Data validation and data quality assessment
F.H.L.R. Clemens (Deltares, Norwegian University of Science and Technology (NTNU), TU Delft - Sanitary Engineering)
Mathieu Lepot (Un Poids Une Mesure, TU Delft - Sanitary Engineering)
Frank Blumensaat (Eawag - Swiss Federal Institute of Aquatic Science and Technology)
Dominik Leutnant (Emschergenossenschaft)
Guenter Gruber (Graz University of Technology)
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Abstract
Once data have been recorded, data validation procedures have to be conducted to assess the quality of the data, i.e. give a confidence grade. Furthermore, gaps may occur in time series and, depending on the purposes, these can be given values by application of e.g. interpolation. Since both aspects are strongly correlated, this chapter gives an overview on the main data validation and data curation/imputation methods. Instead of offering exhaustive details on existing methods, this chapter aims at providing concepts for most popular techniques, a discussion of their advantages and disadvantages in the light of different cases of application, and some thoughts on potential impacts of the choices that must be made. Despite involving mathematical methods, data validation remains a largely subjective process: every data user must be aware of those subjectivities.
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