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Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

Purpose – The aim of this study is to determine the effects of inquiry instruction incorporating with STEM learning on Chemistry Education of Malikussaleh University students’ science process skills and science attitudes.

Design/Methodology/Approach – The pre-experimental design, which is a mixed method approach is used in the study that included a pretest-posttest one group model and descriptive quantitative.

Findings – As a result of data analysis that STEM learning significantly enhances students’ science process skills and attitudes toward inquiry instruction. This study examines how participation in a semester long inquiry-based STEM learning project that involves interdisciplinary skills, sociological research on attitudes, and behaviors enhances the scientific and quantitative literacies of STEM students.

Research Limitations/Implications – Quantitative research is needed to determine the more common effects of learning outcomes. However, this study only determines a self-assessment on science attitudes. The other one is a limitation on the participants and reviewing aspects of learning with more variables in order to obtain more optimal results.

Practical Implications – The results of this study have practical implications in terms of hands-on activities. The learning model can be used to explain the concept of multidisciplinary studies and particularly to students and their parents. It will be a useful model for lecturers, personal tutors, and any other practitioners involved in hands-on activities.

Originality/Value – This paper innovative at a conceptual level of education development for students, graduates, and it is very simple descriptive papers. It will be of value to anybody with an interest in education competitiveness issues.

This chapter describes how researchers and developers may improve the design of technical innovations for crisis communicators by testing how user-friendly the innovation is for its intended end users. In the RESCUE project, a tool for social media information gathering was developed. During this process, tool usability was thoroughly tested. Good usability allows the user to complete tasks and achieve goals with effectiveness, efficiency and satisfaction. The purpose of the usability testing was to strive for a tool that is easy to use during demanding circumstances and contributes to a high level of situation awareness (SA) among users. SA is about being aware of what is happening around you – during, for example, emergency assignments – and what this means for your on-going work tasks. The main focus of this chapter is to describe how usability testing was applied throughout the tool development process, from the pre-production planning phase to the final phase. As a part of this, the tool features are described.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

Investments in gender training for agricultural researchers have not attained expected outcomes, bringing into question the efficacy of training approaches used. New approaches for transformative gender training need to draw on lessons learned from previous courses. This chapter analyses short gender training courses identified using a scoping methodology. Selected courses offered between 2005 and 2015 for scientists in Eastern Africa were critiqued against a theoretical framework for transformative gender training. Also shared is a training model (Gender Responsive Researchers Equipped for Agricultural Transformation course) that addresses gaps in previous courses. The chapter identifies critical lessons for facilitating transformative gender training for non-gender research practitioners which include the need for inter-disciplinary grounding in the disciplines of gender and agriculture, having a phased course delivery and the value of continuous technical support during and after training. Gender training models should also allow for the deepening of gender awareness and consciousness by providing safe spaces for personal reflections on the root causes of gender inequalities and for the questioning of the internalized norms and biases.

Purpose – The purpose of this research is to study the ecological developments that are growing rapidly and are complemented by technological developments that make ecology a discipline which is able to collaborate, integrate, and use data for the development of science.

Design/Methodology/Approach – The method involves integration, analysis, and conclusion, drawing knowledge dissemination from heterogeneous ecological data that make the ecological research so complex requiring an approach to simplify the problem.

Findings – The data involved in ecology are very complex and diverse and spread from various sources, which are not mutually integrated so that a structured arrangement is required through the arrangement of computer-based data management.

Research Limitations/Implications – Eco-informatics is one of the options to manage the data, settings, and transform it into information and knowledge.

We illustrate the potential of diaries for advancing scholarship on organization studies and grand challenges. Writing personal diaries is a time-honored and culturally sanctioned way of animating innermost thoughts and feelings, and embodying experiences through self-talk with famous examples, such as the diaries written by Anne Frank, Andy Warhol, or Thomas Mann. However, the use of diaries has long been neglected in organization studies, despite their historical and societal importance. We illustrate how different forms of analyzing diaries enable a “deep analysis of individuals’ internal processes and practices” (Radcliffe, 2018) which cannot be gleaned from other sources of data such as interviews and observations. Diaries exist in different forms, such as “unsolicited diaries” and “solicited diaries” and have different purposes. We evaluate how analyzing diaries can be a valuable source to illuminate the innermost thoughts and feelings of people at the forefront of grand challenges. To exemplify our arguments, we draw on diaries written by medical professionals working for Doctors Without Borders as part of our empirical research project conducted in extreme contexts. We show the value of unsolicited diaries in revealing people’s thought world that is not apprehensible from other modes of communication, and offer a set of practical guidelines on working with data from diaries. Diaries serve to enrich our methodological toolkit by capturing what people think and feel behind the scenes but may not express nor display in public.

The visualization of ranking information in global public policy is moving away from traditional “league table” formats and toward dashboards and interactive data displays. This paper explores the rhetoric underpinning the visualization of ranking information in such interactive formats, the purpose of which is to encourage country participation in reporting on the Sustainable Development Goals. The paper unpacks the strategies that the visualization experts adopt in the measurement of global poverty and wellbeing, focusing on a variety of interactive ranking visualizations produced by the OECD, the World Bank, the Gates Foundation and the ‘Our World in Data’ group at the University of Oxford. Building on visual and discourse analysis, the study details how the politically and ethically sensitive nature of global public policy, coupled with the pressures for “decolonizing” development, influence how rankings are visualized. The study makes two contributions to the literature on rankings. First, it details the move away from league table formats toward multivocal interactive layouts that seek to mitigate the competitive and potentially dysfunctional pressures of the display of “winners and losers.” Second, it theorizes ranking visualizations in global public policy as “alignment devices” that entice country buy-in and seek to align actors around common global agendas.