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This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

This paper aims to review and critically assess the role that data visualizations played as communication media tools to help society during a worldwide crisis. This paper re-creates and analyzes several visualizations, critically and ethically assesses their strengths and limitations and provides a set of best practices that are informative, accurate, ethical and engaging at each stage in a reader’s interest.

The paper bases its methodology on the construct of “The Network Society” (Van Dijk, 2006; Castells, 2000, 2006) by creating a series of social networked visualizations, identifying the challenges and pitfalls associated with this communication approach and suggesting best practices in information communication technology. The case study is COVID-19.

The research in this study found that visual data dashboards and interactive Web-based charts did play a significant role in helping society understand COVID-19’s impact to make better informed decisions about society’s health and safety.

Visual expositions of data do have strengths and weaknesses depending on how they are designed, how they communicate the story and how they are ethically deployed. Best practices are provided to help mitigate these limitations.

Visualizations are certainly not new, but the technology for rapidly developing and sharing them is new. Visual expositions provide an effective media for communicating complex information to a networked society.

Visual expositions provide an effective media for communicating complex information to a networked society.

This paper highlights the significance of the need to understand complex data in a crisis in a visual format and to communicate the information quickly, persuasively, effectively and ethically to a networked audience.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.

This study was carried out to examine the volume and annual growth pattern of research on e-health literacy research, investigate the open-access types of e-health literacy research and perform document production by country and by sources. The study also mapped the keywords used by authors to represent e-health literacy research and performed an analysis of the clusters of the keywords to reveal the thematic focus of research in the area.

The research was guided by a bibliometric approach involving visualization using VosViewer. Data were sourced from Scopus database using a syntax that was tested and verified to be capable of yielding reliable data on the subject matter. The analysis in this study was based on bibliographic data and keywords.

A total number of 1,176 documents were produced during 2006 and 2022. The majority of the documents (18.90%) were published based on hybrid open-access processes, and the USA has the highest contributions. The Journal of Medical Internet Research is the venue for most of the documents on the subject. The 1,176 documents were described by 5,047 keywords, 4.29 keywords per document, and the keywords were classified into five clusters that aptly capture the thematic structure of research in the area.

e-Health literacy has experienced significant growth in research production from 2006 to 2022, with an average of 69 documents per year. Research on e-health literacy initially had low output but began to increase in 2018. The majority of e-health literacy documents are available through open access, with the USA being the leading contributor. The analysis of keywords reveals the multifaceted nature of e-health literacy, including access to information, attitudes, measurement tools, awareness, age factors and communication. Clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers and effective communication in healthcare.

This study has practical implications for health promotion. There is also the element of patient empowerment in which case patients are allowed to take an active role in their healthcare. By understanding their health information and having access to resources that help them manage their conditions, patients can make informed decisions about their healthcare. Finally, there is the issue of improved health outcomes which can be achieved by improving patients' e-health literacy. Visualisation of e-health literacy can help bridge the gap between patients and healthcare providers, promote patient-centered care and improve health outcomes.

Research production on e-Health literacy has experienced significant growth from 2006 to 2022, with an average of 69 documents per year. Many e-health literacy documents are available through open access, and the USA is the leading contributor. The analysis of keywords reveals the nature of e-health literacy, including access to information, attitudes, measurement tools, awareness and communication. The clusters of keywords highlight different aspects of e-health literacy research, such as accessibility, attitudes, awareness, measurement tools and the importance of age, cancer, caregivers, and effective communication in healthcare.

The purpose of the study is to compare methodologies for mapping a historic building, with image capture by smartphones and drones, using photogrammetric techniques for three-dimensional (3D) modeling of the structure. Processes and products are also analyzed, as well as possibilities for storing and visualizing data for structuring a cadastre of historical and artistic heritage are studied.

For mapping with smartphones, the overlapping of photographs was guaranteed, with data acquisition using three different cameras, on the same date as the aerial survey. The models were made from different combinations of camera use. For storage, a conceptual model based on ISO 19.152:2012 is proposed, which was implemented in the MongoDB, resulting in a database for storage. The visualization was carried out on the Cesium ion platform.

The results indicate that the terrestrial 3D reconstruction using smartphones is an efficient alternative to the historical and artistic cadastre, presenting texture quality superior to the aerial survey in a shorter production time. When dealing with the conceptual model, the LADM (Land Administration Domain Model) standardization guarantees interoperability and facilitates data exchange. In addition, it proved to be flexible for the creation of thematic profiles, supporting their effective storage. The insertion of data in the visualization platform was simple and effective, and it even generated sharing links for visualization of the models.

The study analyses a low-cost method with the use of easily accessible devices, with a combination of methodologies and applied techniques. The data storage and visualization method is also simple and flexible, suitable for application in the cadastre of historical heritage.

This chapter explores the development of social science visualizations as cultural objects within art worlds. The research examines artworks as social science visualizations to show the importance of conducting analysis within distinctive social, institutional, and cultural environments. To make these arguments, the chapter outlines some of the key features of art worlds as they have been analyzed by cultural sociologists and anthropologists. We point out how cultures of reception and institutional intermediaries, such as museums, have historically shaped the construction of artworks, which are never produced or interpreted in a vacuum. The chapter closes with a call to expand both the application of social science visualizations and our understanding of such visualizations as subject to similar art world dynamics. Such visualizations, it is argued, constitute key components of social research practice increasingly oriented toward a digitally connected public hungry for visual interpretations of contemporary social developments.

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

In this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.

This paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.

Early career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.

This study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

The current study aims to map the scientific output of grey literature (GL) through bibliometric approaches.

The source for data extraction is a comprehensive “indexing and abstracting” database, “Web of Science” (WOS). A lexical title search was applied to get the corpus of the study – a total of 4,599 articles were extracted for data analysis and visualisation. Further, the data were analysed by using the data analytical tools, R-studio and VOSViewer.

The findings showed that the “publications” have substantially grown up during the timeline. The most productive phase (2018–2021) resulted in 47% of articles. The prominent sources were PLOS One and NeuroImage. The highest number of papers were contributed by Haddaway and Kumar. The most relevant countries were the USA and UK.

The study is useful for researchers interested in the GL research domain. The study helps to understand the evolution of the GL to provide research support further in this area.

The present study provides a new orientation to the scholarly output of the GL. The study is rigorous and all-inclusive based on analytical operations like the research networks, collaboration and visualisation. To the best of the authors' knowledge, this manuscript is original, and no similar works have been found with the research objectives included here.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.