FAIR data case studies

FAIR data habits of a Molecular Thermodynamics Researcher – Case Study

Engineering Sector: Molecular Thermodynamics
Community preferences: GitLab
Gromacs
Overall FAIRness: ORANGE
F No
A Partially
I Partially
R Partially

Overall FAIRness visualisation:

AERO

 

Context:

The research projects focus on the prediction of material properties of nano-crystalline structures using computerbased molecular simulations. The raw data usually comes in several hundred GB including trajectories and energy histograms. This research type does not handle sensitive or personal data, and includes raw data, filtered data, analysed data as well as plots.

Insight:

The researchers in this case study from the discipline of Molecular Thermodynamics often use open software tools to process and analyse their data. Data is published if there are no further studies within the own group on the data, usally as supplementary material following a journal publication.

The researchers highlight the difficulties in managing research data, which especially relates to difficulties to archive even the own data in a way that data is findable and understandable over time. With regards to re-usability / verification of work, they say it is even more difficult for other people in the field to work with that data due to the high level of complexity. Overall, there is a positive attitude towards FAIR data and data publication, but they highlight the need of easy accessibility of appropriate repositories, a low entry barrier, and a data description standard that is provided and accepted by the scientific community in this field.

Highlight:

Data is published if there are no further studies within the own group on the data

There is no description standard. It’s difficult to archive even the own data in a way that data is findable and understandable over time.

 

 

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