Publications

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Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines.

Karim, S., Wünsche, F., Broneske, D., Kuhn, M., & Saake, G. (2025).
Embracing NVM: Optimizing $B^𝜖$-tree structures and data compression in storage engines. In Binnig, C. et al. (Hrsg.), Datenbanksysteme für Business, Technologie und Web - Workshopband (BTW 2025) (S. 329-333). Bonn: Gesellschaft für Informatik. https://doi.org/10.18420/BTW2025-137

Publishing fine-grained standardized metadata – Lessons learned from three research data centers.

Wenzig, K., Daniel, A., Hansen, D., Koberg, T., & Tudose, M. (2025).
Publishing fine-grained standardized metadata – Lessons learned from three research data centers. 2025 (12). Berlin: Konsortium für die Sozial-, Verhaltens-, Bildungs- und Wirtschaftswissenschaften (KonsortSWD).

A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology.

Abbas, M. N., Broneske, D., & Saake, G. (2025).
A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-01667-y

AutoML meets hugging face: Domain-aware pretrained model selection for text classification.

Safikhani, P., & Broneske, D. (2025).
AutoML meets hugging face: Domain-aware pretrained model selection for text classification. In A. Ebrahimi, S. Haider, E. Liu, M. L. Pacheco, & S. Wein (Hrsg.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop). Albuquerque, USA: Association for Computational Linguistics.
Abstract

The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs.

VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings.

Avetisyan, H., & Broneske, D. (2025).
VerbCraft: Morphologically-aware Armenian text generation using LLMs in low-resource settings. In ¦. A. Holdt, N. Ilinykh, B. Scalvini, M. Bruton, I. N. Debess, & C. M. Tudor (Hrsg.), Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025) (S. 111-119). Tallinn: University of Tartu Library, Estonia.

Trendumfrage Forschungsdateninfrastrukturen 2024.

Hartstein, J., Blümel, C., & Klein, D. (2025).
Trendumfrage Forschungsdateninfrastrukturen 2024. Daten- und Methodenbericht. Hannover: DZHW.
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.

Stata tip 160: Drop capture program drop from ado-files.

Klein, D. (2025).
Stata tip 160: Drop capture program drop from ado-files. The Stata Journal, 2025(1), 252-253. https://doi.org/10.1177/1536867X251322974
Abstract

I explain that -capture program drop- is useless in ado-files. While it prevents errors in do-files when redefining programs in memory, it either isn't executed or results in an error in ado-files. Moreover, in ado-files with local subroutines, -capture program drop- can mistakenly remove unrelated programs from memory.

Tell me more! Using multiple features for binary text classification with a zero-shot model.

Broneske, D., Italiya, N., & Mierisch, F. (2025).
Tell me more! Using multiple features for binary text classification with a zero-shot model. In IEEE Institute of Electrical and Electronic Engineers (Hrsg.), 2024 International Conference on Machine Learning and Applications (ICMLA) (S. 1613-1620). Jacksonville, Florida, USA: IEEE Xplore. https://doi.org/10.1109/ICMLA61862.2024.00249

The standardized data management plan for educational research, an approach to foster tailored data management.

Netscher, S., Kaluza, H., Mauer, R., Mozygemba, K., & Stephan, K. (2025).
The standardized data management plan for educational research, an approach to foster tailored data management. International Journal of Digital Curation, 2025(1). https://doi.org/10.2218/ijdc.v19i1.910

ADAMANT: Hardware-accelerated query processing made easy.

Broneske, D., Burtsev, V., Drewes, A., Gurumurthy, B., Pionteck, T., & Saake, G. (2025).
ADAMANT: Hardware-accelerated query processing made easy. In K.-U. Sattler, A. Kemper, T. Neumann, & J. Teubner (Hrsg.), Scalable Data Management for Future Hardware (S. 1-38). Cham: Springer. https://doi.org/10.1007/978-3-031-74097-8

DZHW Scientists Survey 2023. Data and methods report on the 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:1.0.1
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 [..] Full Abstract: https://doi.org/10.21249/DZHW:scs2023:1.0.1

Explaining item-nonresponse in open questions with requests for voice responses.

Salvatore, C., & Höhne, J. K. (2025).
Explaining item-nonresponse in open questions with requests for voice responses. In A. Pollice & P. Mariani (Hrsg.), Methodological and Applied Statistics and Demography IV (S. 483-489). Cham: Springer. https://doi.org/10.1007/978-3-031-64447-4_82

Bots in web survey interviews: A showcase.

Höhne, J. K., Claaßen, J., Shahania, S., & Broneske, D. (2025).
Bots in web survey interviews: A showcase. International Journal of Market Research, 67(1), 3-12. https://doi.org/10.1177/14707853241297009

Automatic speech-to-text transcription: Evidence from a smartphone survey with voice answers.

Höhne, J. K., Lenzner, T., & Claaßen, J. (2025).
Automatic speech-to-text transcription: Evidence from a smartphone survey with voice answers. International Journal of Social Research Methodology (online first). https://doi.org/10.1080/13645579.2024.2443633

Integrating R into statistics and data analysis education: Learnings from the development and evaluation of a teaching concept for Communication Science.

Scheper, J., Leuppert, R., Possler, D., Freytag, A., Bruns, S., & Niemann-Lenz, J. (2024).
Integrating R into statistics and data analysis education: Learnings from the development and evaluation of a teaching concept for Communication Science. Journalism & Mass Communication Educator, 1-17. https://doi.org/10.1177/10776958241296505
Abstract

Despite the increasing use of the statistical programming language R in statistics and data analysis (SDA), its implementation in communication science education is limited. Experiences, recommendations, and a critical exchange are therefore scarce. The following contribution addresses this very gap. At the Department of Journalism and Communication Research of the Hanover University of Music, Drama and Media, we have transitioned the SDA education to R. We share our concept and demonstrate its success. The results of an online survey indicate that students perceived most teaching elements as helpful, recognizing both opportunities and challenges. We present key learnings designed to assist others in integrating R into SDA education.

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David Broneske
Dr. David Broneske Acting Head +49 511 450670-454
Karsten Stephan
Dr. Karsten Stephan Deputy Head +49 511 450670-415

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