Qualitative social research is characterized by a high degree of diversity. The research strategies and heterogeneous methods chosen for the respective research questions result in research data with very different characteristics. In addition, qualitative data can often not be shared openly due to the sensitivity of its content, which means that the metadata of qualitative data sets is often central to assessing data fit. Metadata helps to contextualize qualitative datasets and creates transparency for researchers interested in reuse. Because qualitative data often contain multiple cases and associated materials, research data centers such as Qualiservice rely on the presentation of micro-metadata that provide information at the data object and case level. FAIR metadata ensures the networking of data objects and metadata with other platforms, the retrieval of datasets in various portals, accessibility for humans and machines and their interoperability. International standards are central to this. But here too, there are still gaps in the ability to describe qualitative data in such a way that important information is available for subsequent use. Using the example of the Research Data Center Qualiservice research data center and the network QualidataNet, we look at the special features and approaches that have been found to describe qualitative data FAIR. We address three levels: 1) the level of micrometadata for information at case level and object level, 2) the controlled vocabulary QualiTerm, which is intended to fill gaps for the description of qualitative data in metadata standards and 3) the level of the DDI-CDI-qualitative working group, which aims at the reusability of non-numerical data via the integrative description of data objects across different disciplines and is developed on the basis of use cases.