
This training introduces the main concepts, standards, and practical challenges of managing qualitative research data in accordance with the FAIR principles, Open Science practices and compliance with the GDPR. It is aimed at researchers and support staff working with qualitative data, such as interviews, focus groups, and observational materials. The idea is to examine how qualitative data can be responsibly shared, preserved, and reused, taking into account the ethical, legal, and technical dimensions. The topics cover, for example, the practical application of data processing levels like the Jones & Alexander's classification framework and the DANS' "Making Qualitative Data Reusable" guide. The workshop also introduces UM's research data support ecosystem, including the UM Research Data Management Code of Conduct, the Data Steward network, and available institutional tools.
GDPR Compliance, FAIR Principles, Data Sharing, Qualitative Data
GDPR Compliance, FAIR Principles, Data Sharing, Qualitative Data
| 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 |
