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.

Beyond support, the DataInitiative actively develops and operates key components of the research data infrastructure at KIT-Campus Alpin to enable public access to research data. This includes building and maintaining software interfaces, services, and repositories that facilitate interoperability with higher-level national and international platforms. 

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.

We develop innovative solutions in data science, machine learning (ML), and artificial intelligence (AI), tailored specifically for environmental research. Our mission is to ensure excellent data quality, reproducibility, and efficient data management—from field station measurements to automated forecasting and visualization from environmental and climate models.

We actively promote good scientific practice through transparent, efficient processes using modern programming languages and automation tools. Our team supports researchers in data exploration, visualization, advanced analysis, and developing applied ML systems.

Another focus of our work is the use and research of data-driven methods in machine learning and artificial intelligence, achieved through close collaborations with researchers across the KIT Campus Alpin and external partners. The extensive environmental data collected through field measurements, numerical simulations, and remote sensing creates valuable opportunities to gain new insights by intersecting and analyzing this information with modern AI techniques.