FAIR data case studies

FAIR Data Habits in Engineering Design – Case Study

Engineering Sector: Engineering Design (especially design methodology), focus on machine components, tests and experiments on structural robustness and deformation
Community preferences: Tools: PDM systems, e.g. Windchill
Format: Data sets include information on corporate structure; cross linking of machine parts and subparts, rationalization of workflows.Data on methodology is typically captured in SML, XQL files;
Data generated by machines are typically stored in proprietary file formats. Sharing: Via PDM system
Overall FAIRness: ORANGE
F No
A Partially
I No
R No


In this case study, the research focus is on engineering design. This field of research is typically characterized by a process chain: the researchers receive input from a client (company). This might be analogous or digital data as well as physical data from measurements; the findings need to be generalized for simulations and eventually result in nominal values for a component that is to be manufactured; to support this data to machine tools are prepared as well.

A primary IT system that supports the researchers is a product data management (PDM) system (for example Windchill). It is mainly intended to capture details of technical objects and allows these data to be combined with other analog or digital data or metadata such as PDFs, scans, TIFF, JPEG .

Storage capacity is up to petabyte level where data at a project level typically range within terabytes.


The researcher clearly states that data from industrial partners are confidential. They have to be ciphered (CAD and model data) and secured by access control measures in the systems such as role-based access in PDM systems such as Windchill. Data to third parties have to be obfuscated, i.e. only the smallest part of the data set is accurate, as to the rest accuracy is reduced. The ownership of data is stipulated by contract and data are  mostly subject to intellectual property rights, e.g. patents and treatment is covered in the contract between researchers and the company.
Due to this,  amost no (raw) data  is published. Publishing data onyl goes as far as it is part of the description in an academic paper.



“There is absolutely no way to publish data owned by a company.”

“To make research data accessible through metadata means a lot of additional work, taking into account that data are growing in size and quality. And, even more important: understanding  and interpreting research data needs complementary knowledge of the research and production processes.”

“Data and processes have to be interpreted within their specific context.”

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