KSC - Observational Data Clustering Preprocessordoi:10.26165/JUELICH-DATA/GLTKXZJülich DATA2025-12-041Hermanns, Alexander; Lange, Anne Caroline; Fuchs, Hendrik; Kowalski, Julia; Franke. Philipp, 2025, "KSC - Observational Data Clustering Preprocessor", https://doi.org/10.26165/JUELICH-DATA/GLTKXZ, Jülich DATA, V1KSC - Observational Data Clustering Preprocessordoi:10.26165/JUELICH-DATA/GLTKXZHermanns, AlexanderLange, Anne CarolineFuchs, HendrikKowalski, JuliaFranke. PhilippJülich DATAFranke, PhilippFranke, Philipp2025-12-03Earth and Environmental SciencesK-Meanair qualityclusteringData assimilationPreprocessing routine for ground-based atmospheric monitoring network data. Utilizing a k-means soft constrained clustering algorithm to derive a representative sub-sampling of the availiable data into an assimilation and validation set. This work was partially performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS- LEE) and received funding from the Helmholtz Association of German Research Centres.CC0 Waiverhttps://doi.org/10.5281/zenodo.14711881Hermanns, A.: KSC – Observational Data Clustering Preprocessor, Zenodo [code], https://doi.org/10.5281/zenodo.14711881, 2025.https://doi.org/10.5194/gmd-18-9417-2025Hermanns, A., Lange, A. C., Kowalski, J., Fuchs, H., and Franke, P.: Data clustering to optimise the representativity of observational data in air quality data assimilation: a case study with EURAD-IM (version 5.9.1 DA), Geosci. Model Dev., 18, 9417–9432, https://doi.org/10.5194/gmd-18-9417-2025, 2025.