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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
| 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 | |
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| 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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