@article{BeurskensScherzinger2024, author = {Beurskens, Michael and Scherzinger, Stefanie}, title = {Legal Perspectives on Research Data Storage}, series = {Datenbank-Spektrum (ISSN: 1618-2162)}, volume = {24}, journal = {Datenbank-Spektrum (ISSN: 1618-2162)}, number = {2}, publisher = {Springer}, address = {Berlin/Heidelberg}, issn = {1618-2162}, doi = {10.1007/s13222-024-00478-1}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2410012125227.647518861484}, pages = {85 -- 95}, year = {2024}, abstract = {Responsibly managing research data has become increasingly important for researchers, especially within the database research community. Despite significant progress in best practices, the state-of-the-art in research data storage is lacking from a legal perspective. We introduce the stakeholders and the dynamic nature of their relationships (such as researchers changing affiliation) and observe that no existing infrastructure for research data storage fully meets their requirements. Therefore, we emphasize the need to design a comprehensive system architecture for research data storage that is aligned with legal considerations from the start, rather than as an afterthought.}, language = {en} } @article{HacklMuellerGranitzeretal.2023, author = {Hackl, Veronika and M{\"u}ller, Alexandra Elena and Granitzer, Michael and Sailer, Maximilian}, title = {Is GPT-4 a reliable rater? Evaluating consistency in GPT-4's text ratings}, series = {Frontiers in Education}, volume = {2023}, journal = {Frontiers in Education}, number = {8}, publisher = {Frontiers}, address = {Lausanne}, issn = {2504-284X}, doi = {10.3389/feduc.2023.1272229}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-18348}, pages = {8 Seiten}, year = {2023}, abstract = {This study reports the Intraclass Correlation Coefficients of feedback ratings produced by OpenAI's GPT-4, a large language model (LLM), across various iterations, time frames, and stylistic variations. The model was used to rate responses to tasks related to macroeconomics in higher education (HE), based on their content and style. Statistical analysis was performed to determine the absolute agreement and consistency of ratings in all iterations, and the correlation between the ratings in terms of content and style. The findings revealed high interrater reliability, with ICC scores ranging from 0.94 to 0.99 for different time periods, indicating that GPT-4 is capable of producing consistent ratings. The prompt used in this study is also presented and explained.}, language = {en} }