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We propose a first version of an assessment framework for the FAIRness of services, together with a process to refine this model including community consultation with a view to finalizing it in August 2021. Aimed at a target audience of data service owners, the model contains concrete recommendations to improve technical aspects of services (FAIR enablement, Quality of service, Openness & Connectivity) as well as more social aspects of services (User centricity, Trustworthiness and Ethical & Legal aspects). Input was gathered from interviews with service owners, a virtual workshop held at the EOSC-hub conference earlier this year and from the recent literature on FAIR services and interoperability. The bibliography includes a series of references that have been mined for input on the suggestions in our model, and the input documents are summarized in an Annex. Feedback and suggestions for improvement will be most welcome as comments on the public Google Docs version of this report: https://docs.google.com/document/d/1wPesjAIeaOwfAT80ykK6xVvk4tVD0CDkfAEAa1VOGCA/
Research Data Management, Data services, Service management, FAIR
Research Data Management, Data services, Service management, FAIR
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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