FAIR data case studies

FAIR data habits of Mechanical & Material Engineering Researcher – Case Study

Engineering Sector: Mechanical and Materials Engineering
 

Community preferences:

MP4
TIFF
DICOM  Format
Adobe File Formats
Excel Spreadsheet
MRI File Formats
CT File Formats
Overall FAIRness: ORANGE
F No
A No
I Partially
R Yes

Overall FAIRness visualisation:

MME

 

Context:

Mechanical Engineering covers a wide range of topics, but mainly focus on efficiency and effectiveness of technologies, materials, and resourcing. Bio-mechanical Engineering as part of this discipline, includes research in medical device design, bio-mechanic, bio-material, and neuro-engineering. The main incentive is to apply engineering knowledge in combination with technology to find solutions for healthcare questions.

Insight:

Visual and audiovisual data types are predominant in the case study from this field of research, with file formats ranging from commonly used ones such as TIFF and MP4 to data specific to MRI (Magnetic resonance imaging) and CT (computed tomography scan)  approaches. Data itself is usually shared via conventional peer-reviewed literature, since cleaning tasks related to publishing data outside of publications entails plenty of effort and resources. With regards to re-usability / verification of work, the intellectual value of contextual information was emphasized. Data with no contextual data or knowledge connected is not useful for new research. A fear of intellectual exploitation by others through published data that is not connected to their own publication is dominant in this community, however the principle of data management and data publishing is welcomed.

Highlight:

But if a person is more motivated to publish or perish it’s the analysis of the data set and the synthesis of it and the interpretation of it that has more lasting value in many people’s minds. Even if you think in terms of say,promotions…if you’ve got a peer-reviewed journal publication, that’s important. If you have gathered a set of data, well you’ve just gathered some data and made it freely available – any monkey could have done that.

The reality is of course it’s a non-trivial task to gather the data. The hard part is gathering it but probably where the intellectual value comes is in the ordering of the data so that it does have real value.

 

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