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ENVRI-FAIR MS23: First face-to-face FAIR data management training event for ENVRI data centre staff completed

Authors: Hellström, Margareta; Johnsson, Maria; Fiore, Nicola; Vaira, Lucia; Konijn, Jacco;

ENVRI-FAIR MS23: First face-to-face FAIR data management training event for ENVRI data centre staff completed

Abstract

The objective of Work Package 6 is to provide training to ENVRIs and key ENVRI stakeholder groups about the FAIR principles, how to implement them in RI services and data management activities at data centre level, how to evaluate the degree of implementation using FAIR metrics, as well as relevant legal and policy requirements. While Task 6.1 is concerned with preparing and disseminating training resources, the main responsibility of Task 6.2 is to organise and provide training for data centre staff from RIs (both inside and outside of ENVRI-FAIR) on FAIR implementation. The primary target group for the training is staff at the ENVRI data centres, especially those concerned with data management and service architecture. This group will benefit both from self-paced study activities and from tutorial sessions organized e.g. in connection to project collaboration meetings and Webinars. A second important target group for the training is the staff of data centres of key local, regional and national institutions dealing with environmental data. In fact, many RIs are concerned with coordinating and disseminating data products (and services) produced by external contributors. These data provider communities will also benefit from training in FAIR practices. To promote this, tutorial sessions will be organized in connection to domain-specific conferences.

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Keywords

training event, service architecture, milestones, ENVRI-FAIR, data management, data centre staff

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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