- Data Science Center (DSC), Universität Bremen, Bremen, Germany
With the increasing reliance on data-driven methods in geosciences and the growing popularity of programming languages such as Python and R, equipping researchers with data science skills has never been more critical. The BMBF-funded project “DataNord” addresses this need by establishing an interdisciplinary data competence center for the Bremen region. The center offers researchers from all disciplines, including geosciences, and career stages a wide range of services to enhance their data handling skills throughout the entire data lifecycle.
As part of the U Bremen Research Alliance – a network comprising the University of Bremen and 12 non-university research institutes –, DataNord leverages extensive expertise in research data management and data science. A central element is the University of Bremen’s Data Science Center (DSC), where an interdisciplinary team of data scientists develops training programs and provides consultation services through the DataNord help-desk. Their target audience includes researchers from renowned institutions in the geosciences, such as the Alfred Wegener Institute (AWI), the University of Bremen’s MARUM, the Leibniz Centre for Tropical Marine Research (ZMT), and the Max Planck Institute (MPI). By addressing their specialized needs, DataNord aims to foster a stronger foundation in data science across the geoscientific community.
To connect foundational data science skills with their application in specialized fields like geosciences, DataNord develops domain-specific training modules, for example, focused on analyzing and visualizing climatic and geological time series using Python. These modules use context-relevant datasets and examples, fostering greater engagement and practical impact. However, such domain-specific resources remain underrepresented in many current curricula and online resources.
To address this gap, DataNord has systematically curated open-source digital learning and teaching materials tailored to geoscientists. This includes self-guided training resources, that cover essential data science methods and feature data and code presented alongside tutorials, demonstrations, or combined exercises with solutions – making them directly applicable for researchers in self-study or valuable for teaching in workshops.
By summarizing and offering such readily accessible resources, DataNord aims not only to support more tailored and efficient data science training in higher education but also to reduce redundancy and increase efficiency in developing geoscience-specific training programs across institutions. Ultimately, this approach aims to enhance data literacy in the geosciences and promote sustainable, transparent, and reproducible research across the scientific community.
Finally, domain-specific training material also allows for rethinking the presentation of scientific knowledge in adherence to the FAIR principles – requiring data to be Findable, Accessible, Interoperable, and Reproducible. It exemplifies for researchers how integrating narrative code, data, and methodological explanations (e.g., in Jupyter Notebooks) can result in executable research documents that enable researchers to share their workflows more effectively and, ultimately, become part of promoting data science skills in the geoscience community themselves.
How to cite: Nolte, A., Steinmann, L., and Drechsler, R.: Open Resources for Geoscience Data Science Training: Insights from the Data Competence Center “DataNord”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11877, https://doi.org/10.5194/egusphere-egu25-11877, 2025.