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The GLES Open Science Challenge 2021 in Hindsight: Experiences Gained and Lessons Learned (Special Issue Conclusion)

Die GLES Open Science Challenge 2021 im Rückblick: Erfahrungen und Lessons Learned (Fazit des Sonderheftes)
[journal article]

Bucher, Hannah
Stroppe, Anne-Kathrin
Burger, Axel M.
Faas, Thorsten
Schoen, Harald
Debus, Marc
Roßteutscher, Sigrid
Cohen, Denis
Huber, Robert A.
Jankowski, Michael
John, Melvin
Menzner, Jan
Schimpf, Christian H.
Schnaudt, Christian
Steiner, Nils D.
Traunmüller, Richard
Unkelbach, Fabienne
Vogel, Vera
Wegscheider, Carsten
Welz, Robert
Wuttke, Alexander

Abstract

The GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and... view more

The GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and relevant contributions to our discipline. The project resulted in the publication of this special issue, which includes seven registered reports based on data from the German Longitudinal Election Study (GLES) collected in the context of the 2021 German federal election. This concluding article of the special issue brings together the perspectives of the participating authors, reviewers, organizers, and editors in order to take stock of the different experiences gained and lessons learned in the course of the project. We are confident that future projects of a similar nature in political science, as well as authors, reviewers, and editors of registered reports, will benefit from these reflections.... view less


Die GLES Open Science Challenge 2021 ist ein Pilotprojekt, das zeigt, dass Registered Reports ein geeignetes und gewinnbringendes Publikationsformat in der quantitativen Politikwissenschaft sind, die dazu beitragen können, die Transparenz und Replizierbarkeit im Forschungsprozess zu erhöhen und somi... view more

Die GLES Open Science Challenge 2021 ist ein Pilotprojekt, das zeigt, dass Registered Reports ein geeignetes und gewinnbringendes Publikationsformat in der quantitativen Politikwissenschaft sind, die dazu beitragen können, die Transparenz und Replizierbarkeit im Forschungsprozess zu erhöhen und somit substanzielle und relevante Beiträge für unsere Disziplin zu liefern. Das Ergebnis ist die Veröffentlichung dieses Sonderheftes mit sieben Registered Reports, die auf Daten der German Longitudinal Election Study (GLES) basieren, die im Rahmen der Bundestagswahl 2021 erhoben wurden. Dieser abschließende Artikel des Sonderheftes bringt die Perspektiven von Autor*innen, Gutachter*innen, Organisator*innen und Herausgeber*innen zusammen, um eine Bilanz der verschiedenen Erfahrungen und Lehren zu ziehen, die im Laufe dieses Projektes gewonnen wurden.... view less

Keywords
publication; research process; quantitative method; election research; transparency; political science

Classification
Sociology of Science, Sociology of Technology, Research on Science and Technology

Free Keywords
Registered reports; Open science; 2021 German federal election; German Longitudinal Election Study

Document language
English

Publication Year
2022

Page/Pages
p. 207-219

Journal
Politische Vierteljahresschrift, 64 (2022) 1

Issue topic
GLES Open Science Challenge 2021

DOI
https://doi.org/10.1007/s11615-022-00437-z

ISSN
1862-2860

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution 4.0

FundingGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185

Version History
[v2]
[v1]

 


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Home  |  Legal notices  |  Operational concept  |  Privacy policy
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.