Community-based RDM trainings portfolio for the engineering sciences
Creators: name: Galdino, Marcos
name: Hastik, Canan
name: Hecker, David
name: Langenbach, Christian
name: Moser, Mario
name: Neumann, Janna
name: Trautwein-Bruns, Ute
name: Wedlich-Zachodin, Kerstin
Publisher: name: Zenodo
Year: 2024
Subjects: RDM
Research Data Management
NFDI4Ing
Training
Education

description: Abstract

With research becoming more data-driven, the sustainable handling of research data has gained more importance during the last years. Funding agencies are increasingly demanding a dedicated Research Data Management (RDM) in research projects. However, next to the additional effort, researchers need the respective skills and competencies to handle and manage data collected and used in their research.

In the course of this development, the RDM community has already conducted various trainings and created several training materials. Especially self-paced format such as Open Educational Resources (OER) and Massive Open Online Courses (MOOCs) are chosen due to best scalability and temporal and spatial independence in learning. The covered training topics are typically rather generic to be applicable for a large group of researchers. Nevertheless, there is a need for discipline-specific trainings to gain increased acceptance in the respective subject-specific community and to address particular aspects and challenges of them. In NFDI4Ing, the National Research Data Infrastructure for the Engineering Sciences, this need has been raised by the engineering community.

Such demand is comprehensible but implies challenges in conception and realisation: Typically, communities from one large field are rarely homogeneous. For the engineering sciences, this ranges from production technology to material sciences to civil engineering. Therefore, it requires adjusting the generic training contents to the respective subject accordingly. A second (general) challenge is to consider is the diverse experience and competency levels as well as learning objectives for each target groups (like bachelor, master, PhD student, data steward etc.).

In NFDI4Ing the Base Service S-6 together with the Community Cluster CC-2 are responsible for preparing trainings for basic RDM topics specifically designed for the engineering sciences. Learning objectives have been defined according to the competency levels (rf. matrix in https://doi.org/10.5281/zenodo.7034477). In their process of creating learning materials, the S-6/CC-2 team uses the following methodical approach: First, existing (generic) RDM trainings have been identified. These are presented in an overview list and are reused – according to the respective license – as foundation for the preparation of engineering-specific trainings. Next to the training contents itself, various training formats (like self-paced trainings, workshops, train-the-trainer etc.) and target groups have been identified and defined. This has been the starting point for the creation and publication of first (engineering-specific) self-paced RDM trainings in NFDI4Ing’s Education Repository [https://education.nfdi4ing.de].

Since the NFDI4Ing S-6/CC-2 team cannot cover all engineering research disciplines in detail by their own, a community-based approach is chosen: The created self-paced trainings are discussed with everyone interested in a so called “Special Interested Group” (SIG) for “Basic RDM training”. For each of these meetings with the community a topic is chosen and the respective existing training is presented. Based on this, participants are asked for feedback and suggestions. This aims to develop RDM trainings tailored to the needs of the engineering science’s community and to enrich (previously rather generic) trainings with an engineering perspective. In terms of the engineering-specific orientation, the community is asked for examples or anecdotes to illustrate the content practically. Even if it is not expected that such examples are applicable for everyone in the heterogeneous engineering community, they represent the community’s individual needs and appear helpful to personally identify with the topics. Integrating negative examples – sometimes referred to as “data horror stories” – into the trainings is a way to share experiences and to highlight potential consequences when (not) following the recommended training contents. Representation formats for all these examples can range from short texts to audio statements, animations, or videos integrated in the web-based trainings. Beyond this, a special focus is currently on adding interactive elements to the trainings. Quizzes, assignment tasks and check questions are also used as diversified learning control and to reinforce the taught contents. Technically these are H5P elements, a format for various interactive elements, which can be exported for websites.

In summary, the community-based approach enables to provide basic RDM trainings specific for the community of the engineering sciences and to align on the community’s needs. The trainings are incrementally and continuously updated.

Next to the engineering community, the S-6/CC-2 team is networking with other training initiatives to make the trainings publicly known and to exchange experiences, like in the cross-consortia’s NFDI Section “Education & Training”. With platforms like DALIA (Data Literacy Alliance) as well as OERSI (Open Educational Resources Search Index) an enhanced dissemination of the trainings is intended.

In this conference talk we present our service offerings as well as our approach how the integrated the community from the engineering sciences into the development of discipline specific RDM trainings. The presentation will conclude with a demonstration of our training repository and an outlook to the future next steps. Everyone from the engineering community as well as from infrastructure services (like IT centres and libraries) is invited to contribute on a subject, generic or didactical level. 

 

Conference Website

NFDI4Ing Conference 2023 [https://nfdi4ing.de/conference-2023/]

 

Acknowledgement

"The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713."