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SaQC - System for automated Quality Control

Anomalies and errors are the rule not the exception when working with time series data. This is especially true, if such data originates from in-situ measurements of environmental properties. Almost all applications, however, implicitly rely on data, that complies with some definition of ‘correct’. In order to infer reliable data products and tools, there is no alternative to quality control. SaQC provides all the building blocks to comfortably bridge the gap between ‘usually faulty’ and ‘expected to be corrected’ in a accessible, consistent, objective and reproducible way.

SaQC is developed and maintained by the Research Data Management Team at the Helmholtz-Centre for Environmental Research - UFZ. It manifests the requirements and experiences made from the implementation and operation of fully automated quality control pipelines for environmental sensor data. The diversity of communities involved in this process and the special needs within the realm of scientific data acquisition and its provisioning, have shaped SaQC into its current state. We define this state as: inherently consistent, yet externally extensible, traceable, approachable for non-programmers and usable in a wide range of applications, from exploratory interactive programming environments to large-scale fully automated, managed workflows.

Features

pyLogo csvConfig

sacRaw

sacFlagged

  • apply basic plausibility checks, as well as

  • more complex, univariat flagging Functions

legendEXMPL

  • automatically keep track of flagging history and flags significance for every datapoint

  • define and use custom schemes to translate your flags to and from SaQC

sacProc

sacMV

Getting Started

Indices and tables