Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties - Source Code

DOI

This source code contains all methods that is being used in ozone mapping project. In addition, it contains scripts to run both explainable AI methods and methods used to study the impact of uncertainties.

Current developments can be tracked in the gitlab repository https://gitlab.jsc.fz-juelich.de/esde/machine-learning/ozone-mapping

This resource contains our complete source code in a zip archive(ozone-mapping.zip), a readme file (README.md) and setup directory(ozone-mapping-setup.zip) which contains requirements file to run in own system or on our cluster.

Identifier
DOI https://doi.org/10.34730/af084443e1c444feb12d83a93a65fa33
Source https://b2share.fz-juelich.de/records/af084443e1c444feb12d83a93a65fa33
Metadata Access https://b2share.fz-juelich.de/api/oai2d?verb=GetRecord&metadataPrefix=eudatcore&identifier=oai:b2share.fz-juelich.de:b2rec/af084443e1c444feb12d83a93a65fa33
Provenance
Creator Betancourt, Clara; Stomberg, Timo; Edrich, Ann-Kathrin; Patnala, Ankit; Stadtler, Scarlet
Publisher EUDAT B2SHARE
Publication Year 2022
Rights The MIT License (MIT); info:eu-repo/semantics/openAccess
OpenAccess true
Contact c.betancourt(at)fz-juelich.de
Representation
Language English
Resource Type Software
Format md; zip
Size 11.2 MB; 3 files
Discipline 3.3.14 → Earth sciences → Meteorology; 4.1.17.1.2.1 → Machine learning → Artificial neural network; 4.1.13 → Computer sciences → Software engineering