear need to harmonize metadata from multiple sources (e.g., GitHub API, citation files, README files) and make it easily accessible for both humans and machines.

Further information can be found on the project page: https://connoss-project.github.io/.