Publications

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Nacaps 2018. Data and methods report for data package version 3.0.0 of the National Academic Panel Survey (1st-6th wave).

Briedis, K., Lietz, A., Mühleck, K., Ruß, U., Scheller, P., ... & Weber, A. (2025).
Nacaps 2018. Data and methods report for data package version 3.0.0 of the National Academic Panel Survey (1st-6th wave). Hannover: DZHW. https://doi.org/10.21249/DZHW:nac2018-dmr-en:3.0.0
Abstract

Nacaps, the National Academics Panel Study, is a new longitudinal study of doctoral candidates and doctorate holders in Germany funded by the Federal Ministry of Education and Research (BMBF). The project aims at providing nationwide cross-sectional and longitudinal data on doctoral candidates and doctorate holders in Germany regarding their study conditions as well as their career trajectories within and outside of academia. The Nacaps study series apply a panel design to multiple cohorts. Nacaps 2018 is the first cohort in this series of studies. In 2019, all doctoral candidates registered at 53 higher education institutions entitled to award PhDs/doctorates in Germany [...] Full Abstract: https://doi.org/10.21249/DZHW:nac2018:3.0.0

LLM-driven bot infiltration: Protecting web surveys through prompt injections.

Höhne, J. K., Claaßen, J., & Wolf, B. L. (2025).
LLM-driven bot infiltration: Protecting web surveys through prompt injections. International Journal of Social Research Methodology (online first). https://doi.org/10.1080/13645579.2025.2598606

Context-aware search space adaptation of hyperparameters and architectures for AutoML in text classification.

Safikhani, P., & Broneske, D. (2025).
Context-aware search space adaptation of hyperparameters and architectures for AutoML in text classification. ACL Anthology, 1018-1027.
Abstract

While Automated Machine Learning (AutoML) systems have shown strong performance on structured data, their application to natural language processing (NLP) tasks remains limited by static, task-agnostic search spaces. In this work, we propose a context-aware extension of AutoPyTorch that dynamically adapts both the hyperparameter search space and neural architecture configuration based on corpus-level meta-features. Our approach extracts interpretable textual statistics—such as average sequence length, vocabulary richness, and class imbalance—to guide the configuration of key hyperparameters. We also introduce two adaptive neural backbones, whose structures are shaped by these meta-features to improve model expressiveness and generalization.

DZHW-Studienberechtigtenpanel 2018 - Daten- und Methodenbericht zum DZHW-Studienberechtigtenpanel 2018 (1. und 2. Befragungswelle).

Woisch, A., Franke, B., Quast, H., Föste-Eggers, D., Mentges, H., ... & Euler, T. (2025).
DZHW-Studienberechtigtenpanel 2018 - Daten- und Methodenbericht zum DZHW-Studienberechtigtenpanel 2018 (1. und 2. Befragungswelle). Hannover: FDZ-DZHW.

Effects of embodied interviewing agents on open narrative responses.

Höhne, J. K., Neuert, C., & Claaßen, J. (2025).
Effects of embodied interviewing agents on open narrative responses. International Journal of Market Research, 68(1), 15-25. https://doi.org/10.1177/14707853251388213

SimKit: Similarity graphs, eigendecomposition and spectral clustering in Neo4j.

Mondal, R., Ignatova, E., Heinzmann, J., Do, M. D., Murali, A., ... & Heyer, G. (2025).
SimKit: Similarity graphs, eigendecomposition and spectral clustering in Neo4j. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), IEEE xplore (S. 685-691). Jacksonville, Florida, USA: IEEE Xplore.

Asking for feedback: Innovating final comment questions in self-administered web surveys.

Claaßen, J., Höhne, J. K., & Kuhlmann, J. (2025).
Asking for feedback: Innovating final comment questions in self-administered web surveys. Journal of Survey Statistics and Methodology (online first). https://doi.org/10.1093/jssam/smaf018

DZHW-Wissenschaftsbefragung 2023.

Fabian, G., Heger, C., Just, A., & Weber, A. (2025).
DZHW-Wissenschaftsbefragung 2023. Daten- und Methodenbericht zur DZHW-Wissenschaftsbefragung 2023. Hannover: DZHW. https://doi.org/10.21249/DZHW:scs2023-dmr-de:2.0.0

DZHW Scientists Survey 2023.

Fabian, G., Heger, C., Just, A., Weber, A., & Oestreich, T. (2025).
DZHW Scientists Survey 2023. Data and methods report on the DZHW Scientists Survey 2023. Hannover: DZHW. https://doi.org/10.21249/DZHW:scs2023-dmr-en:2.0.0
Abstract

The DZHW Scientists Survey 2023 is an online survey of full-time academic and artistic staff at German universities and equivalent institutions of higher education with the right to award doctorates. It is repeated at regular intervals as a trend study to explore the working and research conditions at German universities and equivalent institutions of higher education. The DZHW Scientists Survey 2023 was conducted from January to March 2023. The respondents therefore take a retrospective look at their working and research conditions during the Covid-19 pandemic and their current post-pandemic situation. The previous Scientists Surveys took place in 2010, 2016 and 2019/2020. [...] Full Abstract: https://doi.org/10.21249/DZHW:scs2023:2.0.0

Beyond anti-elitism and out-group attacks: how concerns shape the AfD’s populist representation on German TikTok during the 2024 European elections.

Meyer, H., Niemann-Lenz, J., Rodeck, L., & Revers, M. (2025).
Beyond anti-elitism and out-group attacks: how concerns shape the AfD’s populist representation on German TikTok during the 2024 European elections. Information, Communication & Society, 1-22. https://doi.org/10.1080/1369118X.2025.2553016

A survey mode of the future? Investigating respondents' willingness to participate in self-administered video-based web surveys.

Claaßen, J., Lenzner, T., Höhne, J. K., & Ziller, C. (2025).
A survey mode of the future? Investigating respondents' willingness to participate in self-administered video-based web surveys. Methods, Data, Analyses (online first). https://doi.org/10.12758/mda.2025.10

Trendumfrage Forschungsdateninfrastrukturen 2024 - Daten- und Methodenbericht.

Hartstein, J., Blümel, C., & Klein, D. (2025).
Trendumfrage Forschungsdateninfrastrukturen 2024 - Daten- und Methodenbericht. Hannover: DZHW. https://doi.org/10.21249/dzhw:base4nfdi-dmr:2.0.0
Abstract

The Trend Survey Research Data Infrastructures 2024 is part of the accompanying research of the Basic Services for the National Research Data Infrastructure (Base4NFDI). The trend survey captures the perception, use and evaluation of established and new data infrastructures and services in the German research landscape. The focus in on the perspective of (potential) users.

Improving the performance of evolutionary-based complex detection models using gene ontology-based mutation operator in potein-protein interaction networks.

Abbas, M., Broneske, D., & Saake, G. (2025).
Improving the performance of evolutionary-based complex detection models using gene ontology-based mutation operator in potein-protein interaction networks. In Arai, K. (Hrsg.), Intelligent Systems and Applications. Proceedings of the 2025 Intelligent Systems Conference (IntelliSys) (S. 512-528). Cham: Springer. https://doi.org/10.1007/978-3-031-99958-1_32

Static and dynamic contextual embedding for AutoML in text classification tasks.

Safikhani, P., & Broneske, D. (2025).
Static and dynamic contextual embedding for AutoML in text classification tasks. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2025 7th International Conference on Natural Language Processing (ICNLP) (S. 292-301). Jacksonville, Florida, USA: IEEE Xplore. https://doi.org/10.1109/ICNLP65360.2025.11108687

Social media ads for survey recruitment: Performance, costs, user engagement.

Höhne, J. K., Claaßen, J., Kühne, S., & Zindel, Z. (2025).
Social media ads for survey recruitment: Performance, costs, user engagement. International Journal of Market Research (online first). https://doi.org/10.1177/14707853251367805

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