Praktikum Digitalisierung: Data Literacy right from the start

Data literacy is a necessary and critical engineering skill. However, integrating this skill into the existing curriculum is not without challenges. Exercises should encourage students to re-sponsibly manage data from the very beginning while they independently plan, execute, and document their research. Students must experience modern Research Data Management (RDM) as an integral part of the scientific method that ensures trust in their scientific and de-sign process through transparency and sustainability. Exercises must also help them to devel-op their analytical and synthetic skills [1]. To accomplish these objectives, the Chair of Fluid Systems developed and validated a new undergraduate course, Praktikum Digitalisierung, extending an established courses on exper-imental work. The students learn digital literacy through a series of design tasks and experi-ments accompanied by FAIR data pipelines [2]. "Kitchen table experiments" serve as prepara-tion for more sophisticated laboratory experiments and allows students to carry out hands-on experiments in their own home, using everyday objects for scientific practice. They are sup-ported by a portable hardware kit, shown in Fig.2, that includes a Raspberry Pi with all neces-sary software and multiple sensors. Four exercises currently form the course: (1) A system design evaluation with FAIR quality KPIs: Students design a vehicle in LeoCAD combining LEGO components from a repository featuring PIDs and semantic metadata. They evaluate the quality of their design by tracking and aggregating the components data and calculating KPIs. (2) Application of temperature sensors to measure temperature histories and determine caloric properties of materials. (3) Using acceleration sensors to measure vibrations of a Laval rotor. (4) A Hele-Shaw cell analy-sis experiment visualizes fluid flow instabilities. Each analysis experiment requires students to integrate digital sensor setups, collect and analyze data (e.g., performing signal processing or image processing on experimental data), and apply statistical methods for interpretation as appropriate. The course leverages a range of software tools within a FAIR data life cycle (Fig. 1) linking data sources to the data sinks. Python is used for data acquisition and analysis, with experi-ments scripted and analyzed in interactive Jupyter notebooks. GitLab is used to control ver-sions of software and setups, and experimental data is stored in structured form using HDF5 files. Semantic graphs link the hardware, datasets, experimental conditions, and results, illus-trating how interoperable metadata can make data more meaningful and reusable while form-ing FAIR Digital Objects or FAIR data products. Praktikum Digitalisierung was developed within the context of the authors' involvement in the NFDI4ING initiative and the associated DALIA training and education platform, serving as a best practice example for engineering and other communities. In conclusion, it demonstrates that introducing FAIR-aligned RDM training into the existing curriculum is not only feasible but highly beneficial. It cultivates a new generation of engineers who are fluent in both the physical principles of their discipline as the management of data that underpins scientific insight, there-by strengthening open, transparent, and reproducible engineering practice from the ground up. References [1] P. Pelz et al., "Datenkompetenz von Anfang an!", 2021, https://doi.org/10.26083/tuprints-00019904. [2] M. D. Wilkinson et al., "The fair guiding principles for scientific data management and stewardship," Scientific data, vol. 3, no. 1, pp. 1–9, 2016. [3] L. Cong, M. M. G. Kuhr, and P. F. Pelz, "Information package about Praktikum Digitalisie-rung," Zenodo, Dec. 10, 2024. [Online]. Available: https://doi.org/10.5281/zenodo.14357857