FAIRiness
Adequate data management is imperative to ensure that the FAIR principles apply, i.e. that data is findable, accessible, interoperable, and reusable. However, the diversity of the Lasers4EU installations make the implementation of a common data management plan (DMP) quite challenging.
The aim of FAIRiness is therefore to provide concrete support to Lasers4EU partners in their individual transition to FAIR access data management.
Under coordination of EPFL, each Lasers4EU access-providing facility will appoint a representative in order to build a FAIR network.
The duty of this network will be to
(i) gather DMPs and access policies, already in place at Lasers4EU partners or provided by funding agencies, and
(ii) analyze them in order to identify common features, relevant for access experiments and indispensable to ensure easy data accessibility and portability for the multi-instrument access route.
These features will constitute the key ingredients to draft a Lasers4EU access DMP template, which will be confronted with access reality through its use for the curiosity-driven joint experiments, and refined if necessary.
A FAIR help desk will thereafter be established under the coordination of HZDR. It will help the access providers to adapt the above-mentioned template to their specific user experiments and provide recommendations on the available and most suitable metadata formats. This strategy, as well as the collection of the individual DMPs, will constitute the first Lasers4EU data management plan and be regularly updated during the course of the project; its final version will include the access DMP template.
Kick-off-meeting for Lasers4EU Data Management and FAIR representatives
On the 15 January the representatives for Data Management and FAIR of the Lasers4EU consortium met online for the first time to discuss how best to address these issues given the diversity of the facilities involved.
Next, all partners are invited to complete the questionnaire below so that the Task Leaders can collect information on good data management practices in each of the laboratories.