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Open Science, Reproducibility, and FAIRness

This research area explores whether and how data and methods/code are shared and reused within the academic community. It may involve identifying the reasons, motivations, or obstacles to sharing data and methods/code, and using these insights to develop actionable recommendations. Understanding how secondary research based on existing data and code is conducted in the social sciences, along with the challenges researchers face in this process, contributes to the advancement of research infrastructures. In addition to an empirical focus on the Open Science practices within the community, another key emphasis is on improving the reproducibility and reusability of research data and methods. This also includes examining how well data provision aligns with the FAIR principles.

Research Output

  • Batzdorfer, Veronika, Wolfgang Zenk-Möltgen, Laura Young, Alexia Katsanidou, Johannes Breuer, and Libby Bishop. 2024. “Between urgency and data quality: Assessing the FAIRness of data in social science research on the COVID-19 pandemic.” Research Ethics 20 (4): 744-63. doi: 10.1177/17470161241257575.   
  • Breuer, Johannes, and Mario Haim. 2024. “Are We Replicating Yet? Reproduction and Replication in Communication Research.” Media and Communication 12: 1-7. doi: 10.17645/mac.8382.  
  • Chan, Chung-hong, Tim Schatto-Eckrodt, and Johannes Gruber. 2024. “What makes computational communication science (ir)reproducible?” Computational Communication Research 6 (1): 1-30. doi: 10.5117/CCR2024.1.5.CHAN.
  • Krämer, Thomas, Andrea Papenmeier, Zeljko Carevic, Dagmar Kern, and Brigitte Mathiak. 2021. “Data-Seeking Behaviour in the Social Sciences.” International Journal on Digital Libraries 22 (2): 175-95. doi: 10.1007/s00799-021-00303-0. 
  • Schindler, David, Felix Bensmann, Stefan Dietze, Frank Krüger. 2022. "The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central." PeerJ Computer Science 8:e835 10.7717/peerj-cs.835.