This project develops a use case-driven metadata schema for transparent, FAIR, and reproducible quality control of environmental time series data. The schema formally describes quality control methods, parameters, execution context, and quality flags, enabling data quality decisions to be documented and exchanged in a machine-actionable way. Building on the SensorThings API data model and established QC concepts from the SaQC framework, it aligns with international standards such as the W3C Data Quality Vocabulary.

The design is informed by real-world quality control workflows from the TERENO and ACTRIS observation networks, covering both automated procedures and expert-driven manual interventions. By abstracting common practices across these use cases, the project identifies shared requirements and reusable design patterns for QC processing metadata. In parallel, a web-based application is being developed that allows domain experts to visually inspect data and apply manual quality control using the same standardized model as automated workflows. Together, the schema and tools lay the foundation for a community-driven, interoperable, and reproducible approach to data quality control across environmental research infrastructures.