introducing the base service measure S-6:
community-based training on enabling data-driven science and FAIR data
Community-based training is key to enable researchers of all engineering disciplines to work successfully in data-driven environments. NFDI4Ing will specifically address the engineering community with trainings tailored towards specific challenges encountered during engineers’ typical research processes, e.g. handling large amounts of data or frequent transitions between empirical and simulation-based research environments. All deliverables will be developed and evaluated in close collaboration with local RDM training and support units at engineering research institutions.
key challenges & objectives
The concepts and materials for training are needed in all NFDI4Ing task areas. Therefore, the needs of the different research areas within engineering sciences have to be identified, formulated and addressed. The purpose of Measure S-6 is to grant access to basic and specialised RDM-training materials and courses.
Task S-6-1: Defining and delivering RDM training contents for engineers
Training contents such as workflow specific RDM guidelines and concepts will originate from the archetypes’ and other task areas’ work and are compiled under the lead of this task. They are structured into (a) topics related to data processes and (b) topics regarding work organisation. NFDI4Ing will deliver the following training contents:
- Data process related topics.
- Data life cycle: from data management planning to archiving research results
- Technologies and tools: from data creation to data archiving
- Handling: from database architectures to models and pre-processing routines
- Work organisational topics.
- Organisational theory for research teams in engineering and data quality management
- Best practises and insights for RDM workflows gathered from community participation
In addition to these already identified training contents, an ongoing requirements analysis of the specific needs of the engineering community is crucial. This is especially important considering the importance of training to increase the awareness towards RDM and drive a cultural change. An open and interactive communication platform for feedback and requirements will be established to support the user-oriented development of training materials and courses. New materials will be published under free licences and thus be provided as Open Educational Resources (OER) for reuse and further development by the members of NFDI4Ing and the entire scientific community.
Task S-6-2: Defining RDM training formats for engineers
NFDI4Ing will offer different formats of RDM trainings for different needs, such as: workshops, short seminars, webinars and other eLearning units. A special focus will be set on workshops carried out in cooperation with the Data and Software Carpentries, a worldwide community teaching foundational coding and data science skills to researchers. A combination of online and face-to-face elements, the so-called blended learning approach, is another format that will be considered as it helps face-to-face training to become much more meaningful in practice – using eLearning material for preparation of the trainees who can keep up in their own pace. In addition, the concept of the FAIR Study Group, which has already been established at TIB Hannover, is a favoured alternative to classical workshop formats. Train-the-trainer measures are a further effective possibility for knowledge management and leveraging resources into the community. Such train-the-trainer workshops will be developed and conducted.
The special interest group (SIG) “Basic FDM-Training and further education” has been founded in order to foster the collaboration between NFDI4Ing archetypes and community clusters as well as external initiatives such as the “TU 9 Community for RDM Training Materials for the Engineering Sciences”. The group is open for all interested parties. You can find more information here (Link).
Further results and training materials will be posted in our repository on GitLab (Link).
The measure S-6 is lead by:
Laboratory for Machine Tools and Production Engineering