Knowledge transfer and science communication are firmly anchored in the mission statement and organizational structure of GESIS. Research content and results, scientific expertise and infrastructure offers are accessible not only to social science experts but also to disciplines and social actors from outside the field. The transfer of knowledge at GESIS is based on the results of excellent research and the infrastructure services that build on these results. This applies in particular to the core competencies of GESIS in the area of research data.
In the social science field, GESIS has nationally and internationally proven expertise in the methods of empirical social research with a focus on survey design and methodology. Thematically, the staff conducts research on digital media, political participation, values and attitudes, migration, social differentiation and social change. A further focus is on educational research and gender relations in science. In the field of computer science, digital offerings for the social sciences are developed on the basis of innovative knowledge technologies in order to make them compatible with the latest technological developments. Research here focuses on applied computer science, especially text and data mining, information linking and retrieval, and network science. In addition, the development of methods and tools for the indexing, enrichment and analysis of digital behavioural data is underway. In the area of research data management, GESIS is concerned with, among other things, the secondary use of data and data security as well as the creation of data documentation standards, the networking of data and the handling of new data types.
Due to the increasing specialisation of departments, transdisciplinary research is becoming more and more important. For this reason, GESIS participates in many transdisciplinary projects, such as the research networks of the Leibniz Association.
Contact us, we have expertise in the following areas:
- Sociological survey data
- Digital behaviour data / Big Data
- Study design
- Analysis methods and method training
- Research Data Management
- Quality assurance in data collection
- Data archiving