A Community Effort Towards FAIR and Reproducible Quality Control for Time Series Data

We analyzed the quality assessment use cases of the Earth and Environment research initiatives, ACTRIS and TERENO, to derive common requirements and design patterns for the quality control process as a whole, as well as the related information schema, in a FAIR and reproducible manner. Based on these requirements, we designed a metadata schema that supports the entire data quality control process. This schema describes the methods, execution contexts, and resulting quality flags in a machine-actionable, interoperable manner. We employed the SensorThings API, the STAMPLATE schema data model, and the concepts established in the SaQC framework using the linked-data approach, aligning with standards such as the Data Quality Vocabulary. The collected design patterns also guide the development of a web-based application. This application enables domain experts to visually inspect data and perform manual QC using the same standardized processing and flagging model as automated procedures. This ensures consistency across QC stages.