“Open Science and Open Data” – Short Course by GESIS Training

Wann
Montag, 20. August 2018
– Dienstag, 21. August 2018

Wo
Köln

Veranstalter
GESIS

Short Course as part of the 7th GESIS Summer School in Survey Methodology

Data sharing and re-usability of research data is of increasing importance in quantitative social science. Not only is transparency and replicability of research data and research findings considered an integral part of good scientific practice. In addition, more and more funders, like the European Commission, and Journals require Open Data of high quality that can be re-used by researchers for replication as well as for new (research) purposes.

Ensuring transparency in research is not only a matter of creating new research data, but also of re-using already existing information. The workshop focuses on the idea of Open Science and Open Data, taking data creation as well as data re-use into account. On the one hand, it introduces the FAIR principles to guide researchers in creating re-usable research data. On the other hand, the workshop discusses the re-use of already existing research data and relevant aspects to keep in mind, working with intellectual property of others. With regard to the re-use of existing data, it introduces a (free) tool that helps researchers in the process of harmonizing their data. The workshop furthermore discusses aspects of legal and analytical re-usability as well as of replicability of research findings, in terms of Open Codes, e.g. in the context of data harmonization.

Course Prerequisites: Participants should be experienced in conducting and (re-)using quantitative research data and be well-versed in using one of the main statistical software packages, such as Stata, SPSS or R.

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;
  • re-use already existing data and want to increase transparency in research data and research findings.