Abstract
Citizens are more and more encouraged to participate in public policy decision processes and, therefore, critical questions regarding our lives are asked every day. Informed citizens need access to data, and knowledge in order to explore, understand, and reason about information of a multivariate nature; it is not obvious how to access such data, or how to work with them. Educators face the challenge of adopting new approaches, and grasping new opportunities in order to support the development of students into informed citizens as adults. Educators often do not have time to locate information sources; moreover, it is a challenge to exploit the possibilities of open data wisely. This chapter points to data sets we have found valuable in teaching Civic Statistics; data must be authentic, and reflect the complexities of data used to inform decision making about social issues (whose features are explained in Chap. 2). Topics include refugees, malnutrition, and climate change. We provide enough details so teachers can locate and employ these data sets, or similar ones, as part of regular instruction. Information is made accessible using the innovative tool CivicStatMap, developed to provide access to teaching materials, along with data and analysis tools, including tools to support data visualisation. © Springer Nature Switzerl and AG 2022.

Abstract
In recent years, it has been increasingly necessary for citizens to understand real life statistical data—an ability that is rarely taught in schools, where the majority of tasks in statistics classes contain fictional data without context and make no demands on students to explore or explain. Since most real-world phenomena are multivariate (See Chap. 2), there is a need to develop students’ abilities dealing with complex data and stories they encounter in the media, in order to help prepare them for informed citizenship. The ProCivicStat project has developed materials to support teaching and learning, in the form of detailed lesson plans; a large repository of resources (http://iase-web.org/islp/pcs/) (in several languages) is freely available. This chapter describes our approach to the development of teaching resources. It introduces our storytelling approach in lesson plans, where we use real data in context to encourage students to explore and understand complex data, produce narrative accounts, and often make recommendations about appropriate social actions. The structure of this chapter is as follows: we start with a brief introduction on problems in most tasks commonly encountered in statistics education, and the need for real data in statistics teaching (Sect. 7.1), followed by the presentation of the milestones that are important for creation of lesson plans (Sect. 7.2), and after that we address the use of real data and our storytelling approach (Sect. 7.3). In Sect. 7.4 we talk briefly about empowering teachers (Sect. 7.4) and describe the teachers’ version of the lesson plan (Sect. 7.5). In Sect. 7.6 we present the guidelines for designing student activities, then proceed with an excerpt of a lesson plan to exemplify products of the proposed guidelines (Sect. 7.7). We then highlight the visualization tools that help promote the data exploration step (Sect. 7.8), and finish with a conclusion (Sect. 7.9). © Springer Nature Switzerl and AG 2022.

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

Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
An increasing area of study for economists and sociologists is the varying organizational structures between business networks. The use of network science makes it possible to identify the determinants of the performance of these business networks. In this work we look for the determinants of inter-firm performance. On one hand, a new method of supervised clustering with attributed networks is proposed, SUWAN, with the aim at obtaining class-uniform clusters of the turnover, while minimizing the number of clusters. This method deals with representative-based supervised clustering, where a set of initial representatives is randomly chosen. One of the innovative aspects of SUWAN is that we use a supervised clustering algorithm to attributed networks that can be accomplished through a combination of weights between the matrix of distances of nodes and their attributes when defining the clusters. As a benchmark, we use Subgroup Discovery on attributed network data. Subgroup Discovery focuses on detecting subgroups described by specific patterns that are interesting with respect to some target concept and a set of explaining features. On the other hand, in order to analyze the impact of the network's topology on the group's performance, some network topology measures, and the group total turnover were exploited. The proposed methodologies are applied to an inter-organizational network, the EuroGroups Register, a central register that contains statistical information on business networks from European countries.