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- ItemOpen-Access-Finanzierung(Bonn : Bundesinstitut für Berufsbildung (BIBB), 2022) Kändler, Ulrike; Wohlgemuth, Michael; Ertl, Hubert; Rödel, Bodo[no abstract available]
- ItemCrowdsourcing Scholarly Discourse Annotations(New York, NY : ACM, 2021) Oelen, Allard; Stocker, Markus; Auer, SörenThe number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be . We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface’s usability and the participant’s attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process.
- ItemCombining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA(London : Taylor & Francis, 2021) Lezhnina, Olga; Kismihók, GáborIn our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined.
- ItemWirkungen von Open Access. Literaturstudie über empirische Arbeiten 2010-2021(Hannover : Technische Informationsbibliothek (TIB), 2022) Hopf, David; Dellmann, Sarah; Hauschke, Christian; Tullney, MarcoOpen Access – die freie Verfügbarkeit wissenschaftlicher Publikationen – bietet intuitiv viele Vorteile. Gleichzeitig existieren weiterhin Vorbehalte unter einigen Wissenschaftler:innen, Mitgliedern der Hochschulverwaltung, Verlagen und politischen Entscheidungsträger:innen. Im letzten Jahrzehnt sind viele empirische Studien zu den Wirkungen von Open Access erschienen. Der vorliegende Bericht liefert eine Übersicht über den Forschungsstand von 2010 bis 2021. Die berichteten empirischen Ergebnisse helfen dabei, die Vor- und Nachteile von Open Access zu bestimmen und dienen als Wissensbasis für Wissenschaftler: innen, Verlage, Institutionen und politische Entscheidungsträger:innen. Ein Überblick über den Wissensstand unterfüttert Entscheidungen zu Open-Access- und Publikationsstrategien. Zudem identifiziert dieser Bericht Aspekte von Open-Access-Wirkungen, die potenziell hohe Relevanz haben, aber noch nicht ausreichend untersucht wurden. Insgesamt können verschiedene Vorteile von Open Access beim jetzigen Forschungsstand als empirisch belegt bewertet werden. Dazu gehören ein verbesserter Wissenstransfer, erhöhte Publikationsgeschwindigkeit und die erhöhte Nutzung durch eine beruflich und geografisch diverse Leser:innenschaft. Zudem können einige vermutete negative Open-Access-Wirkungen – wie eine geringere Qualität von Publikationen und Nachteile beim Verkauf von Druckausgaben – als empirisch widerlegt betrachtet werden. Die empirischen Ergebnisse zu Open-Access-Wirkungen unterstützen daher das Ziel der weitgehenden Transformation zu Open Access, dem sich unter anderem die deutschen Wissenschaftsorganisationen verschrieben haben.
- ItemZweitveröffentlichungsrecht für Wissenschaftler*innen(Hannover : Technische Informationsbibliothek, 2021-02-25) Brehm, ElkePräsentation im Rahmen der Veranstaltungsreihe "Open-Access-Talk"
- ItemAnalysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade(Dordrecht [u.a.] : Springer Science + Business Media B.V., 2021) Lackner, Arthur; Fathalla, Said; Nayyeri, Mojtaba; Behrend, Andreas; Manthey, Rainer; Auer, Sören; Lehmann, Jens; Vahdati, SaharThe publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.