6. Data literacy


Training and Education in Data Literacy

In accordance with the Data Literacy Charter , the consortium NFDI4Phys views data literacy – the ability to collect, manage, evaluate, and apply data in a critical manner – as a core competence of the 21st century. Hence, our goal is to promote data literacy within the NFDI4Phys community and beyond to foster a sustainable use of data following the FAIR principles and an open-data culture. In order to achieve that, we will develop a comprehensive training and education program for different status groups (from student to professor) covering both interdisciplinary as well as domain-specific skills. We will develop different learning formats including:

  • Hands-on learning in workshops and labs,
  • Module-based learning providing maximum flexibility,
  • Project-based learning dealing with complex real-world topics.

The German Physical Society (Deutsche Physikalische Gesellschaft e. V.; DPG), which is the largest physics society in the world, will be a key partner and act as a multiplier in promoting data literacy throughout the physics community. We plan to host data literacy summer schools for early-career researchers through the DPG, allowing us to reach a large audience. Moreover, in order to raise awareness for data literary across all status including senior scientists and professor, we will introduce data literacy as regular topic at DPG conferences. Additionally, we plan to establish a cross-physics panel on data literacy in collaboration with the other physics-related consortia (PUNCH4NFDI FAIRmat , DAPHNE4NFDI), with the DPG playing an important role in connecting the different consortia. Moreover, we also have the “Young DPG” (jDPG) , a network of students and early-career scientists in the field of physics, as partner, who will organize a PhD symposium on data literacy to raise awareness within their peer-group. At student level, we intend to establish a blue print for a general studies module on data literacy that can easily be implemented at the different NFDI4Phys locations and modified according to local needs.

The interdisciplinary offers addressing skills such as critical thinking, problem solving, computational thinking, data ethics, data citation, data sharing will be developed in close collaboration with other NFDI consortia as part of our engagement in cross-cutting topics. Together with other NFDI consortia, we will collaborate with the in Bremen developed cross-disciplinary training for doctoral candidates in research data management and data science “Data Train”. We will actively contribute to the development of Data Train’s interdisciplinary curriculum and teaching modules. The courses and workshops offered through Data Train can be used as best practices and also implemented at other NFDI4Phys locations. Moreover, the Data Science Center at the University of Bremen will be an important partner in developing and offering courses and workshops for PhDs and Postdocs to advance their data science skills such as programming, model building, data visualization, or advanced analytics (machine learning).


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