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One challenge researchers currently have is the lack of means to manage research data directly where it is generated, stored, or utilized; in other terms digital research environments that run on workstations or virtual machines located some where in the Cloud. Such environments are well connected with certain infrastructure such as code repositories. By using tools such as git, existing research software can be easily cloned, or researchers can create their own code repositories to share their research software. However, this is not the case for research data. faily Python package and JupiterFAIR JupyterLab extension aims to lessen the gap and to allow researchers to easily create and quickly publish research datasets to popular research data repositories, such as Zenodo, figshare, and 4TU.ResearchData.
data publishing, software development, research software, research data management, research data, data repository
data publishing, software development, research software, research data management, research data, data repository
| 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 | 3 |

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