{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.26165/JUELICH-DATA/GLTKXZ","identifier":"https://doi.org/10.26165/JUELICH-DATA/GLTKXZ","name":"KSC - Observational Data Clustering Preprocessor","creator":[{"name":"Hermanns, Alexander","affiliation":"Forschungszentrum Jülich"},{"name":"Lange, Anne Caroline","affiliation":"Forschungszentrum Jülich"},{"name":"Fuchs, Hendrik","affiliation":"Forschungszentrum Jülich"},{"name":"Kowalski, Julia","affiliation":"RWTH Aachen university"},{"name":"Franke. Philipp","affiliation":"Forschungszentrum Jülich"}],"author":[{"name":"Hermanns, Alexander","affiliation":"Forschungszentrum Jülich"},{"name":"Lange, Anne Caroline","affiliation":"Forschungszentrum Jülich"},{"name":"Fuchs, Hendrik","affiliation":"Forschungszentrum Jülich"},{"name":"Kowalski, Julia","affiliation":"RWTH Aachen university"},{"name":"Franke. Philipp","affiliation":"Forschungszentrum Jülich"}],"datePublished":"2025-12-04","dateModified":"2025-12-04","version":"1","description":["Preprocessing 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."],"keywords":["Earth and Environmental Sciences","K-Mean","air quality","clustering","Data assimilation"],"citation":[{"@type":"CreativeWork","text":"Hermanns, A.: KSC – Observational Data Clustering Preprocessor, Zenodo [code], https://doi.org/10.5281/zenodo.14711881, 2025.","@id":"https://doi.org/10.5281/zenodo.14711881","identifier":"https://doi.org/10.5281/zenodo.14711881"},{"@type":"CreativeWork","text":"Hermanns, 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.","@id":"https://doi.org/10.5194/gmd-18-9417-2025","identifier":"https://doi.org/10.5194/gmd-18-9417-2025"}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"Jülich DATA","url":"https://data.fz-juelich.de"},"publisher":{"@type":"Organization","name":"Jülich DATA"},"provider":{"@type":"Organization","name":"Jülich DATA"}}