The scientists at KIT-Campus Alpin record and produce a large range of research data. These span from continuous field observation data from the TERENO-PreAlpine Observatory and the mobile observation observatory MOSES to extensive climate model data and remote sensing data with high spatial and temporal resolutions. The KIT-Campus Alpin DataInitiative develops and implements best practices and guidelines for a modern research data management that enables a sustainable and efficient use of this data throughout the entire data life cycle. We place a major focus in implementing the FAIR principles and the integration with overarching national and international data infrastructures. The data initiative is the link to the AG Data management of the Institute of Meteorology and Climate Research at KIT, the DataHub of the Helmholtz Research field Earth and Environment, and to the National Research Data Infrastructure Germany (NFDI) through the NFDI4Earth-Initiative.

In addition, we provide data management as-a-service and support the scientists at KIT-Campus Alpin in the development of research software, the establishment of data management plans (DMPs), the publication of research data and the use of meta data standards. By active support of research projects we enable the efficient use of our research data infrastructure at KIT-Campus Alpin.

In addition to data management, the KIT-Campus Alpin DataInitiative aims to establish new tools and solutions originating from the field of data science, machine learning and artificial intelligence in the context of atmospheric environmental research. By utilizing state-of-the-art software and data engineering techniques, we provide and assist in delivering data quality control solutions, reproducible science and data handling from field station data ingestion to automated processing of data originating from climate and environmental models. Furthermore, we strive to enhance efficiency and data quality, reproducibility and transparency by teaching our scientists programmatic data analysis through code and automations. We assist in the development of data exploration tools and viewers, data interfaces, and advanced data analysis in general.

Another focus of our work is the exploration and utilization of data-driven methods of machine learning and artificial intelligence through collaborations with scientists from all KIT-Campus Alpin departments and external partners. The large amounts of environmental data recorded at field stations, produced through numeric simulations or obtained via remote sensing provide a rich source of data that can be combined and investigated with these methods in new ways.