NeEDS responds to the massive scientific and technological challenges that the very rapidly growing field of Data Science has created for users and producers of data in Europe and world-wide. The challenges stem from the complexity of the data, the completely novel questions posed to data scientists, as well as the need of non-experts to visualize and interact with the knowledge extracted from data in order to aid data-driven decision-making. Companies and public sector bodies around Europe find they cannot build up the required capabilities quickly enough, and Europe is remarkably behind US academia in increasing Data Science capacity. NeEDS provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics. NeEDS consists of four academic beneficiaries, eight industrial beneficiaries (from industry sectors ranging from energy, retailing, insurance to banking, as well as national statistical offices), two academic partners and one industrial partner from five EU countries, USA and Latin America with strong and complementary expertise. With this composition, NeEDS is in a unique position to deliver cutting-edge multidisciplinary research to advance academic thinking on Data Science in Europe, and to improve the Data Science capabilities of industry and the public sector.

The NEO-DEM project will use non-standard data and novel methods to impact business efficiency, encourage community collaboration and provide scholarly insights into consumer behaviour. UK businesses can struggle in the developing world, despite excellent track records at home. The following reasons explain a good deal of this failure in retail, service and consumer oriented sectors: * It is not possible to directly transfer domestic business models into emerging economies due to cultural, infrastructural and behavioural differences. Companies need to generate new analytical and strategic models that identify the differing needs of customers based on an understanding of the novel behavioural and consumption patterns exhibited. * In the developed world consumer oriented businesses are increasingly data-driven. They rely on cross-referenced geo-demographic, socio-graphic, and psychographic data as well as transactional data (e.g. Tesco & Boots in the UK); their use is enmeshed within company strategy. In many countries this kind of data are incomplete or non-existent, their absence inhibits growth and means that targeting and resource use is sub-optimal. Replicating the kind of data that is readily available in the UK will often be impossible or expensive and impractical. Even when transactional data is forthcoming (e.g. Tesco Clubcard Malaysia) there is limited scope to cross-reference them with reliable geo-demographic data-sets and models that are taken for granted in the UK (e.g. Experian's Mosaic). Despite lagging behind in infrastructural developments, developing countries have experienced digital revolutions; providing a largely untapped opportunity to generate business intelligence. In 2010 of the 5 billion mobile phones in the world 80% were in developing countries and this proportion is continues to grow. African countries have embraced new financial technologies such as mobile payment: over 17m Kenyans use mobile money; around 25% of the country's GNP flows in this way. Crowd sourcing systems such as Ushahidi lead the way in the aggregation of social factors. The project will create a decision support and market segmentation platform generated via personal data, collaborative aggregation and crowd-sourced feedback, that will allow the generation new models of consumer behaviour to support innovation. Our work will hinge on three case studies in exemplar developing economies (Tanzania, Malaysia and China) where we will develop example behavioural segmentations via novel computational and clustering methods and in partnership with a range of data providers and internationally significant companies including: Alliance Boots, Dairy Farm International, Bakhresa Group, Boots, E-fulusi, Tesco, Marks & Spencer and Experian. Academic research into consumer behaviour patterns will be significantly advanced by the techniques developed, their application in this field is novel. There is scope to exploit advanced forms of computation and clustering that more readily account for market complexities. There is a very high chance that the project will provide insights into consumer behaviour that have hitherto remained obscure. So the contribution to research in this area could be both methodological and empirical and contextual (robust insights into developing world consumers are more rare). This expeditionary collaboration is likely to open the door to and on-going conversation between the fields of business/consumer analytics and computational analysis.

This programme brings together teams from Herriot Watt University, the University of Cambridge, the University of Westminster and Durham University: providing a multidisciplinary focus on the research needed to enable and underpin radical measures to decarbonise the UK's road freight transport sector. The researchers are augmented by a consortium of 22 industrial partners, drawn from users, suppliers and participants in the road logistics sector. These industrial members provide advice and guidance as well as a rapid route to prototyping and implementation of solutions. The first 5-year programme, conducted by the same team, laid the foundations and showed that radical measures are necessary to hit the UK Government's CO2 reduction targets. It also showed that integration between logistics solutions, vehicle technology, and policy measures is essential. This experience has shaped the design of the proposed programme. The new research programme will run for 5 years and has three themes: (i) data collection and management, (ii) logistics systems, and (iii) vehicle technology. A portfolio of 23 projects spans the themes. The first strand of projects (funded mainly by EPSRC), will focus on reducing barriers to promising strategic, deep decarbonisation technologies and solutions. These projects will create and integrate new data, new modelling tools and decision support systems, to create new insights about technological and logistical solutions, compelling arguments for their early adoption and recommendations for the necessary policy measures. Driven by a desire to model and then quantify the benefits of radical logistics options, the models will be developed and validated with data from real freight operations by the industrial partners, collected by novel automated means. Alternative vehicle fuels and power trains and ways of significantly reducing energy consumption will be investigated. The second strand of projects (funded mainly by EPSRC and industry) will focus on extending and optimising the capabilities of promising technologies and on increasing their impact when applied to decarbonisation of road freight. Applied research into the dynamics of logistics mode decisions and testing of novel logistics options such as horizontal collaboration, co-loading and reorganisation of logistics infrastructure, will be enabled by tools developed in the first strand. Technologies developed in the first 5 years of the Centre for Sustainable Road Freight (SRF) will be tested in two separate full-scale field trials with consortium partners, funded by InnovateUK. Road-mapping will provide a mechanism for corporates, government departments and researchers to build a common view of the future. The projects in the third strand (funded by Energy Technologies Institute) will focus on implementation of tools and practices that offer immediate impact. These include novel and powerful software systems for industry to use in data collection and for vehicle characterisation and fleet decarbonisation. Research into the drivers of strategy and policy will, likewise identify the most powerful ways to influence adoption of technologies and logistics solutions. The Road Freight Systems Living Laboratory ('Living Lab') is the central integrating element of the SRF's five-year research programme. Almost every project in the Centre will be part of it. The Living Lab will provide a test bed to measure and model freight operations; to develop technical and logistical interventions based on real-time logistics data; to test the interventions in simulation; to develop decision support tools (several based on work done in the first 5 years of the SRF) and eventually to implement and trial the tools and systems in practice. The Living Lab will be based on an integrated software and data platform that is currently being built by the research team and industry partners.

Lancaster University (LU) proposes a Centre for Doctoral Training (CDT) to develop international research leaders in statistics and operational research (STOR) through a programme in which cutting-edge industrial challenge is the catalyst for methodological advance. Our proposal addresses the priority area 'Statistics for the 21st Century' through research training in cutting-edge modelling and inference for large, complex and novel data structures. It crucially recognises that many contemporary challenges in statistics, including those arising from industry, also engage with constraint, optimisation and decision. The proposal brings together LU's academic strength in STOR (>50FTE) with a distinguished array of highly committed industrial and international academic partners. Our shared vision is a CDT that produces graduates capable of the highest quality research with impact and equipped with an array of leadership and other skills needed for rapid career progression in academia or industry. The proposal builds on the strengths of an existing EPSRC-funded CDT that has helped change the culture in doctoral training in STOR through an unprecedented level of engagement with industry. The proposal takes the scale and scientific ambition of the Centre to a new level by: * Recruiting and training 70 students, across 5 cohorts, within a programme drawing on industrial challenge as the catalyst for research of the highest quality; * Ensuring all students undertake research in partnership with industry: 80% will work on doctoral projects jointly supervised and co-funded by industry; all others will undertake industrial research internships; * Promoting a culture of reproducible research under the mentorship and guidance of a dedicated Research Software Engineer (industry funded); * Developing cross-cohort research-clusters to support collaboration on ambitious challenges related to major research programmes; * Enabling students to participate in flagship research activities at LU and our international academic partners. The substantial growth in data-driven business and industrial decision-making in recent years has signalled a step change in the demand for doctoral-level STOR expertise and has opened the skills gap further. The current CDT has shown that a cohort-based, industrially engaged programme attracts a diverse range of the very ablest mathematically trained students. Without STOR-i, many of these students would not have considered doctoral study in STOR. We believe that the new CDT will continue to play a pivotal role in meeting the skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality as much as there is one of quantity. Our goal is to develop research leaders who can innovate responsibly and secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others with different skills-sets and communicate effectively. An integral component of this is our championing of ED&I. Our external partners are strongly motivated to join us in achieving these outcomes through STOR-i's cohort-based programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industrial and academic. Industry will play a key role in the CDT. Our partners are helping to co-design the programme and will (i) co-fund and co-supervise doctoral projects, (ii) lead a programme of industrial problem-solving days and (iii) play a major role in leadership development and a range of bespoke training. The CDT benefits from the substantial support of 10 new partners (including Morgan Stanley, ONS Data Science Campus, Rolls Royce, Royal Mail, Tesco) and continued support from 5 existing partners (including ATASS, BT, NAG, Shell), with many others expected to contribute.