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Open data are characterized by a number of economic, environmental, technological, innovative, and social benefits. They are seen as a significant contributor to the city’s transformation into smart city. This is all the more so when the society is on the border of Society 5.0, that is, shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware, etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals (SDGs). Therefore, many agendas (sustainable development strategies, action plans) now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities toward smart cities, mapping them to the features of the Society 5.0. We believe that this trend develops a new form of society, which we refer to as “open data-driven society.” It forms a bridge from Society 4.0 to Society 5.0. This chapter attempts to identify the role of openness in promoting human-centric smart society, smart city, and smart living.

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

This study presents estimates of the effect of changes in the real minimum wage on the employment ratio of three groups believed to be most vulnerable to changes in the minimum wage: teenagers, young adults, and adult high-school dropouts. It also examines the effect of the minimum wage on three sub-groups within each of these larger groups: males, females, and nonwhites. The data set was obtained from the monthly outgoing rotation groups of the Current Population Survey (CPS), Three Budgets for Urban Families, and the CPI-W for various urban areas. The sample period is 1979–1999.

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.

The advent of online live streaming platforms (OLSPs) and online health communities (OHCs) has expedited the integration of traditional medical services with Internet new media technology. Since the practice of physicians conducting live streaming is a relatively new phenomenon, the potential cross-platform effects of such physicians’ live streaming have not received adequate attention.

This study collected data from 616 physicians specializing in cardiology, obstetrics and gynecology and neurology between April and November 2022 on Live.Baidu.com and WeDoctor.com. It constructed a panel data set comprising a total of 4,928 observations over an 8-month period and validated the model using empirical analysis with the fixed-effects method.

We find evidence of cross-platform influence in online healthcare. Physicians’ live streaming behavior (whether live or not and the heat of their streams) on OLSPs positively impacts both their consultation and reputation on OHCs. Additionally, physicians’ ability positively moderates the relationships between live streaming heat and their performance (in terms of consultation volume and reputation) on OHCs. However, ability does not moderate the relationship between physicians’ live streaming status (live or not) and their performance (in terms of consultation and reputation) on OHCs. Furthermore, the attractive appearance of the physicians also significantly moderates the impact in a positive way.

This is one of the pioneering studies on physicians’ live streaming. The study offers vital guidance for physicians and patients utilizing dual platforms and holds significant reference value for platform operators (such as OLSPs and OHCs) aiming to optimize platform operations and for the government in policy formulation and industry regulation.

This study is an exploration of areas pertaining to the use of production data in non-production environments. During the software development life cycle, non-production environments are used to serve various purposes to include unit, component, integration, system, user acceptance, performance and configuration testing. Organisations and third parties have been and are continuing to use copies of production data in non-production environments. This can lead to personal and sensitive data being accidentally leaked if appropriate and rigorous security guidelines are not implemented. This paper aims to propose a comprehensive framework for minimising data leakage from non-production environments. The framework was evaluated using guided interviews and was proven effective in helping organisation manage sensitive data in non-production environments.

Authors conducted a thorough literature review on areas related to data leakage from non-production systems. By doing an analysis of advice, guidelines and frameworks that aims at finding a practical solution for selecting and implementing a de-identification solution of sensitive data, the authors managed to highlight the importance of all areas related to sensitive data protection. Based on these areas, a framework was proposed which was evaluated by conducting set of guided interviews.

This paper has researched the background information and produced a framework for an organisation to manage sensitive data in its non-production environments. This paper presents a proposed framework that describes a process flow from the legal and regulatory requirements to data treatment and protection, gained through understanding the organisation’s business, the production system, the purpose and the requirements of the non-production environment. The paper shows that there is some conflict between security and perceived usability, which may be addressed by challenging the perceptions of usability or identifying the compromise required. Non-production environments need not be the sole responsibility of the IT section, they should be of interest to the business area that is responsible for the data held.

This paper proposes a simplified business model and framework. The proposed model diagrammatically describes the interactions of elements affecting the organisation. It highlights how non-production environments may be perceived as separate from the business systems, but despite the perceptions, these are still subject to the same legal requirements and constraints. It shows the interdependency of data, software, technical infrastructure and human interaction and how the change of one element may affect the others. The proposed framework describes the process flow and forms a practical solution in assisting the decision-making process and providing documentary evidence for assurance and audit purposes. It looks at the requirements of the non-production system in relation to the legal and regulatory constraints, as well as the organisational requirements and business systems. The impact of human factors on the data is also considered to bring a holistic approach to the protection of non-production environments.

Esports (electronic sports) are watched by hundreds of millions of people every year and many esports have overtaken large traditional sports in spectator numbers. The purpose of this paper is to investigate spectating differences between online spectating of esports and live attendance of esports events. This is done in order to further understand attendance behaviour for a cultural phenomenon that is primarily mediated through internet technologies, and to be able to predict behavioural patterns.

This study employs the Motivation Scale for Sports Consumption to investigate the gratifications spectators derive from esports, both from attending tournaments physically and spectating online, in order to explore which factors may explain the esports spectating behaviour. The authors investigate how these gratifications lead into continued spectatorship online and offline, as well as the likelihood of recommending esports to others. The authors employ two data sets, one collected from online spectators (n=888), the other from live attendees (n=221).

The results indicate that online spectators rate drama, acquisition of knowledge, appreciation of skill, novelty, aesthetics and enjoyment of aggression higher than live attendees. Correspondingly, social interaction and physical attractiveness were rated higher by live attendees. Vicarious achievement and physical attractiveness positively predicted intention to attend live sports events while vicarious achievement and novelty positively predicted future online consumption of esports. Finally, vicarious achievement and novelty positively predicted recommending esports to others.

During the past years, esports has emerged as a new form of culture and entertainment, that is unique in comparison to other forms of entertainment, as it is almost fully reliant on computer-human interaction and the internet. This study offers one of the first attempts to compare online spectating and live attendance, in order to better understand the phenomenon and the consumers involved. As the growth of esports is predicted to continue in the coming years, further understanding of this phenomenon is pivotal for multiple stakeholder groups.

The datafication of teaching and learning settings continues to be of broad interest to the learning sciences. In response, this study aims to explore a non-traditional learning setting, specifically two Golf Teaching and Research Programs, to investigate how athletes and coaches capture, analyze and use performance data to improve their practice. Athletic settings are well known for spurring the proliferation of personal data about performance across a range of contexts and ability levels. In these contexts, interest in athletes’ experiences with data has often been overshadowed by a focus on the technologies capturing the data and their capabilities.

This ethnographic research focuses on the data-rich experiences of golf coaches and students during two pedagogical encounters. Using Balka and Star’s (2015) concept of shadow bodies, this article explores how golfing bodies can become infused with data, creating partial representations of a lived experience that can be augmented and manipulated for pedagogical purposes, depending on the context and the individuals involved.

Interaction analysis helps the authors to examine the embodied and interactional nature of coach-golfer pedagogical encounters across two sites, a local Professional Golf Association golf course and a Swing Analysis Lab. The authors also split these encounters into two episodes to identify how coaches and golfers use partial representations of their bodies to analyze performance and interpret data.

This research suggests that as data-driven practices continue to engulf athletic settings, and teaching and learning settings broadly, emphasis needs to be placed on ensuring that athletes (learners) – from the most recreational to elite users – have an embodied understanding of their performance to improve their ability. Furthermore, this article raises questions about what data gets shared between instructors and athletes and how that data is used.

Many families in the developed world hire live-in caregivers to meet their childcare and elder care needs. Given the spatial arrangements – i.e., that the caregiver lives with her employer – relationships between employers and live-in caregivers can develop into family-like relationships. The purpose of this paper is to draw on data from two Canadian studies to examine the relationships between migrant live-in caregivers and their employers.

The first study focused on the live-in caregivers in Canada. Semi-structured interviews were conducted with 15 Filipina nurses who migrated to Canada through the Live-in Caregiver Program. The second study was a pilot descriptive study that included interviews of recruiter groups and employers of live-in caregivers. Data were thematically analyzed, aided by NVivo software.

The studies indicate that some live-in caregivers prefer to be treated as one of the family, while others prefer to have a strictly professional employer/employee relationship. Their employers are similarly divided. The authors identify reciprocity and respect as important ingredients for healthy relationships between live-in caregivers and their employers. Without these key ingredients, relationships between employers and live-in caregivers can be exploitative, especially given the unequal power inherent in the relationship.

Further studies are needed to better understand how these relationships evolve over time and in space.

There is a need to create policies to further prevent exploitation of live-in caregivers within an unfavorable employee or familial relationship.

This study provides useful insight into the development of knowledge on relationships between live-in caregivers and employers.