Circular approaches to design, manufacture and services are proposed as one of the most significant opportunities to radically re-think how we use and re-use finite resources. Pairing the digital revolution with the principles of a Circular Economy (CE) has the potential to radically transform the industrial landscape and its relationship to materials and finite resources, thus unlocking additional value for the manufacturing sector. Despite meaningful success by a handful of manufacturers to move towards more sustainable practices through the use of data-driven intelligence, it is unclear which CE strategy is the most valuable for a business and at what time in a products lifecycle it should be implemented. As such, this research aims to identify how data from products in use can inform intelligent decisions surrounding the implementation of Circular Economy strategies so as to accelerate the implementation of circular approaches to resource use within UK manufacturing. Multiple research efforts and best practice examples have shown that a transition towards a Circular Economy can bring about lasting benefits from a more innovative, resilient and productive economy. This is particularly prevalent for manufacturing as it offers one of the biggest potentials for economic and environmental impact of any sector. It is estimated that materials savings alone in the European Union could amount to USD 630 billion. Digital technology is rapidly becoming a key enabler for unlocking the value from Circular Economy strategies with an estimated 10 billion physical objects with embedded information technology already in existence today and a predicted 50 billion in use by 2020. For the manufacturing sector, the ability to monitor and manage objects in the physical world electronically through data-driven decision-making changes the way that value is created. The capture and analysis of data streams between manufacturing, product and user is already enabling organisations to decouple manufacturing growth from resource consumption through new service offerings, providing customers with added value such as financial savings and safety improvement, and enabling organisations to shift their business model from selling to leasing. This shift in ownership, enabled through access to the right data, brings about a need for manufacturers to design products that last and to integrate processes such as remanufacturing to enable materials and resources to be cycled as many times as possible resulting in significant environmental savings, job creation and up-skilling associated with the development of new processes. Through harnessing digital technological advances to inform decisions on Circular Economy strategies, this research has the opportunity to radically transform UK manufacturing and enable the sector to capture significant value from a Circular Economy that is currently being lost. The originality of this research lies in using data-driven intelligence to optimise the selection of CE strategies for products and the timings of intervention in the product lifecycle. This challenging three year project will bring together an internationally renowned team of experts in Circular Innovation, Manufacturing Informatics and Information Theory from Cranfield University and University of Sheffield drawing on leading-edge strengths of the host institutions and international connections with research communities, companies, business intermediaries and governance at national and international scales. The research team will partner with key players across the manufacturing sector, capable of initiating system level change, to develop novel methods for acquiring and integrating new data streams, uncovering exciting opportunities for new value creation within manufacturing organisations and enabling informed circular interventions surrounding the manufacture and use of products.

Within almost every discipline related to the digital economy, there are critical and emerging issues around humans and the data they generate either directly, or as a byproduct of their endeavours. Equally, the data economy has stimulated a range of initiatives responses within each of the three sectors (public, private and third), as well as a broad portfolio of research across relevant disciplines. However, while such important work is ongoing, such these efforts are often disparate and tend not to feed directly back into the science of data-driven systems itself. There is an urgent need to guide the realisation of system design principles that are productive, and yet fit with the ethics and values acceptable to wider society. Those who are expert in development of the systems, algorithms and analytics that raise such issues face challenging culture gaps: firstly, with regard to those who are expert in areas such as the arts and humanities, and secondly with regard to those who are inexpert in technology but who are increasingly impacted by it in their everyday lives. Core to these divisions are issues such as a lack of social understanding of the technical capabilities of data-driven systems, inconsistency of research and development effort across sectors and disciplines, and tensions between industrial, societal and academic drivers, and human needs. Such tensions are visible in several domains, though few as pointedly critical as health. One need only look at NHS' efforts to protect individuals' medical records, in contrast to contrasted against the corporate monetization of DNA samples, as individuals take advantage of advances in low-cost mobile self-monitoring and diagnosiseek low cost solutions to their health-managements. Here, state, corporate and individual-level drivers create inconsistent approaches to the management and value of data. It is time to draw together, consolidate and formalise our efforts across disciplines. We must now seek to structure further endeavour, while considering how new and emerging systems are realised, received and responded to-not just within the bounds of the DE but cross-sector, i.e. within the range of organisations and communities that reflect and support daily human activity and concern. At a sectoral level, industry has often focused narrowly on either corporate monetisation of data from individuals, or individuals' efficiency and short-term optimisation of personal metrics (e.g. the 'quantified self'). Market pressures mean that technical advances are increasingly implemented before social and cultural effects can be determined. This means, however, that data-intensive systems to support long term social, cultural and creative benefits are rare. At the same time, academic research has often focused on questions of interest more to itself than to other sectors. Academic work with public and third sector organisations has been fragmented, with interactions often weighted in favour of shorter term innovation cycles rather than longer term social needs. Such challenges, divergences and tensions lead to duplications, contradictions, and unproductive effort. This is the problem space within which we operate. Our network a holistic and inclusive network approach, sensitive to the socially situated nature of such systems. To achieve this we will (a) develop and sustain a collaborative, cross-sectoral community under the banner of Human Data Interaction, (b) develop a portfolio of system design projects addressing underexplored aspects of the DE (c) create cross-sectoral interdisciplinary synthesis of research under the HDI banner (d) conceptually develop and flesh-out the HDI framework, (e) create a suite of policy and public-facing case studies, papers, prototypes and educational materials, and (f) develop a set of core guidelines intended to inform the design of human-facing data-driven systems.

The UK has had remarkable success in producing new algorithms in Artificial Intelligence. This has driven two processes -- one is the adoption of existing large-scale data analysis tools to industry problems. The other is the acquisition by large tech companies of UK talent, with the intention that it will significantly shape the future commercial landscape. Whilst there has been dramatic recent progress in AI, the systems are still far from being universally useful. The tech giants are well aware of this and are investing heavily in teams and algorithms that can address these fundamental AI challenges. It is vital that the UK retains the ability to create new AI technologies and does not become merely a consumer and user of AI technologies produced by others. This training centre will spearhead this effort by addressing the core research problems and training students to become the next generation scientists and entrepreneurs.

The IoT represents a convergence of ubiquitous computing and communication technologies, with emerging uses that actuate in the real world. No longer do ubiquitous computing systems simply sense and respond digitally, now they physically interact with the world, ultimately becoming embodied and autonomous. At the same time, the game is changing from one of privacy, where it is often (contestably) cited that "users don't care", to one of user safety, where users (along with regulators, governments, and other stakeholders) certainly do care. Likewise, industry needs to become aware that this shift also changes the legal basis under which companies need to operate, from one of disparate and often weakly enforced privacy laws, to one of product liability. The current widely adopted approach in which cloud services underpin IoT devices has already raised major privacy issues. Importantly in an actuated future, untrammelled communications implicating a plethora of heterogeneous online services in their normal operation also brings with it resilience challenges. We must ensure the integrity of actuating systems, which will require greater local autonomy alongside increased situated accountability to users. This problem applies in many areas: industrial control, autonomous vehicles, and smart cities and buildings, including the intimate and shared context of the home. This research seeks to address the challenge in the context of the home, where the network infrastructure protection is minimal, providing little or no isolation between attached devices and the traffic they carry. Scant attention has been paid by the research community to home network security, and its acceptability and usability, from the viewpoint of ordinary citizens. This research is also deeply rooted in pragmatism and recognises the 'real world, real time' conditions that attach to the IoT: - that the cyber security solutions currently being defined for IoT systems will not deal with legacy issues and will never achieve 100% adoption; - that extant businesses limit the period of time for which they will provide software and security updates (if they even remain in business); - that cyber security is an arms race and threats will continue to emerge in future; - and that the public will never become network security experts.

The EPSRC-ESRC Network in Consumer Goods, Big Data and Re-Distributed Manufacturing (RECODE) aims to develop an active and engaged community through which to identify, test and evaluate a multi-disciplinary vision and research agenda associated with the application of big data in the transition towards a re-distributed manufacturing model for consumer goods. Transforming the consumer goods industry through the use of big data and re-distributed models of manufacture poses entirely new challenges inherent to the capture, storage, analysis, visualisation and interpretation of big data. Combined with this is the cross-disciplinary requirement for radically new methods of engaging end-users, empowering customer interaction, facilitating ad-hoc supply chains, re-capturing and re-deploying valuable materials, optimising manufacturing processes, informing new user-driven design of customised goods and services, developing novel business models and implementing data-driven open innovation. The world generates 1.7 million billion bytes of data every day and global big data technology and services is growing by 40% per year, predicted to reach USD 16.9 billion in 2015. The exponential growth of available and potentially valuable data, often referred to as big data, is already facilitating transformational change across sectors and holds enormous potential to address many of the key challenges being faced by the manufacturing industry including increasing scarcity of resources, diverse global markets and a trend towards mass customisation. The consumer goods industry, one of the world's largest sectors worth approximately USD3.2 trillion, has remained largely unchanged and is characterised by mass manufacture through multi-national corporations and globally dispersed supply chains with 80% of materials ending up in landfill. The role of re-distributed manufacturing in this sector is often overlooked, yet there is great potential, when combined with timely advancements in big data, to re-define the consumer goods industry by changing the economics and organisation of manufacturing, particularly with regard to location and scale. RECODE will develop novel methods to engage communities of academics, international experts, user groups, government and industrial organisations to define and scope the shared multi-disciplinary vision and research agenda. New perspectives and contributions from user groups and stakeholders will be used to ensure that the vision of the network is fully inclusive and sensitive to regional trends, variances and scales. Short-term studies will be undertaken across the breadth of the theme to test and evaluate the feasibility of specific research challenges, the findings of which will contribute to an interactive roadmap representing local and global communities and research agendas of the network. Closing the gap between manufacturers, suppliers and consumers will provide opportunities for personalisation of products and services, up scaling of local enterprise and the development of user-driven products tuned to the requirements of local markets providing economic competitiveness for the UK. Improved understanding of skills and training required for interpreting big data and transforming industries will ensure that the UK can take full advantage of opportunities for job creation. Moving towards a localised and regenerative model of consumer goods manufacture will create more efficient and effective supply chains capable of on-demand responses; increasing productivity and competitiveness of the manufacturing industry. This challenging two year network will bring together an internationally renowned team of experts from Cranfield, Brunel, Cambridge, Manchester and Teesside universities drawing on leading-edge strengths of the host institutions and international connections with research communities, companies, business intermediaries and governance at local, national and international scales.