FAIR Data Management is becoming an ever more important aspect within research. Generated datasets need to be findable and reusable so that they can help generate long-term impact and while data repositories such as Edinburgh DataShare or Zenodo are crucial in achieving these criteria by providing secure long term access and a permanent DOI, they are also quite limited in the amount of metadata they store, particularly with regards to finding details about a specific granular material (particles sizes, shapes, etc). This makes it quite difficult for researcher and industrialist alike to find useful datasets across the many different online repositories.

For this reason, we would like to build an Experimental Measurements Database that collates and stores the relevant metadata for the material and test equipment that is missing from repositories, alongside a link to the full dataset. This becomes a searchable database of all available data, enabling the user to find detailed experimental datasets that match their needs. The experimental measurements database will be initially targeted at the TUSAIL (www.tusail.eu) consortium . Ultimately the database aims to serve the wider European and worldwide communities interested in particulate systems.

What is FAIR Data?

FAIR is an acronym for Findable, Accessible, Interoperable, Readable, which are the principles which should apply to scientific data management and guardianship.

Findable: The first part of FAIR and making data re-useable is to make the data findable. Detailed and accurate metadata is key.

Accessible: Accessible data could be openly available or it could require prior authentication and authorisation.

Interoperable: Data needs to be able to be used in different programs or workflows.

Reusable: Well defined data is essential as it makes it easier to understand and therefore use, combine and/or extend the dataset..

"FAIR Principles". The Turing Way project illustration by Scriberia. Used under a CC-BY 4.0 licence. DOI: 10.5281/zenodo.3332807.