Review: Results of the NFDI4Ing Community Meeting on RDM for engineering projects

The most recent NFDI4Ing Community Meeting 2023 took place online on 27 October. Talks and presentations were focused on giving an introduction to research data management and data management plans. The meeting was rounded off with interactive workshop sessions in which current research projects served as case studies. The workshops laid the foundation for NFDI4Ing to initiate application-related harmonisation and standardisation efforts.
Some results of the interactive workshop

The most recent Community Meeting 2023 took place online on 27 October 2023 with around 30 participants, including professors, postdocs, doctoral students, librarians, infrastructure employees, and employees in non-university research institutions. After an introduction to research data management (RDM) by Canan Hastik, Jürgen Windeck presented the relevance of data management plans (DMPs) using the NFDI4Ing Research Data Management Organiser (RDMO) tool ( In the interactive workshop session that followed, Philipp Wetterich, Maximilian Kuhr, Manuel Rexer and Michael Frank from the Department of Mechanical Engineering at the Technical University of Darmstadt presented their research projects in a short keynote speech. These are either in the conceptualisation phase, in the approval phase or about to start funding. All projects are strongly application-orientated, industry-related, supra-regional and based on complex data acquisition processes, analysis and processing chains. These projects served as case studies in the three breakout sessions moderated by Jürgen Windeck, Sabine Schönau and Canan Hastik, in which the applicability of RDMO was discussed along with its advantages and disadvantages. In the following paragraphs, we present some of the key contents.

FDM in a Nutshell
The understanding of what constitutes research data is very diverse. Examples range from simulation data, experimental data, transient sensor measurements, measurement data from kinetic models, but also standards, regulations, raw data from databases, machine-readable data sheets, survey data, image, AV and CT data, microscopy images, AI models, and infrastructure topologies (c.f. the term “research data” in the DFG checklist and the guidelines for handling research data).

Introduction to DMPs and RDMO
DMPs support the planning process to improve the reusability and quality of research data. For the engineering community NFDI4Ing provides RDMO, an open source DMP tool widely established in Germany and adapted for the engineering community. RDMO uses templates to guide researchers through a list of topics that need to be considered in RDM. NFDI4Ing developed a template specifically focused on questions relating to engineering research. Some topics included in the template are data usage, documentation and data quality, storage and technical backup during the course of the project, legal aspect, data exchange and permanent accessibility of data, as well as responsibilities and resources. By applying RDMO to the three specific use cases, the workshop offered a great chance to talk about the specific requirements of engineers working in thermal engineering and process engineering, and on varying needs depending on different levels of knowledge and experience.

Using RDMO for engineering projects
The relevance of good research data management is still largely dictated by research funders. The fact that good handling of one’s own data can help researchers to work effectively with their data beyond the project is only slowly becoming apparent to them. The list of benefits the community identified is long: “controlling the chaos”, traceability, transparency, sharing data, reproducibility, reuse and continued use, project management support, sustainability of the use of funds, error prevention, legal certainty, ensure trust in results, optimization of data quality, avoiding misunderstandings, clear referencing, dialogue nationally, internationally and across subject boundaries, fusion of model and data to reduce uncertainty, and sovereignty of research (data is power). However, the misconception persists that filling out data management plans represents an additional burden.

Lessons learned
The workshop provided valuable insights. While enquiries from researchers about RDMO are usually very abstract, these three case studies and the direct project reference in each case offered great added value. As the requirements for RDM differ for projects in the conception and proposal phase, RDMO could act as a generic guidance tool or as a specific project management tool with granular descriptions. Unfortunately, aspects that are relevant for industry collaborations are underrepresented in RDMO. While RDMO already provides a feature to export the DMP, a better support for the application process was desired. Additionally, more best practices and examples should be added to the questionnaire. To this end, we are calling on the NFDI4Ing community to provide the RDMO team with examples. Feel free to contact us at!

This workshop laid the foundation for the NFDI4Ing to initiate application-related harmonisation and standardisation. More information on the workshop content, the process and the results can be found here on Zenodo.

Canan Hastik
Jürgen Windeck
Sabine Schönau