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Through this video, researchers can learn how to make data Findable, Accessible, Interoperable, and Reusable (FAIR), and how to assess the FAIRness of research data and which tools to use to make data more FAIR. his is demonstrated by assessing the “Monitoring of Young people´s Lifestyle and Everyday life (MULD) dataset (link to http://dda.dk/catalogue/25075?lang=da) ) using the FAIR self-assessment tool developed by the Australian Research Data Commons (link to https://ardc.edu.au/resources/working...). The video, realized by CESSDA Training experts, builds on Chapter 1 "Plan" of the CESSDA Data Management Expert Guide (DOI: 10.5281/zenodo.3820473). The video can be watched also on YouTube. Concept & script: Lea Sztuk Haahr ~ Elly Dijk ~ Ellen Leenarts ~ Veerle Van den Eynden Music: « Adventure » from Bensound.com Visuals: digitalbevaring.dk ~ vidensportal.deic.dk/FAIR ~ www.cessda.eu/DMGuide Voices: Christine Dorisamy Pillai ~ Oliver Parkes ~ Katie Layley ~ Veerle Van den Eynden ~ Anca Vlad ~ Hervé L’Hours Production: Veerle Van den Eynden
FAIR Data, DMEG, RDM, Research Data Management
FAIR Data, DMEG, RDM, Research Data Management
| citations 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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