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Agreed sets of metrics should be implemented and monitored to track changes in the FAIRness of data sets or data-related resources over time. In this respect, the development of FAIR Compliant tools and services should meet the needs of data producers and users. During this session, chaired by Mike Priddy with rapporteuring support by Maaike Verburg, DANS, participants were asked to reply to the following questions: What does your project or initiative do to implement metrics? If your project, initiative, community or institution use tools to assess the FAIRness of datasets which do you use? Are you aware of tools and metrics that are used to assess software FAIRness and are you utilising them in your project or initiative? Which tool(s) and metrics? Are you aware of tools and metrics that are used to assess semantic artefacts FAIRness and are you utilising them in your project or initiative? Around 40 participants joined the session from a large variety of projects, initiatives, as well as the EOSC-A TFs creating a lively conversation.
FAIR, FAIR-IMPACT, FAIRness
FAIR, FAIR-IMPACT, FAIRness
| 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 |
| views | 15 | |
| downloads | 15 |

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