The preprint of the article “AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics” by Clara Betancourt et al. is now in the public discussion phase. Creating the benchmark dataset and writing the paper was a first collaboration of the KISTE members from JSC and University of Bonn. The study was partly founded by the IntelliAQ Project (https://intelliaq.eu). The manuscript is available at https://essd.copernicus.org/preprints/essd-2020-380/. It has been accepted for public discussion by the Inter-Journal Special issue “Benchmark datasets and machine learning algorithms for Earth system science data” of the Journals Earth System Science Data and Geoscientific Model Development.
The AQ-Bench dataset contains air quality data and metadata at more than 5500 air quality observation stations all over the world. It offers a low-threshold entrance to machine learning on a real world environmental dataset. The dataset itself is available at https://b2share.eudat.eu/records/30d42b5a87344e82855a486bf2123e9f . To start machine learning on the AQ-Bench dataset directly in your browser, visit the code repository (https://gitlab.version.fz-juelich.de/toar/ozone-mapping) and launch the binder!
AQ-Bench preprint available for public discussion
