We will develop a data science of the natural environment, deploying modern machine learning and statistical techniques to enable better-informed decision-making as our climate changes. While an explosion in data science research has fuelled enormous advances in areas as diverse as eCommerce and marketing, smart cities, logistics and transport, health and wellbeing, these tools have yet to be fully deployed in one of the most pressing problems facing humanity, that of mitigating and adapting to climate change. This project brings together world-leading statisticians, computer scientists and environmental scientists alongside an extensive array of key public and private stakeholder organisations to effect a step change in data culture in the environmental sciences. The project will develop a new approach to data science of the natural environment driven by three representative grand challenges of environmental science: predicting ice sheet melt, modelling and mitigating poor air quality, and managing land use for maximal societal benefit. In each motivational challenge, there is already an extensive scientific expertise, with intricate models of processes at multiple scales. However this sophisticated modelling of system components is usually let down by naive integration of these components together, and inadequate calibration to observed data. The consequence is poor predictions with a high level of uncertainty and hence poorly-informed policy making. As new forms of environmental data become available, and the pressures on our natural environment from climate change increase, this gap is becoming a pressing concern, and we bring an impressive team to bear on the problem. A key theme of the project is integration, developing a suite of novel data science tools which work together in a modular fashion, and with existing scientifically-informed process models. By building a team that spans the inter-disciplinary divisions between data and environmental scientists we can ensure the necessary interoperability of methods that is currently lacking. Working with the full range of stakeholder environmental organisations will enable continual co-design of the programme and training of end-user scientists to ensure a reduction of the skills gap in this area. The resultant culture shift in the data literacy of the environmental sciences will enable better decision-making as climate change places ever greater strains on our society.

The Future Places Centre will explore how ubiquitous and pervasive technologies, the IoT, and new data science tools can let people reimagine what their future spaces might be. Today, the footprint of such systems extends well beyond the work environments where they first showed themselves and are now, quite literally, ubiquitous. Combined with advances in data science, particularly in the general area of AI, these are enabling entirely new forms of applications and expanding our understanding of how we can shape our physical spaces. The result of these trends is that the potential impact of these systems is no longer confined to work settings or the scientific imagination; it points towards all contexts in which the relationship between space and human practice might be altered through digitally-enabled comprehension of the worlds we inhabit. Such change necessitates enriching the public imagination about what future places might be and how they might be understood. In particular, it points towards new ways of using pervasive technologies (such as the IoT), to shape healthy, sustainable living through the creation of appropriate places. To paraphrase Churchill: if he said we make our buildings, and our buildings come to shape us, the Future Places centre starts from the premise that new understanding of places (enabled by pervasive computing, data science and AI tools), can be combined with a public concern for sustainability and the environment to help shape healthier places and thus make healthier people. It is thus the goal of the centre to reimagine and develop further Mark Weiser's original vision of ubiquitous computing. As it does this so it will cohere Lancaster's pioneering DE projects and create a world-class interdisciplinary research endeavour that binds Lancaster to the local community, to industry and government, making the North West a test-bed for what might be.

The greenhouse gases arising from food account for more than ten percent of the UK carbon footprint. Carbon-related food interventions such as smartphone apps have largely targeted supermarket shopper decisions 'in store' and have yet shown little evidence of impact. To be truly relevant, we must seek nuanced understandings of food acquisition both within and beyond the supermarket: these include the negotiation and planning of household meals, and positioning with respect to take-away and convenience foods. Using multidisciplinary methods as diverse as ethnographic observation, interaction design, carbon profiling and crowdsourcing, we will iteratively develop and trial both low and high tech interventions which meaningfully support food acquisition and carbon awareness, with the aim of promoting lower impact food practice. Built into the research programme is continual engagement with the supermarket itself, to explore opportunities for making scalable, sustained reductions, in its role as a food institution for tens of thousands.

ICT now consumes approximately 10% of global electricity, with large-scale ICT systems such as Cloud datacentres, IoT, and HPC systems generating a substantial ICT footprint in terms of energy consumption and GHG emissions, and are growing contributors to climate change. Researchers across Computer Science and various engineering disciplines have predominantly tackled this problem via enhancing the energy-efficiency of individual components (software, servers, networking, cooling) via improvements to scheduling, software optimisation, hardware, and cooling. However, enhancing system component efficiency has still resulted in a growing global ICT footprint - more data, greater compute ability, and more devices. This is due to the rebound effect, whereby technological progress enhances system efficiency, however increases the rate of consumption and end-use demand. This is of increasing concern given the end of Moore's law, growing global digital service consumption, and the rise of Big Data and AI services in society - all when combined result in a rapidly increasing ICT footprint. It is no longer possible to rely on the conventional perception that 'green' large-scale ICT systems can be achieved just by solely improving component energy-efficiency. There needs to focused effort to actually reverse the global ICT footprint. We believe that this problem is not insurmountable however, yet requires a radical rethink how large-scale ICT systems are designed and operate. A system's ICT footprint is a by-product of its operation; we propose to inverse this dynamic - whereby system operation is instead a by-product of, and directly dictated by, its ICT footprint. What is required isn't greater efficiency, but instead precise control over how ICT systems operate and respond to energy levels and footprint targets; a significant research challenge given the sheer scale and complexity in understanding the relationship between ICT footprint manifestation, component interactions, and the impact of organisational sustainability practises. This challenge is further compounded by potential organisational resistance who may champion commercial profits over environment concerns. However, overcoming this challenge would allow ICT systems operation to be directly matched to energy generated from renewable sources, adhere to a specified GHG emission targets defined at organisational or national level, or dynamically align with an organisation's commercial targets or OpEx restrictions. This fellowship will design a large-scale ICT system capable of self-adapting its operation in response to energy availability and ICT footprint targets. This specifically entails: (1) Studying of causes of ICT footprint manifestation within technology organisations, and understand the rationale and impact of enacting sustainability practises. (2) Determine and model the precise relationship between complex ICT component interactions and resultant ICT footprint. (3) Design a self-adaptive framework that coordinates ICT energy-efficient decision making holistically. (4) Create a holistic resource manager underpinned by energy availability and ICT footprint targets. This fellowship is backed by a consortium of industrial and academic Computer Science and sustainability collaborators in the UK and beyond, and will be underpinned by considerable empirical analysis and experimentation in both production and laboratory CPU/GPU-based datacentre and HPC systems. Findings from this fellowship are potentially ground breaking towards designing future digital infrastructure in the face of environmental change. Our key outcomes include: - Reducing ICT system energy use between 25-50% with no software performance penalty. - Demonstrating the feasibility to reverse global ICT footprint growth via unshackling system operation from the rebound effect. - Releasing the largest in-depth operational and energy data from real-world ICT systems.