Mag.a Dr.in Katja Mayer

Senior Post Doc

(Elise Richter Fellowship FWF)

The Politics of Openness

Tel: +43-1-4277-49612
eMail: katja.mayer@univie.ac.at

Biography

Katja Mayer is a sociologist and works at the interface of science, technology and society. Since 2019, she is working as senior postdoc with the Elise Richter Fellowship (FWF) at the Department of Science and Technology Studies at the University of Vienna. Her research focuses on the interaction between social science methods and their public spheres.

As part of her postdoc position at the Professorship of Computational Social Science and Big Data, she established the field of "Critical Data Studies" at TU Munich.

Her research focus is on the cultural, ethical and socio-technical challenges at the interface of computer science, social sciences and society. Data is treated less as a new raw material, but as a highly variable and fragile phenomenon. In the context of data-driven decision-making, data are not considered as "given", but the way we collect, transform, analyze, and trust data is up for discussion.

In addition, Katja also works as Senior Scientist at the Center for Social Innovation in Vienna. Until recently she was Associate Researcher at the University of Vienna's research platforms "Governance of Digital Practices" and "Responsible Research and Innovation in Scientific Practice". For many years she has been teaching Sociology, STS and Web Sciences at the University of Vienna, the Danube University Krems, the University of Art and Design Linz and the University of Lucerne. She was a visiting fellow at the School of Computer Science at Carnegie Mellon University (USA). Moreover, she was a member of the core team of the Open Access Network Austria (OANA), co-heading the working group "National Strategy for the Transition to Open Science". In the years 2011-2013 she was a research fellow of the President of the European Research Council (ERC).

Publications

Mayer K, Strassnig M. The Digital Humanism Initiative in Vienna: A Report based on our Exploratory Study Commissioned by the City of Vienna. in Fritz J, Tomaschek N, Hrsg., Digitaler Humanismus. 1 Aufl. Band 9. Wien: Waxmann Verlag. 2020 doi: 10.5281/zenodo.4250144

Heck T, Mayer K, Peters I, Fischer C, Breznau N, Havemann J et al. Open Science, aber richtig! Was wir aus der Heinsberg-Studie lernen können. 2020. doi: 10.31222/osf.io/54zx2, 10.31222/osf.io/54zx2

Breznau N, Mayer K, Fischer C, Havemann J, Heck T, Peters I et al. Open Science, but Correctly! Lessons from the Heinsberg Study. 2020. doi: 10.31222/osf.io/axy84, 10.31222/osf.io/axy84

Mayer K, Dobusch L, Heimstädt M, Ross-Hellauer T. Defining predatory journals: no peer review, no point. Nature. 2020 Apr 1;580(7801):29-29. 29. doi: 10.1038/d41586-020-00911-x

Mayer K. Open Science Diplomacy. in Young M, Flink T, Dall E, Hrsg., Science Diplomacy in the Making: Case based insights from the S4D4C project. 2020. S. 133-215

Mayer K, Schuch K. Fostering the Sustainable Development Goals in Horizon Europe. 2019. 101 S. doi: 10.22163/fteval.2019.416

Tennant J, Beamer JE, Bosman J, Brembs B, Chung NC, Clement G et al. Foundations for Open Scholarship Strategy Development. 2019. doi: 10.31222/osf.io/b4v8p

Alburez-Gutierrez D, Chandrasekharan E, Chunara R, Gil-Clavel S, Hannak A, Interdonato R et al. Reports of the workshops held at the 2019 international AAAI conference on web and social media. AI Magazine. 2019 Jan 1;40(4):78-82. doi: 10.1609/aimag.v40i4.5287

Mayer K. Krempel (2005): Visualisierung komplexer Strukturen. in Stegbauer C, Holzer B, Hrsg., Schlüsselwerke der Netzwerkforschung. Wiesbaden: Springer VS. 2019. (Netzwerkforschung).

Mayer K, Pfeffer J. Lazer et al. (2009): Computational Social Science. in Holzer B, Stegbauer C, Hrsg., Schlüsselwerke der Netzwerkforschung. Wiesbaden: Springer VS. 2019. (Netzwerkforschung).

Mayer K. Merton (1950): Patterns of Influence. in Holzer B, Stegbauer C, Hrsg., Schlüsselwerke der Netzwerkforschung. Wiesbaden: Springer VS. 2019. (Netzwerkforschung).

Pfeffer J, Mayer K, Morstatter F. Tampering with Twitter’s Sample API. EPJ Data Science. 2018 Dez 1;7(1):50. doi: 10.1140/epjds/s13688-018-0178-0

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