"Openness" – publicly sharing scientific knowledge and the processes behind it – is central for all science. But achieving such openness, especially when large datasets and complex computational workflows are involved, is challenging.
GESIS does research on how to address these challenges and provides infrastructure and services to support scientists in making their results "open". Beyond the archiving and provision of data and publications we offer easy to use technical solutions for documenting and sharing computational workflows for data-intensive research designs.
Our research on Open Science
- We advance FAIR data in all areas, including new types of data like digital behavioral data (DBD).
- We enable reproducibility of computer-based analyses in the social sciences (and beyond).
- We facilitate sharing of research publications, data, and code.
- We provide altmetrics for measuring the public impact of science.
GESIS’ commitment to open science technologies and practices is long-standing, research-based and reflects in our engagement in NFDI and the strategic institutional expansion on DBD.
Moreover, we support individual researchers through training materials on open science.
And we implement and practice open science ourselves: please visit us on GitHub, re-use our DBD datasets, and try out our analytical tools!
- Rosenbusch, Hannes, Felix Soldner, Anthony M. Evans, and Marcel Zeelenberg. 2021. "Supervised machine learning methods in psychology: A practical introduction with annotated R code." Social and Personality Psychology Compass 15 (2): e12579. doi: https://doi.org/10.1111/spc3.12579.
- Manola, Natalia, Peter Mutschke, Guido Scherp, Klaus Tochtermann, and Peter Wittenburg, ed. 2020. Implementing FAIR data infrastructures: Dagstuhl perspectives workshop 18472. 8, 1. doi: https://doi.org/10.4230/DagMan.8.1.1. urn: urn:nbn:de:0030-drops-132376. https://drops.dagstuhl.de/opus/volltexte/2020/13237.
- Manola, Natalia, Peter Mutschke, Guido Scherp, Klaus Tochtermann, Peter Wittenburg, Kathleen Gregory, Wilhelm Hasselbring, Kees den Heijer, Paolo Manghi, and Dieter van Uytvanck. 2020. "Implementing FAIR data infrastructures: Dagstuhl perspectives workshop 18472." Dagstuhl Manifestos 8 (1): 1-34. doi: https://doi.org/10.4230/DagMan.8.1.1. urn: urn:nbn:de:0030-drops-132376. https://drops.dagstuhl.de/opus/volltexte/2020/13237.
- Lietz, Haiko. 2020. "Drawing impossible boundaries: Field delineation of Social Network Science." Scientometrics 125 2841–2876. doi: https://doi.org/10.1007/s11192-020-03527-0.
- Manola, Natalia, Peter Mutschke, Guido Scherp, Klaus Tochtermann, and Peter Wittenburg. 2019. "Implementing FAIR Data Infrastructures (Dagstuhl Perspectives Workshop 18472): Dagstuhl Perspectives Workshop 18472." Dagstuhl Reports (8, 11): 91-111. doi: https://doi.org/10.4230/DagRep.8.11.91. urn: urn:nbn:de:0030-drops-103577. http://drops.dagstuhl.de/opus/volltexte/2019/10357.
| Title | Start | End | Funder |
|---|---|---|---|
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NFDI for Data Science and Artificial Intelligence
(NFDI4DS)
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2021-10-01 | 2026-09-30 | DFG |
|
NFDI for Business, Economic and Related Data
(BERD@NFDI)
|
2021-10-01 | 2026-09-30 | DFG |
Stellvertretender Abteilungsleiter
FAIR Data
Teamleiter
Find out more about our consulting and services:
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Analyzing Digital Behavioral Data
Methods, tools, frameworks and infrastructures for analyzing digital behavioral data.
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CSS Capacity Building
Talks, tutorials, materials on computational methods for the collection, processing, and analysis of digital behavioral data.
- GESIS Notebooks
- Registration Agency da|ra
- Social Science Open Access Repository SSOAR