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The paper investigates the pivotal role of storytelling as a pedagogical tool in tertiary education, specifically in the context of the practice-based doctoral framework in design disciplines. In such a doctoral model, storytelling assumes different meanings and nuances that open to a study in relation to the self-reflective process at the core of the learning paradigm.

The research methodology integrates a qualitative and participatory approach with visual and design-based methods through which the authors interact with primary sources (the body of work of PhD candidates) and relevant research literature.

Drawing on the expanding field of creative practice research, the research work evidences the emergence of storytelling as a research method and learning tool applied at different levels of the candidates' Doctor of Philosophy (PhD) journey and provides methodological insights into the practice-based doctoral training paradigm.

The paper demonstrates the role of storytelling as a learning tool and evidences the multiple levels that storytelling assumes over the course of a practice-based doctoral journey, integrating processual, operational and contextual dimensions.

A research line has emerged that is concerned with investigating human factors in information systems and cyber-security in organizations using various behavioural and socio-cognitive theories. This study aims to explore human and contextual factors influencing cyber security behaviour in organizations while drawing implications for cyber-security in higher education institutions.

A systematic literature review has been implemented. The reviewed studies have revealed various human and contextual factors that influence cyber-security behaviour in organizations, notably higher education institutions.

This review study offers practical implications for constructing and keeping a robust cyber-security organizational culture in higher education institutions for the sustainable development goals of cyber-security training and education.

The value of the current review arises in that it presents a comprehensive account of human factors affecting cyber-security in organizations, a topic that is rarely investigated in previous related literature. Furthermore, the current review sheds light on cyber-security in higher education from the weakest link perspective. Simultaneously, the study contributes to relevant literature by gaining insight into human factors and socio-technological controls related to cyber-security in higher education institutions.

The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.