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This research data management plan (RDMP) describes how data is handled in the Meaningful Data Counts research project. The project is led by PI Stefanie Haustein and Co-PI Isabella Peters and funded by the Alfred P. Sloan Foundation. It uses mixed social sciences research methods, producing quantitative as well as qualitative datasets. As both PIs practice open science and scholarship, the RDMP puts an emphasis on data sharing and reuse. All data, software code and milestone working documents will be made available freely in online repositories throughout the research project and all publications will be published open access. The RDMP is structured and based on a template created for the Portage Network and is a living document that is expected to be revised as the project evolves.
Research data management plan for Alfred P. Sloan Foundation Grant "to explore the extension of bibliometric analysis to the understanding of research data practices" (Meaningful Data Counts)
RDMP, research data management
RDMP, research data management
| 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 |
| views | 91 | |
| downloads | 100 |

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