"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!
- Saldanha Bach, Janete, Brigitte Mathiak, Valentina Hiseni, and Fidan Limani. 2022. Enhancing data findability: how scientists and repositories can improve their data visibility. GESIS – Leibniz Institute for the Social Sciences. doi: https://doi.org/10.5281/zenodo.6900267.
- Limani, Fidan, Yousef Younes, Valentina Hiseni, Janete Saldanha Bach Estevao, Peter Mutschke, and Brigitte Mathiak. 2021. KonsortSWD Task Area 5 Measure 2 Report Scope: Milestones 1, 2, and 3. https://zenodo.org/record/5901207.
- Saldanha Bach Estevao, Janete, Claus-Peter Klas, and Peter Mutschke. 2022. "The hurdles of current data citation practices and the adding-value of providing PIDs below study level." In JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022 Proceedings, edited by Akiko Aizawa, Thomas Mandl, Zeljko Carevic, Annika Hinze, Philipp Mayr, and Philipp Schaer, 41. New York: ACM. https://doi.org/10.1145/3529372.3533293.
- Klas, Claus-Peter, Matthäus Zloch, Janete Saldanha Bach, Erdal Baran, and Peter Mutschke. 2022. KonsortSWD Measure 5.1: PID Service for variables report. doi: https://doi.org/10.5281/zenodo.6397367.
- Bittermann, André, Veronika Batzdorfer, Sarah Marie Müller, and Holger Steinmetz. 2021. "Mining Twitter to detect hotspots in psychology." Zeitschrift für Psychologie 229 (1): 3-14. doi: https://doi.org/10.1027/2151-2604/a000437.
| 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 |
|
Science 2.0
Leibniz Research Alliance |
2016-07-01 | 2020-06-30 | Sonstige Drittmittel |
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