Abstract
How might you use data visualisation in your teaching? Here, we offer some ideas, and some provocations to review your teaching. We begin with an invitation to examine some of the historical landmarks in data visualisation (DV), to classify the data presented, and to describe the benefits of a sample of the DV to users. Early uses of DV by Nightingale and Neurath are shown, to provide examples of DV which communicated the need for action, and provoked social change. A number of modern DVs are presented, categorised as: tools to display individual data sets and tools for the exploration of specific rich data sets. We argue that students introduced to the core features of Civic Statistics can acquire skills in all of the facets of Civic Statistics set out in Chap. 3. We conclude by revisiting Herschel, to provoke thoughts about the balance of activities appropriate to statistics courses. © Springer Nature Switzerl and AG 2022.

Abstract
Wearable Health Devices (WHDs) are increasingly becoming an integral part of daily life and significantly contributing to self-monitoring in healthcare. WHDs have a wide range of applications, ranging from sports to clinical settings, where the monitoring of cardiovascular health, particularly through ECG, plays a crucial role. This study introduces a unique WHD called VitalSticker, which exhibits distinctive features such as having a comfortable tiny patch form-factor to be attached to the chest, collecting multiple vital signs with medical-grade quality (ECG, respiration, temperature and actigraphy) and seamlessly sending data to a companion app. This paper encompasses a detailed description of the hardware, firmware, and case design of the WHD. A study was conducted to assess the quality of the ECG signal acquired by VitalSticker, comparing it with the signal obtained from a CE medical-grade certified ambulatory device. The results demonstrate that our VitalSticker achieves similar medicalgrade quality when compared to the reference device, surpassing its counterpart in several specifications. Furthermore, this study presents the successful implementation of an ECG baseline wander correction filter that runs on the tiny on-board wearable microcontroller without introducing any artifacts into the ECG signal, reducing the need for further processing for this outside the wearable patch.

Abstract
Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

Abstract
The use of social media in health is emerging as a means of bringing the various actors together with several benefits. In the specific case of cancer disease, these tools can help patients to improve their psychological well-being and their outcomes. As cancer is the cause of a quarter of deaths in Portugal, it is a pressing issue to understand which tools and information both patients and health professionals find most useful to build effective health social media. It was observed that there is a latent need for an oncology social environment, allowing greater well-being for patients and strengthening their relationship with health professionals and institutions, constituting an asset to the services provided. This chapter fills a gap in the bibliography by bringing together the views of both patients and health professionals from several areas, in close collaboration with the Francisco Gentil Portuguese Oncology Institute of Porto, E.P.E. © 2024, IGI Global. All rights reserved.

Abstract
The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.

Abstract
Dashboard for online higher education support monitoring and evaluation of students’ interactions, but mostly limited to interaction occurring within learning management systems. In this study, we sought to find which collaborative learning activities and tools in online higher education are included in teaching dashboards. By following Kitchenham’s procedure for systematic reviews, 36 papers were identified according to this focus and analysed. The results identify dashboards supporting collaborative tools, both synchronous and asynchronous, along categories such as learning management systems, communication tools, social media, computer programming code management platforms, project management platforms, and collaborative writing tools. Dashboard support was also found for collaborative activities, grouped under four categories of forum discussion activities, three categories of communication activities and four categories of collaborative editing/sharing activities, though most of the analysed dashboards only provide support for no more than two or three collaborative tools. This represents a need for further research on how to develop dashboards that combine data from a more diverse set of collaborative activities and tools.