The archetype concept.
Hello, I’m Frank.
“Hello, I’m Frank. I’m an engineer who works with a range of different and heterogeneous data sources – from the collection of data from test persons up to manufacturing networks. One of the main challenges during my research process is the synchronisation and access management of data that is generated at different sources simultaneously.
My professional background is mostly informed by production engineering, industrial engineering, ergonomics, business engineering, product design and mechanical design, automation engineering, process engineering, civil engineering and transportation science.
Key challenges and objectives
Key challenges for the archetype Frank arise from (a) the variety of involved engineering disciplines and (b) from the collaborative nature of working with many participants. These challenges can be broken down further:
a.1) Diversity of raw data: Data differs greatly in its level of structure in contrast to sensory data and software data. Frank must record, store, describe, analyse and publish a great variety of file types.
a.2) Recording methodology: The need for efficient documentation of recording methodologies, description of research environments and study setups are identified as an integral part of the data set.
b.1) Common language: Regarding collaboration with many participants and due to the variety of disciplines, a standardised vocabulary in terms of discipline-specific issues as well as research methodologies is required but not yet established.
b.2) Anonymisation and access: Research in engineering often involves working with corporate and confidential data. Therefore, anonymisation schemes for distributed data are required. Access to stored data must be restricted, protected and managed in a scalable manner.
Approach & measures
The archetype Frank is split into separate measures that build on each other:
Measure F-1 – Target process specification: Based on existing definitions of RDM process frameworks, we consolidate, adapt and test those in terms of Frank’s research activities. We use an iterative process development approach in order to elaborate a specific guideline and decision support regarding how to handle RDM for Frank step-by-step.
Measure F-2 – Technological feasibility and decision-making: Whereas measure F-1 defines the underlying needs that arise from current implementation and practicability problems, measure F-2 examines which technologies at hand can be used for FDM.
Measure F-3 – Design concept of an application program interface: With the previously identified implementation problems regarding RDM practice as well as feasible technological options at hand, a general concept of a program interface must be designed. This concept comprises how to combine and link several technologies.
Measure F-4 – Incentivization of an active and interdisciplinary RDM use: For Frank, it is necessary to consider organisational measures, as Frank’s projects often involve many researchers in an interdisciplinary setting. Incentivization is one of the organisational measures, which ensures compliance with the above-developed RDM target process.
In context of Measure F-1, we already have collected various frameworks, all with varying depth and purpose. Those frameworks have been assessed, judged and broken down in order to identify the general requirements of a RDM-framework. Furthermore, laws and guidelines have been taken into account to meet the requirements of a universally valid framework that will guide the user through all standards and specifications issued by institutions such as the DFG.
In addition, the needs of researchers must not be lost sight of. In order to fulfil this goal, an exploratory survey was carried out between October 2020 and January 2021. The survey was distributed, among others, by the German Academic Association for Production Technology (WGP) and the Fraunhofer Group for Production. Besides questioning the status quo of RDM, it was also asked for specific wishes and needs. In addition, several focus group interviews with representatives of the participants and use cases in the task area Frank, e.g. the Cluster of Excellence Internet of Production at RWTH Aachen University, and research groups at TU Munich and TU Berlin, are currently taking place to further investigate the needs of researchers in detail. In these interviews, the participants are being asked about their daily workflows and in how far they already implement RDM. The results of both survey and interviews will be published in summer 2021.
The task area is lead by:
For general Information on Frank please contact: