In the age of big data and data driven science, the ability to properly manage research data is a key competence any student in the sciences should acquire. Likewise, in order to be able to provide adequate software platforms, students of computer science need to learn about the requirements towards such systems from the domain scientists.

In order to meet both requirements, in this semester, we combined two courses at our university: The class „Management of Scientific Data“ taught as part of our Masters‘ programs in Computer Science and in Computational and Data Science and an introductory class on geoinformatics for first year Bachelor students in Geography.

The students jointly planned a field campaign to obtain vegetation data of a forest plot in the Roda valley close to Jena. The Management of Scientific Data students acted as coaches for the bachelor students, directly applying the knowledge on proper data collection and description in their lectures. They later on used the data for tasks on quality assurance and data analysis. The bachelor students were exposed to the topic of data management in a very early stage of their academic training.

An important aspect is that the data collected was not just „toy data“, but data that is really needed in national and international research projects. The Roda Valley is a designated area for the validation of forest biomass by the European Space Agency. It is used intensively to test and develop new methods in remote sensing. The data collected by the students will serve as the necessary in situ data providing the ground truth.

Based on the positive experience and feedback from this semester, we will extend this combination of classes next year. Students will work together not just in the planning of the field campaign but over the entire course of the semester. We also intend to include Ecology students. Their field work takes place in the same area – integrating their data would considerably increase the value of the data collected and the opportunities for analyses.

With this, we hope to lay the foundation for the training of future data scientists with a background in either a domain science or computer science.