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Recent technological advances have created volumes of data such that, unless some effective methods are used to analyse them, they will be either wasted or under‐examined for their useful information content. Scientific data visualization is an attempt to use graphical and numerical tools to extract information contained in data and hence to allow its analysis. This paper seeks to present a systematic approach to the development of tools for scientific data visualization.

It is shown that the approach to implement these tools involves four major steps: description of a reference model, validation of the data process, description of the software component and the design and implementation of the visualization tool.

This approach is substantiated by defining conditions suitable for scientific data visualization processes, in a relaxed manner. These conditions are subsequently refined more formally. Definitions and theorems of the proofs are succinctly discussed.

The mathematical description of the visualization process is necessary to understand and maintain some significant reduction in errors in scientific visualization processes.

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.

New computer-assisted techniques for visualizing data are evolving in a number of areas in transportation. For example, in engineering, 3D visualization and microsimulation techniques are being applied for the identification and evaluation of geometric and operational solutions for improving visually impaired pedestrian access to roundabouts and channelized turn lanes. For planning, visualization is being used for corridor analysis. Data visualization is being used as a tool for improving decision-making within transit agencies, as well as a tool for understanding truck trip generation on highways. Many of these new techniques take advantage of archived intelligent transportation systems (ITS) data. Examples of other innovative data sources include global positioning systems (GPS), geographic information systems (GIS), computer-aided design (CAD), and a variety of visualization tools available for use with travel survey data. As these various techniques and software applications move forward, consideration needs to be given to how the “lessons learned” from these applications can facilitate the use of data visualization techniques for travel survey data analysis and decision-making.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

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 study aims to examine the use of data visualization as a tool to support practitioner-led organizational learning within the emergency services. The authors investigate how data visualization can support visual communication and the analysis of emergency response data to promote system improvement.

The authors investigate if communication data, presented as node-link diagrams, can be understood and evaluated by firefighters. Objective understanding of the communication network is measured quantitatively, while subject judgement of the emergency response system is measured qualitatively and compared to prior system evaluation outcomes. The authors compare different data visualization layouts and assess their value in supporting practitioner evaluation of emergency response systems.

The authors find that while firefighters are largely unfamiliar with their use, data visualizations function as a tool for visual communication and analysis. The authors identify the importance of visualization design and the difficulty in representing characteristics of a dynamic network within static diagrams. The authors also find some correlation between layout design and how respondents interpret visual data.

Results demonstrate the value of data visualization to support practitioner-led organizational learning and suggest future work to support the development of emergency response management.

This paper aims to examine major cognitive biases in auditors’ analyses involving visualization, as well as proposes practical approaches to address such biases in data visualization.

Using the professional judgment framework of KPMG (2011), this study performs an analysis of whether and how five major types of cognitive biases (framing, availability, overconfidence, anchoring and confirmation) may occur in an auditor’s data visualization and how such biases potentially compromise audit quality.

The analysis suggests that data visualization can trigger and/or aggravate the common cognitive biases in audit. If not properly addressed, such biases may adversely affect auditors' judgment and decision-making.

To ensure that data visualization improves audit efficiency and effectiveness, it is essential that auditors are aware of and successfully address cognitive biases in data visualization. Six practical approaches to debias cognitive biases in auditors’ visualization are proposed: using data visualization to complement rather than supplement traditional audit evidence; positioning data visualization to support rather than replace sophisticated analytics tools; using a dashboard with multiple dimensions; using both visualized and tabular data in analyses; assigning experienced audit staff; and providing pre-audit tutorials on cognitive bias and visualization.

The study raises awareness of psychological issues in an audit setting.

Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue.

A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data.

The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption.

Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages.

The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession.

A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

Data literacy – the ability to read, analyze, interpret, evaluate and argue with data and data visualizations – is an essential competency in social studies. This study aims to examine the degree to which US state standards require teachers to teach data literacy in social studies, addressing the questions: to what extent are US social studies teachers required to teach data literacy? If they are required to teach it, are they provided with guidance about competencies to address at each school or grade level and with respect to particular content?

The study used content analysis, using a variety of priori and emergent codes, to review social studies standards documents from all 50 states and the District of Columbia.

Findings indicate that although state standards suggest that data visualizations should play a role in social studies instruction, they provide poor guidance for a coherent, progressive and critical approach across grade levels.

Researchers currently know little about if and how teachers address data literacy in social studies education. This study provides a snapshot of guidance teachers across states are given for teaching data literacy, and by extension, the quality of data literacy instruction recommended for students across the USA.

The purpose of this paper is to develop data visualisation proof of concept prototypes that will enable the Curtin University Library team to explore its users’ information-seeking behaviour and collection use online by analysing the library’s EZproxy logs.

Curtin Library’s EZproxy log file data from 2013 to 2017 is used to develop the data visualisation prototypes using Unity3D software.

Two visualisation prototypes from the EZproxy data set are developed. The first, “Global Visualisation of Curtin Research Activity”, uses a geographical map of the world as a platform to show where each research request comes from, the time each is made and the file size of the request. The second prototype, “Database Usage Visualisation”, shows the use of the library’s various subscription databases by staff and students daily, over a month in April 2017.

The paper has following limitations: working to a tight timeline of ten weeks; time taken to cleanse noise data; and requirements for storing and hosting the voluminous data sets.

The prototypes provide visual evidence of the use of Curtin Library’s digital resources at any time and from anywhere by its users, demonstrating the demand for the library’s online service offerings. These prototype evidence-based data visualisations empower the library to communicate in a compelling and interesting way how its services and subscriptions support Curtin University’s missions.

The paper provides innovative approaches to create immersive 3D data visualisation prototypes to make sense of complex EZproxy data sets.