The German Longitudinal Election Study [(GLES)][1], in collaboration with the journal German Political Science Quarterly [(PVS)][2] and the German Society for Electoral Studies [(DGfW)][3], invites researchers to participate in the GLES Open Science Challenge 2021. We call for open topic submissions for a Special Issue featuring Registered Reports and based on the data of the GLES Cross-Section 2021 pre-election survey or the GLES Rolling Cross-Section 2021 pre- and post-election data. **Overview** After 16 years of Angela Merkel as chancellor, these elections mark a watershed in German politics. The race for her successor is wide open. Simultaneously, it is unclear how long the Covid-19 pandemic and its corresponding restrictions will dominate the political agenda in Germany. Under these volatile circumstances, we invite open topic submissions for studies that employ 2021 GLES survey data to examine relevant questions in these times of political change and crises. The main characteristics of this Special Issue will be to review study proposals prior to data publication and to assess their merits based on the relevance of the research question(s) and the appropriateness of the theoretical and methodical approaches irrespective of the empirical results of the analyses. The combination of pre-registration and review process prior to data publication is known as Registered Reports. The GLES Open Science Challenge 2021 will provide a replicable model as to how Registered Reports can be applied to secondary data analysis, thereby providing a case study in how this format can be used for election data. **Open Access of Study Material and Data** To facilitate the pre-registration process, the GLES has adjusted its publication and accessibility processes and published the questionnaires and methodological documentation of the GLES Cross-Section, Pre-Election 2021 and the GLES Rolling Cross-Section 2021 both in English and German prior to data collection. The questionnaires are accessible via our [Downloads][4] section. Additionally, authors are welcome to use the [existing study materials][5] of the GLES Cross-Section 2017 pre-election survey and the GLES Rolling Cross-Section 2017 to set up their abstracts. The submission and review processes are scheduled along the data collection and publication processes to allow for a results-blind preparation of Registered Reports as well as the first round of review. Data access is provided in November and December 2021. Data are free of charge and can be directly downloaded after registration via the [GESIS Data Catalogue][6]. **More information** You can find the complete Call for Papers here. If you have any questions or informal enquiries, please do not hesitate to contact the [Editorial team][7]. Further Readings You can find a list of published Registered Reports in this [Zotero library.][8] Publication bias Chambers, C. D. (2013). Registered Reports: A new publishing initiative at Cortex. Cortex, 49, 609–610. Findley, M. G., Jensen, N. M., Malesky, E. J., & Pepinsky, T. B. (2016). Can Results-Free Review Reduce Publication Bias? The Results and Implications of a Pilot Study. Comparative Political Studies, 49(13), 1667–1703. https://doi.org/10.1177/0010414016655539 Weston, S. J., Ritchie, S. J., Rohrer, J. M., & Przybylski, A. K. (2019). Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets. Advances in Methods and Practices in Psychological Science, 2(3), 214–227. https://doi.org/10.1177/2515245919848684 Replication crisis Bischof, D., & Van der Velden, M. (2019). The Use and Usefulness of p‐Values in Political Science: Introduction. Swiss Political Science Review, 25(3), 276-280. Pre-registration of analyses using secondary data Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The pre-registration revolution. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2600-2606. https://doi.org/10.1073/pnas.1708274114 Weston, S. J., Ritchie, S. J., Rohrer, J. M., & Przybylski, A. K. (2019). Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets. Advances in Methods and Practices in Psychological Science, 2(3), 214–227. https://doi.org/10.1177/2515245919848684 [1]: https://gles.eu/ [2]: https://www.springer.com/journal/11615 [3]: http://www.dgfw.info/en/ [4]: https://gles.eu/gles-open-science-challenge-2021/downloads/ [5]: https://gles.eu/gles-open-science-challenge-2021/downloads/ [6]: https://www.gesis.org/en/services/finding-and-accessing-data/european-values-study/data-access [7]: http://gles-osc@gesis.org [8]: https://www.zotero.org/groups/479248/osf/collections/KEJP68G9