The field of Materials Science and Engineering undergoes a digitization process that establishes novel technologies and new ways to work. This however, simultaneously requires evolution and adaption of the users’ digital skills.

Today, scientists face various challenges when attempting to generate and share FAIR research data. Implementation of high-level recommendations, such as the FAIR principles [1], requires prior knowledge and a set of technical skills, which are typically not part of the academic education. A survey of the scientific community in 2021 [2] revealed that more than 45% of researchers have little to no prior knowledge of the FAIR principles. Only 35% document their research data in a structured way. However, their interest in training and educational resources on the matter was high (91,7%). Consequently, the realization of FAIR research data requires educational resources that can help researchers to increase their data literacy and technical skill.

We therefore created a metadata training course covering fundamental aspects of data and metadata, targeted towards PhD candidates and early-career researchers. The material covers the basics of (semi-)structured metadata, schemas and standards, and persistent identifiers (PIDs). Our didactic concept stands out through its hands-on and storytelling nature. Familiar problems and interrelated tasks encourage participants to explore newly acquired skills and concepts. This prepares the learners to adapt the learning outcomes to their everyday metadata practices. The course material is designed in a modular fashion to allow easy adaptation, both depending on the participants skill level or towards a specific scientific domain - such as the here presented adaption towards Materials Science and Engineering.

The material was used for courses in 10 occasions (also during MatWerk Summer and Winter Schools). The anonymous post-hoc evaluation among participants attests both high interest as well as high quality of our material. A high interest in the course and its material by Data Stewards highlights the need for fundamental education in the field of (meta)data literacy. A scientific domain-agnostic version of the course material was published over The Carpentries Incubator [3].

References
[1]: https://doi.org/10.1038/sdata.2016.18
[2]: https://doi.org/10.3289/HMC_publ_05
[3]: https://zenodo.org/doi/10.5281/zenodo.10091707