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Algorithms are finite pieces of statements or instructions that are used together to solve a problem. Data and the process applied on the data constitute the scope of our application. Algorithms and data structures along with methods to store and process that data are an integral part of computer science. It is necessary today that students and instructors have a clearer and better understanding of how the common algorithms function and how data behaves. We have created an application that allows its users to perform both of these crucial tasks in a platform-independent fashion. Data visualization means presenting data in a graphical or pictorial form clearly and effectively. There are few standalone-agnostic data visualization and algorithm simulation applications. However, they implement very few Data Structures and Algorithms, not covering different types of algorithms. We have implemented algorithms from different areas in computer science such as Machine Learning, Data Structures, and Algorithms, Data Visualizations, etc. There are various platforms for each domain however very few platforms provide the facility to implement on a single platform. Our web or desktop application would possibly include algorithms of major domains for Educational purposes. [ABSTRACT FROM AUTHOR]

Introduction/AimData visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface.Materials and MethodsIndividual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008–2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the “rsatscan” and “shiny” packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.ResultsUsing the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence.DiscussionThe challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making.ConclusionsThe results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

This paper describes the Reticulum workshop, a teaching experiment focused on the concept of network, involving students from two master’s courses (design and digital humanities). The workshop takes up the characteristics of the Reticulum conference cycle: interdisciplinary, interprofessional, intergenerational, intersecting knowledge and know-how. We present our approach as research-based teaching (with collaborative experimentation and knowledge production) and teaching-based research (with a study of conceptual pathways, technical choices and design mechanics). We analyse the workshop and its traces in order to show that the experiment is viable and reproducible; we examine the appropriation of the different techniques used during the workshop, and explain the underlying logics; finally, we describe the importance of modeling and visualising for reflective work, distinguishing between actual connectivity and interpretative connectivity.

Data lies at the heart of the digital society. Their growing importance leads us to believe that data education is essential. It can be considered as one of the pillars of digital humanities education. The curricula for digital sciences and technology (SNT) in tenth grade in France (first year of the lycée) include teaching data. We seek to know how it contributes to data education. We are conducting an analysis of the SNT curriculum and textbooks in the light of the model of data education proposed by researchers in computer science education. Our results show that this teaching subject has limited contribution to data education, particularly in its analytical and critical dimensions.

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.

The pharmaceutical industry is going through a significant change to adopt smart manufacturing for more integrated supply chains and improved sustainability. Today's competitive market demands have put pressure on healthcare systems to take a comprehensive assessment of the drug life cycle, its environmental effect, industrial use of energy and resources, supply chain, and impact on end-users. The exploitation of emerging Industry 4.0 technologies will allow a sustainable process design and personalised health care system through the realisation of digital twins, which could transform the pharmaceutical sector to be more flexible, robust, adaptive, and smart. A significant level of research and development has been applied to pharmaceutical manufacturing especially in existing, outdated design and scale-up paradigms in isolated unit operations. However, addressing the key challenges in pharmaceutical manufacturing requires whole systems approaches to incorporate Industry 4.0 concepts.This paper aims to share the latest development of an advanced digital twin of a continuous wet granulation and tableting process at The University of Sheffield. These include the delivery of a digital platform consisting of an Advanced Process Control system (APC), mechanistic model platform and industrial IoT platform for data analytics and visualisation. The combined solution aligns with the concepts of Industry 4.0 by providing a digital twin, cloud integration, sophisticated statistical, as well as hybrid and mechanistic models. The models are in turn, used for soft-sensors, Model Predictive Control and Optimisation algorithms to predict and control product Quality Attributes. The potential application of digital twins in the pharmaceutical industry will also be explored.

Abstract Background The case-crossover design is suited to medication safety studies but is vulnerable to exposure misclassification. Using the example of tricyclic antidepressants and the risk of hip fracture, we present a data visualisation tool for observing exposure misclassification in case-crossover studies. Methods A case-crossover study was conducted using Australian Government Department of Veterans’ Affairs claims data. Beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012 were included. The case window was defined as 1–50 days pre fracture. Control window one and control window two were defined as 101–150 and 151–200 days pre fracture, respectively. Patients were stratified by whether exposure status changed when control window two was specified instead of control window one. To visualise potential misclassification, each subject’s tricyclic antidepressant dispensings were plotted over the 200 days pre fracture. Results The study population comprised 8828 patients with a median age of 88 years. Of these subjects, 348 contributed data to the analyses with either control window. The data visualisation suggested that 14% of subjects were potentially misclassified with control window one while 45% were misclassified with control window two. The odds ratio for the association between tricyclic antidepressants and hip fracture was 1.18 (95% confidence interval = 0.91–1.52) using control window one, whereas risk was significantly increased (odds ratio = 1.43, 95% confidence interval = 1.11–1.83) using control window two. Conclusions Exposure misclassification was less likely to be present with control window one than with an earlier control window, control window two. When specifying different control windows in a case-crossover study, data visualisation can help to assess the extent to which exposure misclassification may contribute to variable results.

Abstract Citizen science is frequently cited as a successful approach for increasing public engagement with environmental issues, but this requires a purposeful design that is inclusive of, and responsive to, diverse interests. This paper explores the mechanisms for improving participant and public engagement with citizen science results, using the New Zealand Garden Bird Survey (NZGBS) as a case study. It investigates how citizen science can apply democratic processes to be more responsive, while drawing on insights from behaviour change frameworks to facilitate a purposeful design. By inviting NZGBS participants to select, inform and peer review the design and promotion of new resources, our goal was to embed their values, opinions and perspectives into the developments. This not only empowered 15,844 respondents to contribute directly to the citizen science initiative's governance over 6 years, but also made it more engaging and useful to them and the wider public. New resources were designed to create a sense of collective action, making them attractive, easy to understand and promote on multiple media channels, aiming to reach a wider range of audiences. By diversifying and refining our communication strategy, we successfully enhanced the level and nature of engagement with the resources. The number and diversity of NZGBS participants also increased to involve people from a wider range of backgrounds and roles, ethnicities and ages. By applying a democratic process, we demonstrate how citizen scientist perspectives were elevated, to offset and mitigate the influence of institutional powers, which can dominate and shape engagement processes. We also highlight the value of behaviour change frameworks for understanding how choice architecture, social networks and key influencers, and their complex interactions can create the enabling environment required for engagement. A free Plain Language Summary can be found within the Supporting Information of this article.

Abstract Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end‐devices. This enhances the quality of service (QoS) to the proximity of end‐users as latency‐free processing is obtained in cloud‐IoT‐based environs. Mining industry will encounter a level‐up due to these technological advancements like fog‐IoT‐based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time‐sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state‐of‐art of fog, IoT, and cloud architectures are provided, and a Fog‐IoT mines monitoring (FIoTMM) system for real‐time monitoring of open‐cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines.

The textual data of a document is supplemented by the graphical information in it. To make communication easier, they contain tables, charts and images. However, it excludes a section of our population - the visually impaired. With technological advancements, the blind can access the documents through text to speech software solutions. In this method, even images can be conveyed by reading out the figure captions. However, charts and other statistical comparisons which involve critical information are difficult to be “read” out this way. Aim of this paper is to analyse various methods available to solve this vexatious issue. We survey the state-of-the-art works that do the exact opposite of graphing tools. In this paper, we explore the existing literature in understanding the graphs and extracting the visual encoding from them. We classify these approaches into modality-based approaches, conventional and deep-learning based methods. The survey also contains comparisons and analyses relevant study datasets. As an outcome of this survey, we observe that: (i) All existing works under each category need decoding in a variety of graphs. (ii) Among the approaches, deep learning performs remarkably well in localisation and classification. However, it needs further improvements in reasoning from chart images. (iii) Research works are still in progress to access data from vector images. Recreating data from the raster images has unresolved issues. Based on this study, the various applications of decoding the graphs, challenges and future possibilities are also discussed. This paper explores current works in the extraction of chart data, which seek to enable researchers in Human Computer Interaction to achieve human-level perception of visual data by machines. In this era of visual summarisation of data, the AI approaches can automate the underlying data extraction and hence provide the natural language descriptions to support visually disabled users.

This paper outlines the development and use of a tool suite developed by the NCI Agency to provide situational awareness and decision support during the current Covid-19. The tool suite was developed to understand how Covid-19 could impact the provision of communication and information services (CIS) to NATO, and so understand where risks to NATO operational functions might occur. The tool suite combines open source data on instances of Covid-19 globally along with internal information about the impact of Covid-19 on NCI Agency staff and the services they deliver to the NATO enterprise. It supports business impact assessments due to Covid-19; showing trends, age demographics, and providing early indications of critical services that may be affected, sites that may be affected, etc. The tool suite is an example of data science techniques supporting data driven decision making within a military organization. [ABSTRACT FROM AUTHOR]

Visualisations are often the entry point to information that supports stakeholders’ decision- and policy-making processes. Visual displays can employ either static, dynamic or interactive formats as well as various types of representations and visual encodings, which differently affect the attention, recognition and working memory of users. Despite being well-suited for expert audiences, current climate data visualisations need to be further improved to make communication of climate information more inclusive for broader audiences, including people with disabilities. However, the lack of evidence-based guidelines and tools makes the creation of accessible visualisations challenging, potentially leading to misunderstanding and misuse of climate information by users. Taking stock of visualisation challenges identified in a workshop by climate service providers, we review good practices commonly applied by other visualisation-related disciplines strongly based on users’ needs that could be applied to the climate services context. We show how lessons learned in the fields of user experience, data visualisation, graphic design and psychology make useful recommendations for the development of more effective climate service visualisations. This includes applying a user-centred design approach, using interaction in a suitable way in visualisations, paying attention to information architecture or selecting the right type of representation and visual encoding. The recommendations proposed here can help climate service providers reduce users’ cognitive load and improve their overall experience when using a service. These recommendations can be useful for the development of the next generation of climate services, increasing their usability while ensuring that their visual components are inclusive and do not leave anyone behind. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia (Monash IA Lab). The lab has been influential with contributions in algorithms, interaction techniques and experimental results in Network Visualisation, Interactive Optimisation and Geographic and Cartographic visualisation. It has also been a leader in the emerging topic of Immersive Analytics, which explores natural interactions and immersive display technologies in support of data analytics. We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.

In this essay, we critically reflect on digital disruption in journalism and journalism education with specific focus on the South African context. After contextualising the problematics in terms of what Castells terms the “information technology revolution”, we define data visualisation and survey the existing literature on the subject. The history of journalism education in South Africa is briefly revisited before assessing the current state of the profession in the country. The dangers posed by digital visualisation to the core ethos and function of journalism as a vehicle in the service of contributing to a resilient democracy is thrown into relief by utilising critical concepts from the work of Foucault, Habermas and Fuchs. We subsequently cite a few examples of the undisclosed bias inherent in data visualisation. In conclusion, we consider the feasibility and potential effects of the necessity to adopt data visualisation techniques on journalism in South Africa.

The Reception Reader is a web tool for studying text reuse in the Early English Books Online (EEBO-TCP) and Eighteenth Century Collections Online (ECCO) data. Users can: 1) explore a visual overview of the reception of a work, or its incoming connections, across time based on shared text segments, 2) interactively survey the details of connected documents, and 3) examine the context of reused text for “close reading”. We show examples of how the tool streamlines research and exploration tasks, and discuss the utility and limitations of the user interface along with its current data sources.

The research looks into the methods of visual representation of points accumulated by students in a single study course that are employed in modern data science. Statistical indicators and prospects for improving the subject based on them are discussed: which parts of the subject should be adjusted to increase the training course's quality. Data processing and display methods that the course instructor can employ effectively in the process of observing continuous visualisation of scores are discussed. The article introduces a novel approach for enhancing the content of a training course through statistical analysis and visual representation of student scores, utilising React.js technology. The proposed platform empowers course instructors to incorporate various assessment methods such as quizzes, presentations, mid-term exams, and final exams and subsequently display students' grades based on each assessment method. The platform employs advanced statistical indicators and robust visual presentation capabilities to calculate and illustrate the points achieved. This enables instructors to easily identify desired evaluation methods, observe the distribution of points, and simultaneously track multiple evaluation methods to monitor the dynamic progression of the training course. Consequently, this holistic approach enables instructors to identify and address problematic sections within the course content, leading to meaningful improvements. [ABSTRACT FROM AUTHOR]

Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life. Real-time analysis of social media can provide valuable feedback and insights to both politicians and news agencies. In this paper, we discuss the design and implementation of a system for tracking and analysing social media. The SocMINT system provides an easy-to-use, visual dashboard to monitor the discussion on specific topics, to capture trends in communities and, by iteratively applying multidimensional data analysis and filtering, to deeply analyse posts and influencers. SocMINT aggregates data from multiple social sources and performs sentiment analysis on textual, visual and mixed content via a specifically designed neural network architecture. The system was applied in a real context by administrative staff of a political party to effectively analyse candidates' political communication on Facebook, Instagram and Twitter and the related online community reactions and discussion. In the paper, we report on this real-world case study, showing how the system meaningfully captures trends in public opinion, comparing the main KPIs provided by SocMINT with the outcomes of traditional polls. [ABSTRACT FROM AUTHOR]

Data visualisation has become ubiquitous in everyday life, from seeing images in news media to tracking individual health indicators. While the effects of data visualisation on society and people have been explored within a range of literature, there has been far less attention paid to the interconnectedness of data visualisation and policy making. In this special issue, we explore how data visualisation matters for policy priorities, processes and outcomes; how it reflects the demands and constraints posed by specific policy problems; and finally, what data visualisations reveal about broader political, social, and cultural shifts and the implications for policy. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Ciências da Computação is the property of Universidade Aberta and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Business Intelligence (BI) is known to make smart decisions in various fields. BI proves beneficial for better-visualising of data through reports, charts, ad-hoc queries, dashboards, and benchmarks. Choosing the appropriate BI tools for an organisation may result in higher profit margins. BI tools are usually self-efficient and have a wide scope for data analysis. Furthermore, BI tools can also be used to aid results and monitor business aspects over a long period. This paper reviews fifteen open-source BI tools and analyzes comparisons of these tools based on user reviews while keeping track of the features offered specifically to each BI Tool. The results give an empirical study of BI tools in the hope to better assist users when faced with having to make decisions on which tools are superior as well as giving reasons behind such choices. [ABSTRACT FROM AUTHOR]

Aroaro is a mixed-reality, multi-user platform, which can be deployed on different virtual reality and augmented reality devices. In Aroaro, users can experience data visualisation. This paper presents our proposed approach to network data visualisation in a business context, which assumes a business unit is tasked with problems that need to reveal information found in a network data set. Aroaro provides users with a novel way to visualise the network in a mixed-reality immersive environment while helping decision-makers retrieve the analytics toolbox that supports their tasks. We report on experimentation with subjects who participated in lab sessions to qualitatively compare aspects of their decisions performed on two visualisation platforms. Our objective at this stage is to infer how and when our immersive, virtual reality visualisation tool turns out to be superior to alternative 2D flat-screen visualisation facilities. [ABSTRACT FROM AUTHOR]

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called "smplot" for easy and elegant data visualization. The R package "smplot" generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/). [ABSTRACT FROM AUTHOR]

Hiring academic staff into departments and supporting them remains the single costliest activity that most institutions of higher learning engage in and requires careful, long-term, data-driven planning. This study identifies widely available (but seldom actually used) variables needed for this process: available instructional workload units and student credits. While doing so, this study also walks through best practices in extracting such variables from administrative systems in repeatable, auditable ways using stock database design tools and methods. Finally, this study (literally) illustrates, in practice, how surfacing such variables in readily interpretable tables, pictures, and interactive dashboards can facilitate their application and use, by providing administrators with ways of presenting these data to peers and oversight boards in transparent, ideologically neutral, but yet actionable, formats. Although this study brings considerable quantitative data to bear, it is, at its core, a practical study in how academic administrators should be managing their data—the 'new gold'. [ABSTRACT FROM AUTHOR]