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During this demo session of the Open Science Fair conference, the new trainer functionality of the FAIR-Aware tool was launched. FAIR-Aware (https://fairaware.dans.knaw.nl/) is an online self-assessment tool that helps users assess their knowledge on how to make their data(set) FAIR before depositing it in a repository. It is the first step in creating FAIR data, focussing on fostering awareness and willingness to comply with the FAIR data principles. The tool is discipline-agnostic and useful for anyone working with data (e.g., researchers, data stewards, research performing organisations, or funders), which makes it suitable for training in any context. Using the new trainer functionality, you can now easily use the tool for your own user bases and quickly and easily interpret the results. The demo session showed the audience how to access and use the functionality from start to finish. Relevant materials are linked in the slides. Please contact the email address linked on the slides for questions about (the new functionality of) the tool.
FAIRsFAIR, FAIR-Aware, FAIR Awareness, Training, Tool, Open Science Fair 2021
FAIRsFAIR, FAIR-Aware, FAIR Awareness, Training, Tool, Open Science Fair 2021
| 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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