Chris Davis

Innovation with open data: Essential elements of open data ecosystems

Open data ecosystems are expected to bring many advantages, such as stimulating citizen participation and innovation. However, scant attention has been given to what constitutes an open data ecosystem. The objective of this paper is to provide an overview of essential elements of open data ecosystems for enabling easy publication and use of open data. To achieve this objective, the literature has been reviewed and a scenario about the publication and use of open data has been analyzed. It was found that various applications, tools and portals are available which together can form an ecosystem. The best functionalities of this ecosystem can be selected and utilized by open data providers and users. To create an open data ecosystem at least four key elements should be captured, namely, 1) releasing and publishing open data on the internet, 2) searching, finding, evaluating and viewing data and their related licenses, 3) cleansing, analyzing, enriching, combining, linking and visualizing data and 4) interpreting and discussing data and providing feedback to the data provider and other stakeholders. Furthermore, to integrate the ecosystem elements and to let them act as an integrated whole, there should be three additional elements 5) user pathways showing directions for how open data can be used, 6) a quality management system and 7) different types of metadata to be able to connect the elements.

Industrial Ecology 2.0

Industrial ecology (IE) is an ambitious field of study where we seek to understand systems using a wide perspective ranging from the scale of molecules to that of the planet. Achieving such a holistic view is challenging and requires collecting, processing, curating, and sharing immense amounts of data and knowledge. We are not capable of fully achieving this due to the current state of tools used in IE and current community practices. Although we deal with a vastly interconnected world, we are not so good at efficiently interconnecting what we learn about it. This is not a problem unique to IE, and other fields have begun to use tools supported by the World Wide Web to meet these challenges. We discuss these sets of tools and illustrate how community driven data collection, processing, curation, and sharing is allowing people to achieve more than ever before. In particular, we discuss standards that have been created to allow for interlinking of data dispersed across multiple Web sites. This is currently visible in the Linking Open Data initiative, which among others contains interlinked datasets from the U.S. and U.K. governments, biology databases, and Wikipedia. Since the types of technologies and standards involved are outside the normal scope of work by many industrial ecologists, we attempt to explain the relevance, implications, and benefits through a discussion of many real examples currently on the Web. From these, we discuss several best practices, which can be enabling factors for how IE and the community can more efficiently and effectively meet its ambitions - an agenda for Industrial Ecology 2.0.

Understanding the Evolution of Industrial Symbiosis Research: A Bibliometric and Network Analysis (1997-2012)

This study analyzes the evolution of the research field of industrial symbiosis (IS). We elucidate its embedding in industrial ecology (IE), trace the development of research themes, and reveal the evolution of the research network through analysis of the core literature and journals that appeared from 1997 to 2012 by citation analysis, cocitation analysis, and network analysis. In the first period (1997–2005), IS research held a minority share in the IE literature. The research revolved around the concept of IS, the assessment of eco‐industrial park projects, and the establishment of waste treatment and recycling networks. In the second period (2006–2012), diverse research approaches and theories enriched the field, which has led to a maturation in theory building. Our findings clearly illustrate that IS evolved from practice‐oriented research toward coherent theory building through a systematic underpinning and linking of diverse topics. As scientific attention shifted from exploring a phenomenon to elucidating underlying mechanisms, IS knowledge found worldwide practical implementation. The coauthorship network shows that the academic communities of IS are distributed worldwide and that international collaboration is widespread. Through bibliometric and network analysis of IS, we have created a systemic, quantitative image of the evolution of the IS research field and community, which gives IS researchers an underpinned overview of the IS research and may help them to identify new directions and synergy in worldwide research.

Modeling Metal Flow Systems

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models - an agent‐based model and a system dynamics model - both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.

New Methods for Analysis of Systems-of-Systems and Policy: The Power of Systems Theory,Crowd Sourcing and Data Management

Our world is a complex socio-technical system-of-systems (Chappin & Dijkema, 2007; Nikolic, 2009). Embedded within the geological, chemical and biological planetary context, the physical infrastructures, such as power grids or transport networks span the globe with energy and material flows. Social networks in the form of global commerce and the Internet blanket the planet in information flows. While parts of these global social and technical systems have been consciously engineered and managed, the overall system-of-systems (SoS) is emergent: it has no central coordinator or manager. The emergence of this socio-technical SoS has not been without consequences: the human species is currently facing a series of global challenges, such as resource depletion, environmental pollution and climate change. Tackling these issues requires active policy and management of those socio-technical SoS. But how are we to design policies if policy makers and managers have a limited span of control over small parts of the global system of systems?

Infrastructure modelling 2.0

To support stakeholders involved in infrastructure development, we develop evolutionary models of these complex systems, which is a formidable task with respect to data requirements, information representation and knowledge management. Re-addressing a case on bio-electricity infrastructure evolution, we demonstrate first a series of visualisations of economic and ecologic system parameters as they change during infrastructure development over simulated decades. This setup allows us to demonstrate to stakeholders a means to anticipate the consequences of decisions on (dis)investment of power generation options available. In developing these tools, our approach needed to be expanded to better handle the complexity of infrastructure systems, due to the multiple relevant social and technical contexts from which these systems need to be considered. The second part of this paper describes our work on enabling collaborative mapping of our knowledge of infrastructure systems to help integrate diverse types of knowledge. Current internet-enabled developments such as Web 2.0 and the Semantic Web offer tremendous scope to lower the transaction cost of gathering and assembling data. Already, these are changing the ways scientific collaboration is conducted. Finally, we suggest to connect this to evolutionary models to elucidate the dynamics of these systems.

Integrating Life Cycle Analysis with Agent Based Modeling: Deciding on bio-electricity

In order to evaluate possible bio-electricity infrastructures that may develop subject to economic and ecological decision-making, an Agent Based Model (ABM) was created that uses Life Cycle Analysis (LCA) to analyze the environmental impacts of the infrastructure systems that emerge. By representing processes as distinct instances of technologies, it is possible to have an infrastructure that self-assembles as each owner of any technology must trade with other owners of technologies to satisfy the individual input and output demands. The ABM is used to generate and simulate the complex bio-electricity system evolution, while an LCA is used to analyze it at each simulation tick by presenting the results of calculating life cycle environmental performance. Thereby, a methodology has been created that allows for a type of dynamic LCA, which provides ecological information for decision-making. Foundations, implementation and application of this new methodology for dynamic LCA will be addressed and themes for further research will be discussed.

Comparing industrial symbiosis in Europe: towards a conceptual framework and research methodology

Industrial symbiosis (IS) continues to raise the interest of researchers and practitioners alike. Individual and haphazard attempts to increase linkages among co-located firms have been complemented by concerted efforts to stimulate the development of industrial regions with intensified resource exchanges that reduce environmental impact. Additionally, there are examples of both spontaneous and facilitated linkages between two or more firms involving flows of materials/energy waste. A striking feature of IS activities is that they are found across diverse social contexts and vary considerably in form (Lombardi et al., 2012); there are substantial differences in the ways in which IS manifests itself. Equally diverse are the activities of policy makers to stimulate such linkages. Such diversity can already be found within Europe, as became apparent in a first meeting among some of the present authors in 2009 (Isenmann and Chernykh, 2009). Researchers present there decided to create a network of European researchers on IS, with the explicit aim to develop a comparative analysis. We can thus provide insight to the relationship between the style of IS and its context and thereby the potential for policy makers in different contexts to learn from each other. Policy learning can be a tempting route to IS, but is fraught with difficulties if the influence of context is not appreciated (e.g., Wang et al., Chapter 6, this volume).

Next Steps in Modelling Socio-technical Systems: Towards Collaborative Modelling

In the practice of building models, we have encountered a number of methodological areas that need to be addressed in order to make the modelling process more robust, scalable, maintainable and transparent. In the context of modelling socio-technical systems (existing infrastructures and businesses) models are primarily data-driven: they contain heterogeneous agents, numerous assumptions and facts about agents and their environment. The data (the facts and assumptions used in the models) span multiple disciplines and are contributed by multiple domain experts. The focus of this chapter is to present new options for improving the management of model data. The issue of data management will be addressed at two levels: that of researchers who need to collaborate when creating and maintaining the model data, and that of data management within a simulation model. The methods proposed in this chapter rely on the use of Semantic Web technologies and philosophies to address data management issues. It is the goal of Semantic Web to make data understandable and useful for both machines and humans. In this chapter we will discuss the complications of modelling socio-technical systems and suggest uses of Semantic Web technologies to aid both collaboration between the modellers and knowledge management for the agents.

Self-Organization in Wikis

The notion of inverse infrastructures – that is, bottom-up, user-driven, self-organizing networks – gives us a fresh perspective on the omnipresent infrastructure systems that support our economy and structure our way of living. This fascinating book considers the emergence of inverse infrastructures as a new phenomenon that will have a vast impact on consumers, industry and policy. Using a wide range of theories, from institutional economics to complex adaptive systems, it explores the mechanisms and incentives for the rise of these alternatives to large-scale infrastructures and points to their potential disruptive effect on conventional markets and governance models.