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The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

This study aimed to examine the antecedents, correlates, and consequences of burnout among higher education faculty in Saudi Arabia using the theoretical framework of the job demands-resources model.

Using a quantitative research design, a cross-sectional survey was employed to collect data from faculty members across multiple Saudi universities. The constructs were measured using validated scales, and data analysis included exploratory factor analysis, Pearson correlation analysis, factorial ANOVA, and multiple regression.

The study identified moderate levels of burnout, confirming a significant positive relationship with role conflict and a negative relationship with internal locus of control. Burnout significantly contributed to depression, insomnia, and turnover intentions. However, no significant relationship was found between burnout and workplace conflict when controlling for other variables.

The study's findings can inform policymakers and academic administrators about measures to alleviate faculty burnout, thus contributing to healthier academic work environments aligned with Saudi Arabia's Vision 2030 goals.

This research extends the job demands-resources model within the context of higher education institutions in Saudi Arabia, offering nuanced insights into burnout dynamics among university faculty in this region. Despite the model's robustness, the absence of a significant relationship between burnout and workplace conflict signals the need for a more intricate understanding of burnout's antecedents and consequences.

This study aims to evaluate the nutrient profile, carbon footprint and water footprint of one-month menus presented in five hospitals in Turkey and compare their environmental impacts with that of the Mediterranean diet.

The energy and nutrient content of menus were compared with recommendations of the Turkey Dietary Guidelines (TUBER) 2022. Nutrient profiles of hospital menus were evaluated using Nutrient Rich Food 9.3 (NRF 9.3) and SAIN-LIM models. The carbon and water footprints of the menus were calculated and compared with those of the Mediterranean diet.

Menus’ energy and nutrient content did not conform with TUBER 2022. The SAIN-LIM score of Hospital A (5.7 ± 1.1) was significantly higher than that of Hospitals C (4.8 ± 0.7, p = 0.001) and E (5.1 ± 0.7, p = 0.025). The carbon footprint of Hospital A was significantly lower (2.6 ± 0.3 kg CO₂ eq/person/day) and that of Hospital D (4 ± 0.9 kg CO₂ eq/person/day) was significantly higher than those of others (p < 0.001). While other menus were similar (p > 0.05), the water footprint of Hospital A was significantly lower (3.5 ± 0.7 L/kg, p < 0.001). In addition, if the menus were suitable for the Mediterranean diet, a reduction of 2.2–23.4% in the carbon footprint and 37.5–58.6% in the water footprint could be achieved. Moreover, menus’ carbon and water footprints were negatively correlated with NRF 9.3 and SAIN-LIM scores.

The primary aim should be to ensure that the planned menus follow the dietary guidelines. In addition, it is an undeniable fact that sustainable nutrition is a complex process with many dimensions. However, it seems possible to improve the nutrient profiles of the menus and reduce their environmental footprint with minor changes to be made in food services.

It seems possible to improve the nutrient profiles of the menus and reduce their environmental footprint with minor changes. For both health and environmental impacts, food services should switch to menus suitable for the Mediterranean diet.

The findings provide new insights into hospital menus’ quality and environmental impact.

The research paper aims to study dissatisfaction of teleworking employees in Spain during the Covid-19 health pandemic in order to propose three models: sociodemographic profile of the teleworking dissatisfied employee; advantages and disadvantages for the teleworking dissatisfied employee and advantages for the teleworking dissatisfied employee.

This study uses official open data obtained from the Spanish National Statistical Institute (INE, 2022) through Decision Trees statistical multivariable models implementing Classification and Regression Trees and Recursive Partitioning and Regression Trees techniques to determine the variables that can influence the satisfaction or dissatisfaction of the subjects.

This investigation offers three models with two sociodemographic profiles of dissatisfied teleworking employee, who is a high/middle-level manager/employee around 45 years old, and she/he lives with the partner. Regarding the most important advantage of teleworking, employees consider “use/saving of time” and as disadvantage “worse organization and coordination of work”.

This research provides empirical evidence with inductive reasoning on understanding the challenges of teleworking dissatisfied employees in Spain not only in turbulent times but also in “normalcy” to improve overall teleworker well-being and accomplish company’s and organization’s long-term objectives for better productivity and effectivity. The study has high practical value due to the integral approach incorporating dissatisfaction as a driver that can trigger negative behaviours towards the organizations and that is seldom addressed in the literature. Additionally, this paper could provide some new ideas for accomplishing “Spain Digital 2025” and “Europe’s Digital Decade: 2030” plans on institutional level.

This article describes the interviews the authors conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word “data” in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term.

The authors invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into five main research areas: philology and literary criticism, language and linguistics, history of art, computer science and archival studies. The interviews were transcribed and analysed using a grounded theory approach.

A list of 13 research data types has been compiled thanks to the information collected from participants. The term “data” does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported.

Our findings confirm that “data” within the FAIR framework should include all types of inputs and outputs humanities research work with, including publications. Also, the participants of this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.

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.

The purpose of this study is to examine the quality of information in articles in the online encyclopedia, Wikipedia, as perceived by readers and content contributors. This study explored several dimensions and characteristics of information presented in Wikipedia by identifying new emerging dimensions in terms of readers’ perceptions of the quality of online information.

Two rounds of online surveys were conducted using a mixed-method approach. In the first survey, the authors conducted content analysis on 197 participants, and in the second survey, the authors conducted factor analysis on 107 study participants. The authors used Qualtrics Panel Services to recruit individuals who read and/or edited the English version of Wikipedia articles and resided in the United States.

The mixed-method approach employed in this study to explore the quality of online information had three core components: users’ perceptions of information quality, content analysis, and exploratory factor analysis of the perceived information quality structure. The study found a new information quality category, social aspect quality. Dimensions include fun, goodness, empowering and user generated.

The results demonstrate the emergence of novel quality attributes for information quality presented online, particularly in social media. Moreover, this study is one of the rare studies to employ a mixed-method approach that offers diverse but reliable perspectives on information quality as perceived by everyday users of Wikipedia.