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Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

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.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

This study aims to unveil the pivotal components and implementation pathways in the digital innovation of smart tourism destinations, while constructing a theoretical framework from a holistic perspective.

The research focuses on 31 significant urban smart tourism destinations in China. Secondary data was collected through manual search supplemented by big data scraping, whereas primary data was obtained from interviews with municipal tourism authorities. Grounded theory was used to theoretically construct the phenomenon of digital innovation in smart tourism destinations.

This research has formulated a data-driven knowledge framework for digital innovation in smart tourism destinations. Core components include digital organizational innovation, smart data platforms, multi-stakeholder digital collaborative ecosystem and smart tourism scenario systems. Destinations can achieve smart tourism scene innovation through closed innovation driven by smart data platforms or open innovation propelled by a multi-stakeholder digital collaborative ecosystem.

Based on insights from digital innovation practices, this study proposes a series of concrete recommendations aimed at assisting Destination Management Organizations in formulating and implementing more effective digital innovation strategies to enhance the sustainable digital competitiveness of destinations.

This study advances smart tourism destination innovation research from localized thinking to systemic thinking; extends digital innovation theory into the realm of smart tourism destination innovation; repositions the significance of knowledge in smart tourism destination innovation; and constructs a comprehensive framework for digital innovation in smart tourism destinations.

本研究致力于揭示智能旅游目的地数字创新中的核心组件及实施路径, 并创建一个整体视角下的理论框架。

研究选定中国31座重要城市型智能旅游目的地为研究对象。通过人工检索结合大数据抓取的方式收集二手资料, 以各市旅游主管部门为访谈对象收集一手资料。运用扎根理论对智能旅游目的地的数字创新现象进行理论构建。

本研究构建了一个数据型知识驱动的智能旅游目的地数字创新框架。其中, 核心组件包括数字组织创新、智慧数据平台、多主体数字协同生态和智慧旅游场景体系。目的地可通过智慧数据平台驱动的内生型创新或多主体数字协同生态推动的开放式创新, 实现智能旅游场景创新。

本研究将智能旅游目的地创新相关研究由局部思考推向系统思考; 将数字创新理论扩展到智能旅游目的地创新的研究中; 重新定位知识在智能旅游目的地创新中的重要地位; 以及构建了一个智能旅游目的地数字创新整体框架。

本研究基于数字创新实践洞察, 提出了一系列具体建议。旨在帮助目的地管理组织更有效地制定和实施数字创新策略, 以增强旅游目的地可持竞争力。

La investigación se centra en 31 destacados destinos turísticos urbanos inteligentes de China. Los datos secundarios se recopilaron mediante una búsqueda manual complementada con técnicas de big data, mientras que los datos primarios se obtuvieron a partir de entrevistas con las autoridades turísticas municipales. Se empleó la teoría fundamentada para construir teóricamente el fenómeno de la innovación digital en los destinos turísticos inteligentes.

Esta investigación tiene como objetivo identificar los componentes esenciales y las rutas de implementación de la innovación digital en destinos turísticos inteligentes, y construir un marco teórico desde una perspectiva holística.

Este estudio ha desarrollado un marco de conocimiento basado en datos para la innovación digital en destinos turísticos inteligentes. Los componentes centrales incluyen la innovación organizativa digital, la plataforma de datos inteligentes, el ecosistema digital colaborativo de múltiples actores y el sistema de escenarios turísticos inteligentes. Además, tanto la innovación endógena impulsada por la plataforma de datos inteligentes como la innovación abierta impulsada por el ecosistema digital colaborativo de múltiples actores contribuyen a la innovación por escenarios en destinos turísticos inteligentes.

A partir de las prácticas de innovación digital, este estudio ofrece una serie de recomendaciones dirigidas a las Organizaciones de Gestión de Destinos (DMOs) para la formulación e implementación de estrategias de innovación digital de manera más efectiva, y mejorar la competitividad digital sostenible de los destinos turísticos.

Este estudio avanza la investigación sobre innovación en destinos turísticos inteligentes desde el pensamiento localizado hasta el pensamiento sistémico; extiende la teoría de la innovación digital al ámbito de la innovación en destinos turísticos inteligentes; reposiciona la importancia del conocimiento en la innovación de destinos turísticos inteligentes; y construye un marco integral para la innovación digital en destinos turísticos inteligentes.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

This chapter seeks to quantify the effects of geographic access to community health centers on the likelihood of an individual having a regular source of health care.

Utilizing survey and center location data, the analysis employs bivariate cross-tabulation with chi-square and multinominal logistic regression to quantify the relationship between variables.

While individuals living in close spatial proximity to community health centers were more likely to identify a community health center as a regular source of care as compared with those without proximal access, the effect of community health center access on the identification of any source of regular health care was generally insignificant or negative, except for populations with a chronic medical condition.

While these findings support current literature suggesting that spatial proximity to care is insufficient to transform at-risk populations into regular primary care users, it is important to note that it is possible that individuals prefer to access primary care services outside of their immediate neighborhoods, potentially mediating the observed effect of proximity to care on the likelihood of having a regular source of care. Also, because this analysis is based on cross-sectional survey data, it is impossible to make a causal argument about the relationship between variables. Only the observed association can be asserted and used to inform future studies.

Existing research supports a positive association between community health center utilization and measures of health for social groups traditionally facing barriers to care, but few studies isolate the effect of center availability and health, particularly when considering those living in the catchment area but are not regular users. Due to the complexity and prevalence of barriers to health care for vulnerable and at-risk populations, these findings suggest that improving geographic access to primary health care does not guarantee positive outcomes for target groups. The magnitude of social disadvantage on vulnerable and at-risk populations can have a devastating effect on health care outcomes that is not easily overcome by social programs.

In response to food supply constraints resulting from coronavirus disease 2019 (COVID-19) restrictions, in the year 2020, the project developed automated household Aquaponics units to guarantee food self-sufficiency. However, the automated aquaponics solution did not fully comply with data privacy and portability best practices to protect the data of household owners. The purpose of this study is to develop a data privacy and portability layer on top of the previously developed automated Aquaponics units.

Design Science Research (DSR) is the research method implemented in this study.

General Data Protection and Privacy Regulations (GDPR)-inspired principles empowering data subjects including data minimisation, purpose limitation, storage limitation as well as integrity and confidentiality can be implemented in a federated learning (FL) architecture using Pinecone Matrix home servers and edge devices.

The literature reviewed for this study demonstrates that the GDPR right to data portability can have a positive impact on data protection by giving individuals more control over their own data. This is achieved by allowing data subjects to obtain their personal information from a data controller in a format that makes it simple to reuse it in another context and to transmit this information freely to any other data controller of their choice. Data portability is not strictly governed or enforced by data protection laws in the developing world, such as Zimbabwe's Data Protection Act of 2021.

Privacy requirements can be implemented in end-point technology such as smartphones, microcontrollers and single board computer clusters enabling data subjects to be incentivised whilst unlocking the value of their own data in the process fostering competition among data controllers and processors.

The use of end-to-end encryption with Matrix Pinecone on edge endpoints and fog servers, as well as the practical implementation of data portability, are currently not adequately covered in the literature. The study acts as a springboard for a future conversation on the topic.

This paper aims to analyze Hangzhou Urban Brain in responding to COVID-19, including systematically sorting out the development mode, capabilities, composition and application of Urban Brain and exploring its role and effect. This paper tries to provide a reference for other cities' digital infrastructure construction through case analysis.

The authors took Hangzhou Urban Brain as a typical case in urban digital infrastructure construction, and they conducted thorough research on its practice in facing COVID-19. The authors analyzed the key elements of Urban Brain, the application and the evaluation of Urban Brain through literature review, field investigation, questionnaire and interviews.

Hangzhou Urban Brain has been deeply applied in urban management and has a good foundation. Therefore, when the COVID-19 occurred, the Urban Brain played an important role. The detailed practices facing COVID-19 are mainly in five aspects: information collection and analysis, ensuring material supply by government–enterprise collaboration, using AI and Big Data to “Visualize” COVID-19, etc. Moreover, Urban Brain has won high evaluation. However, Hangzhou Urban Brain still has problems like data privacy and security, technical issues, etc.

This case study shows that Hangzhou's experience in Urban Brain construction is worthy of reference and promotion. Firstly, it can strengthen the understanding of digital infrastructure in responding to public health emergencies. Furthermore, it provides a reference for other urban governance worldwide by excavating the role and effect of digital infrastructure in preventing and controlling COVID-19. Thirdly, it explores how to improve the digital infrastructure construction to support public health challenges, which will help the cities grasp the actual value of data and make progress in this field. By this, it can provide references for cities in the world, especially in Asia to achieve sustainable city development.