System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science

• Published in: Environmental modelling & software : with environment data news 2023-11, Vol.169, p.105809, Article 105809
• Main Authors: Schmidt, Lennart, Schäfer, David, Geller, Juliane, Lünenschloss, Peter, Palm, Bert, Rinke, Karsten, Rebmann, Corinna, Rode, Michael, Bumberger, Jan
• Format: Article
• Language: English
• Subjects: Anomaly detection; Data management; FAIR; Quality assurance; Quality control; Sensor data
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2023.105809

Environmental sensor networks produce continuously increasing volumes of raw data that need to be transformed into usable data for monitoring ongoing environmental changes and decision-support. The crucial challenge is providing data in real-time, which requires the rigorous automation of quality control (QC) workflows using suitable software tools. We present the System for automated Quality Control (SaQC), a software package for the automated quality control of environmental time series data that is universal and that can be expanded in its set of domain-agnostic QC and processing functionalities, while at the same time being user-friendly in its low-code configuration environment. Two applications present the configuration of basic and advanced quality control applications using SaQC. We also elaborate on the explicit user controls over the handling of quality flags and how SaQC can be used to make QC-workflows traceable and reproducible, thus promoting FAIR data streams of high quality. •We present the Python package System for automated Quality Control (SaQC).•SaQC facilitates the implementation of workflows for automated quality control•It is designed for domain scientists that manage environmental sensor networks.•It is universal, user-friendly and can be extended.•It addresses crucial challenges regarding quality control and the FAIRness of data streams.