Veranstaltungsprogramm

Universities often offer general data literacy courses for students, but these are usually voluntary and lack discipline-specific relevance. The aim must be to provide all students with basic digital and data literacy skills, thus creating the conditions for sustainable scientific work and a digitised job market.

There are a number of challenges in establishing data literacy courses within a degree programme. Changes to module catalogues and study regulations could allow for the establishment of independent modules on data literacy. However, this needs to be coordinated within faculties and is often a lengthy process. Alternatively, new digital techniques can be integrated directly into existing formats. In any case, content alignment of topics and teaching formats is necessary, as well as the commitment of educators.

NFDI4Chem aims to integrate research data management (RDM) into chemistry curricula as an essential element of data literacy. As a first step, the Study Commission of the German Chemical Society (Gesellschaft Deutscher Chemiker, GDCh) updated its recommendations for bachelor's degree programmes in chemistry at universities in 2021. These explicitly address the development of skills in data science, which includes scientific programming, data analysis and modelling, research data management and cheminformatics methods.

However, the delivery, focus and implementation can vary from one institution to another. Differences are particularly evident in the implementation phase in terms of course content and structure, target groups and modules, and additional offerings. Some institutions, such as RWTH Aachen or RPTU Kaiserslautern-Landau, have already introduced electronic lab notebooks (ELNs) in practical courses and implemented their use in their curricula. Others, such as the University of Ulm and the Friedrich Schiller University of Jena, are planning similar steps.

In addition to the use of ELNs, the teaching of RDM skills in lectures is also a focal point. Modern interactive teaching methods are used, including digital tools such as Jupyter notebooks, Git or Moodle courses. The University of Ulm and the Georg-August-University of Göttingen offer Python-based interactive lectures on data collection and analysis, in addition to basic RDM knowledge such as FAIR principles and metadata management. Another example highlights the differences in content depending on the degree programme; for example, the Friedrich Schiller University of Jena combines analytical chemistry teaching with a seminar on good scientific practice, while TU Dortmund has introduced a compulsory statistical methods module for bachelor students. The University of Ulm will address fundamental aspects of RDM in a cross-disciplinary way, alongside engineering and physics, as there is often a fluid technical overlap between these fields today.

This contribution presents and discusses institutional strategies for implementing chemistry-specific data literacy. Several practical examples are used to illustrate how different universities in Germany teach data literacy in chemistry. It addresses different thematic aspects and their forms of integration, and presents concepts for Bachelor's and Master's programmes that can be easily adopted and adapted by other locations and departments with minimal effort. In this context, NFDI4Chem acts as a mediator and central contact point where individual approaches are documented for other institutes.

DataNord is an interdisciplinary data competence center for the Bremen region, established as part of the U Bremen Research Alliance (UBRA) – a regional collaboration network of the University of Bremen and twelve non-university research institutes. It serves as a hub for cross-disciplinary and interinstitutional data science learning, research, and networking in the Bremen region. DataNord is one of eleven data competence centers funded by the German Federal Ministry of Education and Research (BMBF) as part of a nationwide initiative.

Bringing together universities, non-university research institutions, state institutes, NFDI consortia, and infrastructure centers, DataNord consolidates comprehensive expertise in research data management and data science. Its scientific profile areas include: (1) Environmental and Marine Sciences, (2) Social Sciences, (3) Material and Engineering Sciences, (4) Health Sciences, and (5) Humanities.

DataNord provides training and support for researchers at all career levels to enhance their data skills. Its comprehensive offers include trainings, consulting services, hackathons, summer schools, networking opportunities, self-learning materials, and research projects. The entire data lifecycle is considered, from data collection and management to analysis and critical evaluation of hypotheses and results, while taking ethical, legal, and social aspects into account.

Two central pillars of DataNord are the Data Science Center (DSC) at the University of Bremen and the interdisciplinary doctoral training program "Data Train – Training in Research Data Management and Data Science" of the UBRA. At the DSC, an interdisciplinary team of data scientists was established providing consulting services and flexible training modules, tailored to researchers with varying levels of expertise from all five scientific focus areas. The Data Train programme has successfully provided foundational data science education since 2021 and will be expanded as part of DataNord. It features three structured learning tracks – the Starter Track with introductory courses and two hands-on Operator Tracks “Data Steward” and “Data Scientist”.

Additionally, DataNord hosts a variety of networking events and community activities designed to facilitate collaboration and knowledge transfer. These include “Data Snacks”, “Data Stories”, “Hacky Hours”, “Data Factories”, the “Data Community Club”, and the “Research Data Day”. These initiatives target different audiences, from researchers to the broader public. Moreover, the “Research Academy” integrates data literacy directly into research processes, while a Citizen Science project and science communication initiatives further drive the transfer of knowledge and technology into society, industry, and policymaking.

With its interdisciplinary approach, DataNord leverages Bremen’s existing data infrastructures and networks to build bridges between disciplines. This fosters new collaborations in research and education and promotes knowledge transfer beyond the region.

In our presentation, we introduce DataNord as an inter-institutional solution for promoting data skills and accelerating the cultural shift towards a FAIR data culture. We share insights from our experience, highlight best practices, and discuss challenges.

Furthermore, we explore DataNord’s role within Bremen’s data ecosystem and its significance in Germany’s broader research landscape. With its interdisciplinary focus and extensive collaborations – including partnerships with eleven NFDI consortia – DataNord serves as a scalable model and best-practice for other regions in Germany.