"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.
With these efforts in Open Science research and development we
- advance FAIR data in all areas, including new types of data like digital behavioral data (DBD)
- enable reproducibility of computer-based analyses in the social sciences (and beyond)
- facilitate sharing of research publications, data, and code
- 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!
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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
- Bosch, Thomas, Richard Cyganiak, Joachim Wackerow, and Benjamin Zapilko. 2015. DDI-RDF Discovery Vocabulary: A Vocabulary for Publishing Metadata about Data Sets (Research and Survey Data) into the Web of Linked Data: (Draft of metadata specification). http://rdf-vocabulary.ddialliance.org/discovery.html.
- Block, William, Thomas Bosch, Bryan Fitzpatrick, Daniel Gillman, Jay Greenfield, Arofan Gregory, Marcel Hebing, Larry Hoyle, Chuck Humphrey, Jon Johnson, Jenny Linnerud, Brigitte Mathiak, Steven McEachern, Olof Olsson, Barry Radler, Ørnulf Risnes, Dan Smith, Wendy Thomas, Joachim Wackerow, Dennis Wegener, and Wolfgang Zenk-Möltgen. 2012. "Developing a model-driven DDI specification." DDI Working Paper Series. doi: https://doi.org/10.3886/DDIWorkingPaper04.
- Hartmann, Thomas, Benjamin Zapilko, Joachim Wackerow, and Kai Eckert. 2016. "Directing the development of constraint languages by checking constraints on RDF data." International Journal of Semantic Computing 10 (2): 193-217. doi: https://doi.org/10.1142/S1793351X16400079.
- Krämer, Thomas. 2017. A data discovery index for the social sciences. doi: https://doi.org/10.5281/zenodo.896431.
- Jesse, Friederike, and Brigitte Mathiak. 2018. "Fesselsteine, Fallensteine... - was sagt die Crowd dazu?" Mitteilungen der Sudanarchäologischen Gesellschaft 2018 (Sonderheft): 29-34.