[Postponed] GGS-Workshop: " ‘Open Science’: Potentials & Perspectives for Researchers (not only) from the Social Sciences, Economics and Law"
- https://www.uni-giessen.de/de/fbz/zentren/ggs/veranstaltungen/index_html/SoSe2023/open-science
- [Postponed] GGS-Workshop: " ‘Open Science’: Potentials & Perspectives for Researchers (not only) from the Social Sciences, Economics and Law"
- 2023-09-28T09:15:00+02:00
- 2023-09-29T16:50:00+02:00
28.09.2023 09:15 bis 29.09.2023 16:50 (Europe/Berlin / UTC200)
Room 101, Alter Steinbacher Weg 44, 35394 Giessen
The workshop aimes at junior researchers of the GGS at all qualification levels as well as junior researchers of profile-related disciplines.
Instructor: | Professor Dr Elmar Schlüter | |
Dates: |
September 28 and 29, 2023, 09:15 a.m. - 4:50 p.m. |
|
Max. participants: | 15 | |
Course language: | English | |
Registration Deadline: | September 18, 2023 | |
ECTS: | 2 |
Objectives
“How can the quality of research be optimally assured and how can research processes be made as transparent as possible?” In a nutshell, this question sums up what has become known under the umbrella term ‘Open Science’ - a guiding concept of a research approach oriented towards the quality criteria of transparency and openness, with access to project results as free as possible. The aim of this workshop is to convey selected central aspects of ‘Open Science’ with a view to the participants’ own research activities. Possible topics are:
- What are the central variants of ‘Open Science’?
- What are the overall potential advantages and disadvantages of an orientation towards ‘Open Science’ criteria?
- Which forms of publication are particularly suitable in the context of ‘Open Science’ (e.g. preregistered reports, prepublication servers, open access journals)?
- What does ‘preregistration’ of quantitative and qualitative studies mean? How can pre-registration be implemented in research practice?
- How and where can research data be stored sustainably and transparently for secondary analysis?
- How can data collections and analyses be made transparent in the context of ‘Open Science’? Which innovative data analysis methods are suitable for this purpose (e.g. multiverse analyses)?
You can find more details in the syllabus " ‘Open Science’: Potentials & Perspectives for Researchers (not only) from the Social Sciences, Economics and Law".