Data validation and data quality assessment

F.H.L.R. Clemens, Mathieu Lepot, Frank Blumensaat, Dominik Leutnant, Guenter Gruber

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

5 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationMetrology in Urban Drainage and Stormwater Management
Subtitle of host publicationPlug and Pray
EditorsJean Luc Bertrand-Krajewski, Francois Clemens-Meyer, Mathieu Lepot
PublisherInternational Water Association (IWA)
Chapter9
Pages327-390
Number of pages64
ISBN (Electronic)978-1-7890-6011-9
ISBN (Print)978-1-7890-6010-2
DOIs
Publication statusPublished - 2021

Keywords

  • Data curation/imputation
  • data quality assessment
  • data validation
  • interpolation

Fingerprint

Dive into the research topics of 'Data validation and data quality assessment'. Together they form a unique fingerprint.

Cite this