This record contains data for the manuscript "O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone in rural and suburban environment" by L. H. Leufen, F. Kleinert and M. G. Schultz.

We provide the processed input data, the forecasts made by O3ResNet, and the O3ResNet model.

To have a better insight into the data and model, we provide a ready-to-run jupyter notebook to load and visualize our data and results. To run this notebook we rely on docker. Download the docker file (leufen-docker.tar.gz), the basic data file (leufen-data-base.tar.gz) and the model file (leufen-model.tar.gz) and follow the instructions (instructions.md and instructions.pdf) to load data, model, and the notebook. Note that changes made by the user to the notebook well be removed on exit as long the option "--rm" is present. When following the instructions, it is not required to unpack the input data files (leufen-data-.tar.gz). If you encount issues with disk space limits and docker, it is always possible to use a reduced number of data files or to unpack data for a single country and just parse them to the docker container. When using a windows host system, some commands provided in the instructions might slightly deviate.