"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!
- Lietz, Haiko, Mathieu Génois, Johann Schaible, Maria Zens, and Marcos Oliveira. 2023. "Community formation at IC2S2 2017." International Conference on Computational Social Science (IC²S² 2023), Copenhagen, 2023-07-18.
- Saldanha Bach, Janete, Fidan Limani, and Brigitte Mathiak. 2023. KonsortSWD Measure 5.2: Enhancing data findability : Milestones 4 and 5 report. 0.9. doi: https://doi.org/10.5281/zenodo.7520524.
- Schoch, David. 2023. "graphlayouts: Layout algorithms for network visualizations in R." Journal of Open Source Software 8 (84): 5238. doi: https://doi.org/10.21105/joss.05238.
- Soldner, Felix, Bennett Kleinberg, and Shane Johnson. 2022. Confounds and Overestimations in Fake Review Detection: Experimentally Controlling for Product-Ownership and Data-Origin. https://osf.io/29euc/?view_only=d382b6f03e1444ffa83da3ea04f1a04a.
- Schoch, David. 2022. "netrankr: An R package for total, partial, and probabilistic rankings in networks." Journal of Open Source Software 7 (77): 4563. doi: https://doi.org/10.21105/joss.04563.
| Title | Start | End | Funder |
|---|---|---|---|
|
NFDI for Data Science and Artificial Intelligence
(NFDI4DS)
|
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