FAIR for Qualitative Data

Course Description

The following is a course for researchers working with qualitative research material and willing to explore how FAIR (Findable-Accessible-Inter-operable-Re-usable) could perhaps fit their work. The course will be informative but also hands-on and practice base.

The course covers the basics of FAIR practices and helps qualitative researchers to think how far and if the FAIR principles could be applied to their work. The course is a co-constructive exercise, so your work and active participation will be key to keep designing the course as I move along wmy work as a data steward at the faculty.

Learning objectives :

1. Theoretically discuss FAIR and its possible limits for non-readable machine research data.
2. In breakout rooms look into FAIR repositories with qualitative data and constructively be critical to the researcher’s approaches in terms of becoming findable and accessible.We will use a real case scenario to explore how inter-operability and re-usability can become useful aids while working with teams of qualitative researchers.

 Target group: Researchers at Maastricht University Working with Qualitative Data

 Language: English

Maximum number of participants: 15