A consistent, extensible, easy-to-use tool/framework for reproducible quality control of time series data.

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What SaQC can do for you

SaQC in a nutshell

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 an accessible, consistent, objective and reproducible way.

SaQCExemplary screenshot of a time series analysis using SaQC.

Logo of SaQC
Programming language
  • Python 100%
  • GPL-3.0-or-later
</>Source code

Participating organisations

Helmholtz Centre for Environmental Research (UFZ)

Related projects


DataHub Initiative of the Research Field Earth and Environment

Updated 1 month ago