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This workshop aimed to train junior scientists in implementing the FAIR principles for research data & software management & development. We want to help you identify similarities and differences between these two scientific objects and apply respectively appropriate good practices in preparing, publishing and archiving your work. It was a new, experimental workshop format that contextualises the highly practical lesson material from the Software and Data Carpentries with the FAIR principles. Find more details on TIBHannover.GitHub.io/2018-07-09-FAIR-Data-and-Software.
Editable versions of the slides are available on https://drive.google.com/drive/folders/1JO-0SjKw52ICbNSHZuqdXjdIZIrh3Alp.
FAIR Data, GitHub, R, FAIR Software, Git, Carpentries, Python
FAIR Data, GitHub, R, FAIR Software, Git, Carpentries, Python
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 |
views | 70 | |
downloads | 150 |