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The purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.

The study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.

The study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.

The study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.

This study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.

The government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.

This study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.

High levels of youth unemployment in Africa, the difficulty of accessing salaried jobs, and the weakness of public institutions in charge of employment issues push youths towards informal channels that can help them find jobs. Among these informal channels, religion has been playing an increasingly important role. Thus, this study aimed to analyse the effects of religion on youths' access to self-employment.

This study used data from the survey on the improvement of youth employment policies in Francophone Africa—conducted in 2018 by the Laboratory for Economic and Social Research and Studies (LARES) of Marien Ngouabi University—to estimate the effects of religion on access to self-employment. The econometric model employed is a two-stage model. Conditional mixed process developed by Roodman (2011) was used to verify the model's robustness.

The results indicate that religion exhibits a positive and significant effect on access to self-employment. This effect is stronger for youths from Muslim communities than for those from other religious communities, compared to youths who do not engage in religious communities.

Based on the current dynamics observed in numerous African countries with respect to employment access, these results imply that religious denominations should be considered when developing policies and programs related to employment, particularly for youths.

The approach followed in this study contributes to the literature predominantly by demonstrating how the network theory approach helps explain, to some extent, the link between religion and access to employment in general and access to self-employment, particularly in developing economies—mainly in sub-Saharan Africa, where the recourse to informal channels of access to self-employment constitutes a significant solution approach for youths.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0097

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

This paper aims to examine the primary supply data sources that have been used for research into the sharing economy, and the advantages and limitations of these sources in the literature.

To address the research aims, this study conducted a systematic literature review and content analysis of all relevant articles. Following the review, the methodological sections of the selected papers were examined to identify the characteristics and limitations of all data sources used in the papers.

This study revealed several limitations of the use of three major data sources, namely, web scraping with self-made bots, inside Airbnb and AirDNA, for sharing economy research. The review shows that the majority of the selected papers did not acknowledge any limitations, nor did they discuss the quality of the data sources.

The findings of this paper can serve as guidelines for selecting appropriate data sources for research into the sharing economy and cautions researchers to address the limitations of the data sources used.

To the best of the authors’ knowledge, this is the first study that explores the advantages and limitations of data sources used in short-term rental market research.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

This paper aims to understand the current development situation of scientific data management policy in China, analyze the content structure of the policy and provide a theoretical basis for the improvement and optimization of the policy system.

China's scientific data management policies were obtained through various channels such as searching government websites and policy and legal database, and 209 policies were finally identified as the sample for analysis after being screened and integrated. A three-dimensional framework was constructed based on the perspective of policy tools, combining stakeholder and lifecycle theories. And the content of policy texts was coded and quantitatively analyzed according to this framework.

China's scientific data management policies can be divided into four stages according to the time sequence: infancy, preliminary exploration, comprehensive promotion and key implementation. The policies use a combination of three types of policy tools: supply-side, environmental-side and demand-side, involving multiple stakeholders and covering all stages of the lifecycle. But policy tools and their application to stakeholders and lifecycle stages are imbalanced. The development of future scientific data management policy should strengthen the balance of policy tools, promote the participation of multiple subjects and focus on the supervision of the whole lifecycle.

This paper constructs a three-dimensional analytical framework and uses content analysis to quantitatively analyze scientific data management policy texts, extending the research perspective and research content in the field of scientific data management. The study identifies policy focuses and proposes several strategies that will help optimize the scientific data management policy.

Information management workflow in building information modelling (BIM)-based collaboration is based on using a common data environment (CDE). The basic premise of a CDE is exposing all relevant data as a single source of truth and facilitating continuous collaboration between stakeholders. A multitude of tools can be used as a CDE, however, it is not clear how the tools are used or if they fulfil the users’ needs. Therefore, this paper aims to investigate current practices of using CDEs for information management during the whole built asset’s life cycle, through a state-of-the-art literature review and an empirical study.

Literature data is collected according to the PRISMA 2020 guideline for reporting systematic reviews. This paper includes 46 documents in the review and conduct a bibliometric and thematic analysis to identify the main challenges of digital information management. To understand the current practice and the views of the stakeholders using CDEs in their work, this paper used an empirical approach including semi-structured interviews with 15 BIM experts.

The results indicate that one of the major challenges of CDE adoption is project complexity and using multiple CDEs simultaneously leading to data accountability, transparency and reliability issues. To tackle those challenges, the use of novel technologies in CDE development such as blockchain could be further investigated.

The research explores the major challenges in the practical implementation of CDEs for information management. To the best of the authors’ knowledge, this is the first study on this topic combining a systematic literature review and fieldwork.