Abstract
Centrality measures are used in network science to assess the centrality of vertices or the position they occupy in a network. There are a large number of centrality measures according to some criterion. However, the generalizations of the most well-known centrality measures for weighted networks, degree centrality, closeness centrality and betweenness centrality have solely assumed the edge weights to be constants. This article proposes a methodology to generalize degree, closeness and betweenness centralities taking into account the variability of edge weights in the form of closed intervals (interval-weighted networks, IWN). We apply our centrality measures approach to two real-world IWN. The first is a commuter network in mainland Portugal, between the 23 NUTS 3 Regions. The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015.

Abstract
Energy management in buildings can be largely improved by considering adequate forecasting techniques to find load consumption patterns. While these forecasting techniques are relevant, decision making is needed to decide the forecasting technique that suits best each context, thus improving the accuracy of predictions. In this paper, two forecasting methods are used including artificial neural network and k-nearest neighbor. These algorithms are considered to predict the consumption of a building equipped with devices recording consumptions and sensors data. These forecasts are performed from five-to-five minutes and the forecasting technique decision is taken into account as an enhanced factor to improve the accuracy of predictions. This decision making is optimized with the support of the multi-armed bandit, the reinforcement learning algorithm that analyzes the best suitable method in each five minutes. Exploration alternatives are considered in trial and test studies as means to find the best suitable level of unexplored territory that results in higher accumulated rewards. In the case-study, four contexts have been considered to illustrate the application of the proposed methodology.

Abstract
What is the relationship between data science, statistics, and Civic Statistics? Are they symbiotic, or are they in conflict? A graphic on the homepage of the American Statistical Association (https://www.amstat.org/ASA/about/home.aspx?hkey=6a706b5c-e60b-496b-b0c6-195c953ffdbc) reads BIGTENT statistics+data science, indicating their intended direction of travel—statistics and data science need to live together. Products of data science (including social media) have transformed modern life. We outline the idea of disruptive socio-technical systems (DST)—new social practices that have been made possible by innovative technologies, and which have profound social consequences—and we point to some examples of technologies that are, or have capacity to facilitate DST. Civic Statistics aims to address pressing social issues, and data science has created new concerns and also new approaches to work on social issues. Here, we argue that this should go beyond simply addressing known problems, and should include empowering citizens to engage in discussions about our possible futures, including the regulation of potential and actual DST. These are exciting times; there are new approaches to knowing about and understanding the world, many of them associated with data science, and students need to engage with these important epistemological issues as a key element in Civic Statistics skills. Here, we relate features of data science to features of Civic Statistics, and to dimensions of knowledge relevant to Civic Statistics. From the viewpoint of Civic Statistics, we argue that we have a responsibility to prepare students for their roles as spectators (understanding the nature and potential of data science products in creating DST), and as referees (having a political voice about which DST are acceptable and unacceptable), and as players (engaging with data science for their own and others’ benefit). We elaborate on the skills needed for these roles. We argue that citizens should use ideas and tools from data science to improve their lives and their environments. © Springer Nature Switzerl and AG 2022.

Abstract
Ever since there has been an organized collection and use of data for informing decision making, there has been a debate about the extent to which these data have been put to the best use for improving social welfare in terms of general well-being of a community or an entire society. This chapter offers a contribution to that debate, showing how different facets of civic statistics can be translated into action that delivers social impact. We first introduce data movements and how they emerged as a response to the unmet need for data science services to scale social impact of nonprofit and governmental organizations. These movements focused on feasible hands-on projects which are simultaneously educational, impactful, and scalable. Their success is notable, and their operational model applicable in the context of formal educational organizations, as we show using two exemplary cases. The cases offer insights about how organizations can engage with society through civic action and applied data science to create new academic and training programs. Our intention is to share the lessons learned from the data movements and their interactions with educational institutions, also in the context of service-learning, to inspire others to create exciting, engaging educational programs with lasting social impact. © Springer Nature Switzerl and AG 2022.

Abstract
Climate change is an existential threat facing humanity and the future of our planet. The signs of global warming are everywhere, and they are more complex than just the climbing temperatures. Climate data on a massive scale has been collected by various scientific groups around the globe. Exploring and extracting useful knowledge from large quantities of data requires powerful software. In this chapter we present some possibilities for exploring and visualising climate change data in connection with statistics education using the freely accessible statistical programming language R together with the computing environment RStudio. In addition to the visualisations, we provide annotated references to climate data repositories and extracts of our openly published R scripts for encouraging teachers and students to reproduce and enhance the visualisations. © Springer Nature Switzerl and AG 2022.

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.