Publikationen
211 Übereinstimmungen gefunden / 1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 151-165 166-180 181-195 196-210 211-211
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
Die Trendumfrage Forschungsdateninfrastrukturen 2024 (Umfrage FDI 2024) ist Teil der Begleitforschung zu den Nationalen Forschungsdateninfrastruktur (NFDI)-Basisdiensten (Base4NFDI). Die Umfrage FDI 2024 erfasst die Wahrnehmung, Nutzung und Bewertung bestehender und neuer Dateninfrastrukturen und Dienste in der deutschen Forschungslandschaft. Der Fokus liegt auf der Perspektive der (potenziellen) Nutzenden. |
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
Die DZHW-Wissenschaftsbefragung 2023 ist eine Onlinebefragung des hauptberuflichen wissenschaftlich-künstlerischen Personals an deutschen Universitäten und gleichgestellten Hochschulen mit Promotionsrecht. Sie wird als Trendstudie zur Erforschung der Arbeits- und Forschungsbedingungen an deutschen Universitäten und gleichgestellten Hochschulen in regelmäßigen Abständen wiederholt. Die DZHW-Wissenschaftsbefragung 2023 wurde von Januar bis März 2023 durchgeführt. Die Befragten blicken also retrospektiv auf ihre Arbeits- und Forschungsbedingungen während der Covid-19-Pandemie sowie auf ihre aktuelle postpandemische Situation. Die vorherigen Wissenschaftsbefragungen fanden [..] Vollständiger 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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