Open Access to Research Data. Facing Funders’ Requirements on Making Research Data FAIR

Donnerstag, 01. August 2019
– Freitag, 02. August 2019



Short Course D as part of the 8th GESIS Summer School in Survey Methodology

Course description

Nowadays, more and more funders, like the European Commission, require Open Data of high quality that can be re-used by researchers for replication as well as for new (research) purposes. Hence, data sharing of research data is of increasing importance in quantitative social science research. Of course, transparency and replicability of research data and research findings is considered an integral part of good scientific practice. In addition, data sharing enables others to continue working with the data in their own research projects, for teaching etc. Data sharing thus fosters research and innovations, avoids duplicating already existing data and increases efficiency of public money spent. In the context of Open Access to research data, researchers are prompt not only to share their data in one way or another, but to systematically process so called FAIR data, ensuring that research data can be found, accessed, interoperated and re-used by others.

Developed by data professionals and archivists, the FAIR data principles are challenging for many researchers, being in doubt on how to satisfy appropriate requirements by funders. The current short course introduces the FAIR data principles in light of ensuring Open Access to research data. It discusses requirements of Open Access to research data and how the FAIR data principles foster a widespread of high quality data. Based on the definition of the FAIR principles and the discussion on appropriate requirements, the course guides researchers on how to process shareable data that can be re-used by others and how researchers could make such shareable data FAIR.

Target group

Participants will find the course useful if they conduct quantitative social science research and want to create Open Data or to gain basic knowledge on how to meet the funders' requirements for Open Science and the FAIR data principles.