Search results

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.

The purpose of this paper is to try to reach the main factors that could put national security at risk as a result of government cloud computing programs.

The paper adopts the analytical approach to first lay foundations of the relation between national security, cybersecurity and cloud computing, then it moves to analyze the main vulnerabilities that could affect national security in cases of government cloud computing usage.

The paper reached several findings such as the relation between cybersecurity and national security as well as a group of factors that may affect national security when governments shift to cloud computing mainly pertaining to storing data over the internet, the involvement of a third party, the lack of clear regulatory frameworks inside and between countries.

Governments are continuously working on developing their digital capacities to meet citizens’ demands. One of the most trending technologies adopted by governments is “cloud computing”, because of the tremendous advantages that the technology provides; such as huge cost-cutting, huge storage and computing capabilities. However, shifting to cloud computing raises a lot of security concerns.

The value of the paper resides in the novelty of the topic, which is a new contribution to the theoretical literature on relations between new technologies and national security. It is empirically important as well to help governments stay safe while enjoying the advantages of cloud computing.

Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it.

A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis.

RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues.

The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted.

RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.

With the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.

Research in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.

The proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.

E-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

The paper aims to explore the value generated by a specific configuration of a smart city's infrastructure by proposing a comparison between a silos configuration versus a crowd configuration at the data storage and processing level.

A system dynamics simulation is adopted to determine and compare the value created by the two configurations of smart city's infrastructure. The simulation outlines the flow of data and their positive and negative feedback that reinforce and hinder the smart city value generation.

The results demonstrate the huge impact of the availability of data for App developers when crowdsourcing configuration is adopted. Furthermore, results unveil the potential in value generation of a crowdsourcing smart city platform configuration compared to a silos architecture.

The authors have proposed a comparison between two alternative smart city digital platform configurations. The paper seeks to test the magnitude of the pros and cons of a crowdsourcing approach in setting up a smart city digital platform. The paper provides new guidelines for improving the data management of smart cities.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

This paper aims to provide top-level design and basic platform for intelligent application in China high-speed railway.

Based on the analysis for the future development trends of world railway, combined with the actual development needs in China high-speed railway, The definition and scientific connotation of intelligent high-speed railway (IHSR) are given at first, and then the system architecture of IHSR are outlined, including 1 basic platform, 3 business sectors, 10 business fields, and 18 innovative applications. At last, a basic platform with cloud edge integration for IHSR is designed.

The rationality, feasibility and implementability of the system architecture of IHSR have been verified on and applied to the Beijing–Zhangjiakou high-speed railway, providing important support for the construction and operation of the world’s first IHSR.

This paper systematically gives the definition and connotation of the IHSR and put forward the system architecture of IHSR for first time. It will play the most important role in the design, construction and operation of IHSR.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.