Publications
233 Übereinstimmungen gefunden / 1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 151-165 166-180 181-195 196-210 211-225 226-233
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 |
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 |
Gotta catch 'Em All... Or Not?: How LLMs bypass traditional checks & mimic human response behavior in web surveys.Shahania, S., Spiliopoulou, M., & Broneske, D. (2025).Gotta catch 'Em All... Or Not?: How LLMs bypass traditional checks & mimic human response behavior in web surveys. In Association for Computing Machinery (Hrsg.), CI '25: Proceedings of the ACM Collective Intelligence Conference (S. 113-128). New York: ACM. https://doi.org/10.1145/3715928.3737491 |
Neue berufliche Rollen? Kompetenz- und Aufgabenprofile in der IT-gestützten Forschungsberichterstattung.Thiedig, C., Schelske, S., Petersohn, S., & Euler, T. (2025).Neue berufliche Rollen? Kompetenz- und Aufgabenprofile in der IT-gestützten Forschungsberichterstattung. Daten- und Methodenbericht zur quantitativen Erhebung des BMBF-geförderten Projektes BERTI. Hannover: DZHW. |
SBC-SHAP: Increasing the accessibility and interpretability of machine learning algorithms for sepsis prediction.Walke, D., Steinbach, D., Kaiser, T., Schönhuth, A., Saake, G., Broneske, D., & Heyer, R. (2025).SBC-SHAP: Increasing the accessibility and interpretability of machine learning algorithms for sepsis prediction. The Journal of Applied Laboratory Medicine. https://doi.org/10.1093/jalm/jfaf091 |
Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.Walke, D., Steinbach, D., Gibb, S., Kaiser, T., Saake, G., ... & Heyer, R. (2025).Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. PLOS One. https://doi.org/10.1371/journal.pone.0327636 |
DZHW-Studienberechtigtenpanel 2012. Daten- und Methodenbericht zur 3. Befragungswelle des Studienberechtigtenjahrgangs 2012.Jahn, V., Spangenberg, H., Ohlendorf, D., Föste-Eggers, D., Niebuhr, J., Vietgen, S., & Euler, T. (2025).DZHW-Studienberechtigtenpanel 2012. Daten- und Methodenbericht zur 3. Befragungswelle des Studienberechtigtenjahrgangs 2012. Hannover: DZHW. Abstract
The DZHW-Panel Study of School Leavers 2012 is part of the DZHW-Panel Study of School Leavers survey series, in which standardized multiple surveys are used to collect information on the post-school careers of school leavers with a (school) higher education entrance qualification. As a rule, several survey waves are conducted at different times before and after the acquisition of the higher education entrance qualification for each year group of persons with a university entrance qualification. Accordingly, this is a combined cohort-panel design. The panel 2012 is the 19th cohort of the study series with currently three waves. Full abstract: https://doi.org/10.21249/DZHW:gsl2012:3.0.0 |
SurveyBot: A new era of web survey pretesting.Shahania, S., Spiliopoulou, M., & Broneske, D. (2025).SurveyBot: A new era of web survey pretesting. In I. Maglogiannis, L. Iliadis, A. Andreou, & A. Papaleonidas (Hrsg.), Artificial Intelligence Applications and Innovations. AIAI 2025. IFIP Advances in Information and Communication Technology. Cham: Springer. https://doi.org/10.1007/978-3-031-96235-6_29 |
Towards automatic bias analysis in multimedia journalism.Hinrichs, R., Steffen, H., Avetisyan, H., Broneske, D., & Ostermann, J. (2025).Towards automatic bias analysis in multimedia journalism. Discover Artificial Intelligence, 5(1), 1-28. https://doi.org/10.1007/s44163-025-00362-1 |
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 |
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