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This report provides a summary of the three schools run for early career researchers (ECRs) during the FAIRsFAIR project. The curriculum provides a broad but shallow introduction to the necessary technical and social skills in Data Science for Early Career Researchers. In particular there are modules in Open and Responsible Research, Software Carpentry (the Unix command line, R or Python, git), Research Data Management, Visualisation, Information Security, Machine Learning, Author Carpentry and Computational Infrastructures. The schools were originally designed to be an intensive two week school run on a face to face basis.
Data Skills, Training
Data Skills, Training
| 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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| downloads | 16 |

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