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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 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 novel coronavirus (SARS-CoV-2) variant of 2019 has taken more than 3.8 million lives according to the World Health Organization. To stop the spread of such a deadly and contagious disease, lockdown of varying nature was imposed worldwide. Lockdown, preventive techniques and observation of standard operating procedures (SOPs) have effectively decreased the spread of contagious diseases but have affected various businesses and industries economically. The food industry has been hit hard by different restriction parameters, due to which a disruption in food supply and demand was observed. Therefore, this study aims to study this disruption in the supply chain of processed food.

A comprehensive review was conducted on PubMed, Google Scholar, and Scopus to locate articles on processed foods, food delivery and supply chain. The selected articles were evaluated using the context analysis method.

The pandemic situation has increased the consumption and demand for processed food products from retail stores, and decreased the demand for food service products. These circumstances called for technological advancement in the field of food supply from farm to fork. This study reviews research articles, policies and secondary literature. Several advances have been made to deliver safe, nutritious and wholesome food to consumers. Block chain-based food supply chains, value stream mapping, sustainable supply chain domain and online ordering systems via mobile apps have been discussed in correspondence with information and communication technology (ICT) during COVID-19.

This study concludes that the use of advanced software and its adequate knowledge by suppliers, logistics companies and consumers have assisted in handling shocks to the global food system and provided in-time food delivery, traceability, database information and securely processed food to consumers.

This study shows the effects of COVID-19 pandemic on global food systems; disruption in food demand and supply chain is overlooked and changed; use of technological advances in food supply chain to tackle pandemic; online food ordering system gained popularity and improved technically.

1. The review highlights the effects of the COVID-19 pandemic on global food systems.

2. The disruption in food demand and supply chain is overlooked and changed.

3. The use of technological advances in the food supply chain to tackle the pandemic.

4. The online food ordering system gained popularity and improved technically.

The review highlights the effects of the COVID-19 pandemic on global food systems.

The disruption in food demand and supply chain is overlooked and changed.

The use of technological advances in the food supply chain to tackle the pandemic.

The online food ordering system gained popularity and improved technically.