Articles | Volume 16, issue 12
Research article
21 Jun 2023
Research article |  | 21 Jun 2023

A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes

Najmeh Kaffashzadeh

Data sets

TOAR Data Infrastructure Sabine Schröder, Martin G. Schultz, Niklas Selke, Jianing Sun, Jessica Ahring, Amirpasha Mozaffari, Mathilde Romberg, Eleonora Epp, Max Lensing, Sander Apweiler, Lukas H. Leufen, Clara Betancourt, Björn Hagemeier, and Saini Rajveer

Model code and software

A statistical data-driven test for probabilistic data quality control Najmeh Kaffashzadeh

Short summary
Although quality control is a well-known issue in data application, research initiatives and organizations apply given methods based on traditional techniques (ad hoc thresholds and manual). These approaches are not only error prone but also unsuitable for a large volume of data. The method proposed in this paper is based on a new concept (probability) as an intuitive indicator and data’s characteristics, which leads it to be applicable to a wide variety of data and eases its fit for purpose.