Dynamic networks occur in many fields of science, technology and medicine, as well as everyday life. Understanding their behaviour has important applications. For example, whether it is to uncover serious crime on the dark web, intrusions in a computer network, or hijacks at global internet scales, better network anomaly detection tools are desperately needed in cyber-security. Characterising the network structure of multiple EEG time series recorded at different locations in the brain is critical for understanding neurological disorders and therapeutics development. Modelling dynamic networks is of great interest in transport applications, such as for preventing accidents on highways and predicting the influence of bad weather on train networks. Systematically identifying, attributing, and preventing misinformation online requires realistic models of information flow in social networks. Whilst simple random networks theory is well-established in maths and computer science, the recent explosion of dynamic network data has exposed a large gap in our ability to process real-life networks. Classical network models have led to a body of beautiful mathematical theory, but do not always capture the rich structure and temporal dynamics seen in real data, nor are they geared to answer practitioners' typical questions, e.g. relating to forecasting, anomaly detection or data ethics issues. Our NeST programme will develop robust, principled, yet computationally feasible ways of modelling dynamically changing networks and the statistical processes on them. Some aspects of these problems, such as quantifying the influence of policy interventions on the spread of misinformation or disease, require advances in probability theory. Dynamic network data are also notoriously difficult to analyse. At a computational level, the datasets are often very large and/or only available "on the stream". At a statistical level, they often come with important collection biases and missing data. Often, even understanding the data and how they may relate to the analysis goal can be challenging. Therefore, to tackle these research questions in a systematic way we need to bring probabilists, statisticians and application domain experts together. NeST's six-year programme will see probabilists and statisticians with theoretical, computational, machine learning and data science expertise, collaborate across six world-class institutes to conduct leading and impactful research. In different overlapping groups, we will tackle questions such as: How do we model data to capture the complex features and dynamics we observe in practice? How should we conduct exploratory data analysis or, to quote a famous statistician, "Looking at the data to see what it seems to say" (Tukey, 1977)? How can we forecast network data, or detect anomalies, changes, trends? To ground techniques in practice, our research will be informed and driven by challenges in many key scientific disciplines through frequent interaction with industrial & government partners in energy, cyber-security, the environment, finance, logistics, statistics, telecoms, transport, and biology. A valuable output of work will be high-quality, curated, dynamic network datasets from a broad range of application domains, which we will make publicly available in a repository for benchmarking, testing & reproducibility (responsible innovation), partly as a vehicle to foster new collaborations. We also have a strategy to disseminate knowledge through a diverse range of scientific publication routes, high-quality free software (e.g. R packages, Python notebooks accompanying data releases), conferences, patents and outreach activities. NeST will also carefully nurture and develop the next generation of highly-trained and research-active people in our area, which will contribute strongly to satisfying the high demand for such people in industry, government and academia.

This application seeks continued financial support from the ESRC for LIS, a cross-national data archive and research institute. LIS is a data infrastructure of income and wealth data whose primary purpose is to enable cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Whilst LIS is physically located in Luxembourg, users of the LIS microdata come from about 100 countries including the UK. The work of acquiring and harmonising diverse datasets from multiple countries is labour intensive; by centralising this task, LIS saves time for researchers carrying out comparative analyses, avoiding the repetition of these tasks every time a scholar starts a project; in addition, thanks to its expertise over many years, LIS can ensure users the best comparability of the data. In order to avoid having to charge individual user fees, LIS is seeking financial support to be able to continue providing researchers with access to high-quality data. This application seeks support from ESRC to help cover LIS' basic operating costs, which primarily consists of staff salaries and computer equipment. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. Since its founding, LIS datasets have been used by 8,000 researchers from around the world to analyse economic and social policies and their effects on outcomes including poverty, income inequality, employment status, wage patterns, gender inequality, family formation, child-wellbeing, health status, immigration, political behaviour and public opinion. The newer LWS datasets enable research on wealth portfolios, asset levels, and the interplay between household income and wealth. According to the Publish and Perish software that retrieves and analyses academic citations, the Hirsch's h-index is above 160 for LIS and 55 for LWS. LIS is a unique resource not only with respect to the breath of its data offering (it is the only data archive in existence that includes income, wealth and labour market microdata, over time and in one place from such diverse geographic regions and at such varied income levels), but also because it is the only archive providing access to confidential microdata through a secure remote execution system, that allows thousands of registered users all over the world to receive the logs of their statistical queries in real time (an average of about 70,000 requests are processed every year). LIS has also long operated as a venue for researchers and practitioners to exchange research ideas, results, and methods. These exchanges take place through the widely accessed Working Paper Series (now including 840 papers), the Visiting Scholar program, pre- and postdoctoral postings, annual workshops and conferences. The participating countries are high-income and middle-income countries. LIS will continue to grow to include many more middle-income countries' datasets, enabling greater comparative research opportunities. Additionally, it is now seeking to expand its data offerings in terms of increased frequency of data availability, and improved tools for data access and meta data. The UK has always had an important role in LIS since its very inception in the 1980s, when British economist Tony Atkinson gave a fundamental contribution to its construction and development (he later become the president of its Board). Individuals and organisations in the UK have been actively engaged with LIS for over three decades, providing data, contributing financing and serving as board members. Researchers in the UK have queried the microdata; produced publications, government reports and working papers using the LIS data; attended summer workshops; participated in the Visiting Scholar program; contributed to research conferences and conference volumes; and provided invaluable intellectual guidance and direction regarding LIS' activit

Peatlands store more carbon than any other terrestrial ecosystem, both in the UK and globally. As a result of human disturbance they are rapidly losing this carbon to the atmosphere, contributing significantly to global greenhouse gas emissions and climate change. We propose to turn this problem into a solution, by re-establishing and augmenting the unique natural capacity of peatlands to remove CO2 from the atmosphere and to store it securely for millennia. We will do this by working with natural processes to recreate, and where possible enhance, the environmental conditions that lead to peat formation, in both lowland and upland Britain. At the same time, we will optimise conditions to avoid emissions of methane and nitrous oxide that could offset the benefits of CO2 removal; develop innovative cropping and management systems to augment rates of CO2 uptake; evaluate whether we can further increase peat carbon accumulation through the formation and addition of biomass and biochar; and develop new economic models to support greenhouse gas removal by peatlands as part of profitable and sustainable farming and land management systems. Implementation of these new approaches to the 2.3 million hectares of degraded upland and lowland peat in the UK has the potential to remove significant quantities of greenhouse gases from the atmosphere, to secure carbon securely and permanently within a productive, biodiverse and self-sustaining ecosystem, and thereby to help the UK to achieve its ambition of having net zero greenhouse gas emissions by 2050.

In a climate of diminishing budgets, falling police officer numbers and a growing number of calls related to "public safety and welfare" (College of Policing, 2015) senior police officers have highlighted the need to manage crime and anti-social behaviour (ASB) differently (Thornton, 2015; Habgood, 2015). Research conducted by HouseMark suggests that the cost of tackling ASB to UK social landlords alone was approximately £295 million in 2012/13 (Wickenden, 2014). With this in mind, the primary research focus is to establish: Who experiences or witnesses ASB and in what context? A number of police forces have received criticism for their lack of understanding in relation to the intensity of harm to communities and vulnerable individuals caused by ASB (HMIC, 2010). The proposed study will address this gap in knowledge by providing a more comprehensive understanding of ASB victims, harm and vulnerability. It will draw on data from four sources: Understanding Society (2009/10-2014/15), the Crime Survey for England and Wales (2009/10-2014/15), the 2011 UK Census and the English Indices of Deprivation. Collectively, this will build a comprehensive picture of the individuals, households and areas most likely to experience: high prevalence of ASB; a strong link between ASB and crime victimisation; severe impact of ASB victimisation on quality of life and daily routine; and high levels of dissatisfaction with police response to ASB. The proposed research will constitute the most comprehensive study of the relationship between victim and neighbourhood characteristics to date, including deprivation, community cohesion and trust. The research has real potential to inform policy and practice, including resource allocation (e.g. patrolling strategies), planning policy, victim assistance, the design of the built environment and wider interventions to address ASB. The research will involve working collaboratively with a stakeholders including: the Office for National Statistics, Nottingham Crime and Drugs Partnership and two major regional partnerships: the East Midlands Policing Academic Collaboration and East Midlands Collaborative HR Services. The five East Midlands police forces employ over 14,000 police officers and staff, covering a population of over 4.5 million. These relationships will ensure the findings are directly implemented into regional learning and practice. Findings will be publicised via: four peer-reviewed journal articles; a policy paper co-authored with a practitioner; online content (blog, Twitter); press releases; three Advisory Committee meetings; a policy roundtable; an end of project conference; and three conference presentations. Collectively, this activity will ensure the research is accessible and disseminated widely. Criminology is currently undersupplied with trained secondary data analysts despite the availability of a wealth of existing large and complex datasets which, if examined, would offer invaluable theoretical insights and assist policymakers. An important outcome from the research will be to increase that capacity within criminology via training the Research Staff to undertake advanced secondary data analyses as well as enhancing the experience of the Early Career Researcher (PI) in managing large research projects. The ability to provide a more effective response to ASB is particularly important at a time when budgets are being drastically reduced. The research team comprising of senior, middle and early career researchers, are collectively experts in data linking, have published extensively using secondary data and have a track record of delivering impactful research. The proposed research will enable the team to conduct internationally-leading research, develop the capacity of an early career researcher (PI), work collaboratively with non-academic stakeholders to extract maximum value from existing data resources and produce research with high societal and economic impact.

Traditional methods for producing economics statistics, for instance GDP, rely on data gathered through surveys of a population. Whilst such methods are accurate, and well calibrated, they are very expensive to run, and take a long time to feed-back information. As such, National Statistics Institutes such as the UK's Office for National Statistics (ONS) are looking to integrate so-called administrative data, and alternative data-streams such as web-scrapped data into their estimation of economic statistics. Using such data can potentially increase both the frequency and the accuracy at which economic statistics are produced. However, it is often unclear how these alternative data-sources (of which there can be many) relate to the traditional survey results, and how we can produce high-frequency series which are consistent with the survey data. Given that we could measure many different aspects of the population, only a few of these might actually be relevant to producing a particular statistic of interest. From a methodological viewpoint, this mandates that we choose between several competing statistical models, a problem known as model selection. Traditional model selection methods assume that the number of data-points is much larger than the number of data-streams, however, when linking administrative, and alternative data-sources, that assumption will no longer hold and one has to consider the so-called high-dimensional statistical setting. This project proposes to adapt recent advances in high-dimensional methodology to the analysis and production of bench-marked economic statistics. The project aims to examine both the empirical behaviour of these methods via simulation, and work with practitioners at the ONS to implement and test these methods through the development of a easy to use software package.