2020, Kismihók, Gábor, Zhao, Catherine, Schippers, Michaéla, Mol, Stefan, Harrison, Scott, Shehata, Shady, Lane, H. Chad, Zvacek, Susan, Uhomoibhi, James

This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper shows that the main barriers for goal setting applications in education are not related to the technology, the available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack of students’ goal setting skills and abilities, and the current curriculum design, which, especially in the observed higher education institutions, provides little support for goal setting interventions.

2020, Guns, P.-J.D., Guth, B.D., Braam, S., Kosmidis, G., Matsa, E., Delaunois, A., Gryshkova, V., Bernasconi, S., Knot, H.J., Shemesh, Y., Chen, A., Markert, M., Fernández, M.A., Lombardi, D., Grandmont, C., Cillero-Pastor, B., Heeren, R.M.A., Martinet, W., Woolard, J., Skinner, M., Segers, V.F.M., Franssen, C., Van Craenenbroeck, E.M., Volders, P.G.A., Pauwelyn, T., Braeken, D., Yanez, P., Correll, K., Yang, X., Prior, H., Kismihók, G., De Meyer, G.R.Y., Valentin, J.-P.

Safety pharmacology is an essential part of drug development aiming to identify, evaluate and investigate undesirable pharmacodynamic properties of a drug primarily prior to clinical trials. In particular, cardiovascular adverse drug reactions (ADR) have halted many drug development programs. Safety pharmacology has successfully implemented a screening strategy to detect cardiovascular liabilities, but there is room for further refinement. In this setting, we present the INSPIRE project, a European Training Network in safety pharmacology for Early Stage Researchers (ESRs), funded by the European Commission's H2020-MSCA-ITN programme. INSPIRE has recruited 15 ESR fellows that will conduct an individual PhD-research project for a period of 36 months. INSPIRE aims to be complementary to ongoing research initiatives. With this as a goal, an inventory of collaborative research initiatives in safety pharmacology was created and the ESR projects have been designed to be complementary to this roadmap. Overall, INSPIRE aims to improve cardiovascular safety evaluation, either by investigating technological innovations or by adding mechanistic insight in emerging safety concerns, as observed in the field of cardio-oncology. Finally, in addition to its hands-on research pillar, INSPIRE will organize a number of summer schools and workshops that will be open to the wider community as well. In summary, INSPIRE aims to foster both research and training in safety pharmacology and hopes to inspire the future generation of safety scientists.

2021-02-25, Brehm, Elke

Präsentation im Rahmen der Veranstaltungsreihe "Open-Access-Talk"

2020, Fathalla, S., Vahdati, S., Lange, C., Auer, Sören

One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org.

2021, Oelen, Allard, Stocker, Markus, Auer, Sören

The 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.

2021, Lezhnina, Olga, Kismihók, Gábor

In 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.

2020-12-09, Doerry, Janine, Blümel, Ina, Cartellieri, Simone, Heller, Lambert, Wagner, Jens-Christian

Auszug aus dem Antrag im MWK-Förderprogramm Pro*Niedersachsen – Kulturelles Erbe – Sammlungen und Objekte

2020, Tavakoli, Mohammadreza, Faraji, Ali, Mol, Stefan T., Kismihók, Gábor

This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.

2022, Hopf, David, Dellmann, Sarah, Hauschke, Christian, Tullney, Marco

Open 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.

2020-02-13, Björnemalm, Matthias, Cappellutti, Federica, Dunning, Alastair, Gheorghe, Dana, Goraczek, Malgorzata Zofia, Hausen, Daniela, Hermann, Sibylle, Kraft, Angelina, Martinez Lavanchy, Paula, Prisecaru, Tudor, Sànchez, Barbara, Strötgen, Robert

The white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”