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New Data: The e-food deserts index

New Data: The e-food deserts index

The e-food deserts index (EFDI) is a multi-dimensional composite index for GB which measures the extent to which neighbourhoods exhibit characteristics associated with food deserts across four key drivers of groceries accessibility:

· Proximity and density of grocery retail facilities

· Transport and accessibility

· Neighbourhood socio-economic and demographic characteristics

· E-commerce availability and propensity

It draws on a long interest in food security among Geographers and policy makers, most prominent in the late 1990s and early 2000s ‘urban food deserts’ debate. At the time it was argued that urban food deserts had been ‘abandoned’ by the major grocers, resulting in poor access to the larger format grocery stores which provided fresh, healthy and affordable food. Many of these neighbourhoods were some of the most deprived in England and Wales and were located within inner city areas where residents faced considerable financial and practical (e.g. access to transport) barriers to accessing food store provision.

The EFDI incorporates new indicators of online groceries (home delivery) provision and propensity to engage with online groceries, the latter drawn from an existing CDRC data resource, the 2018 Internet User Classification. In addition to urban deprivation, it highlights a new driver of inequalities in access to groceries, termed ‘e-food deserts’ – remote and rural neighbourhoods which suffer the dual disadvantage of comparatively poor access to physical retail opportunities alongside limited provision of online groceries.

The EFDI is constructed at a neighbourhood level using Lower Super Output Areas (LSOAs) in England and Wales and Data Zones (DZs) in Scotland. Input data are drawn from a range of sources including the Census and existing indicators of deprivation and accessibility at the neighbourhood level. It also incorporates a number of custom-derived indicators of food store accessibility, consumer behaviours and availability of groceries e-commerce drawn from our own modelling.

The index can be viewed on CDRC Maps (England and WalesScotland) and is available to download for researchers and policy makers to attach to their own data. The research team would be very keen to understand how the index has been used in subsequent applications.

Screenshot of Newcastle showing the EFDI at Lower Super Output Areas
Newcastle upon Tyne – users can search the EFDI by location on CDRC Maps

The research team have used the index as input to wider ongoing work into geographical inequalities in e-groceries provision as part of the first GB wide assessment of the geography of online groceries provision. Whilst they found that online groceries coverage is generally excellent at the household level, they note considerable inequalities in online groceries provision between urban and rural areas. Whilst online groceries could afford considerable potential to improve retail access in rural areas, inequalities in provision are currently driving new notions of contemporary food deserts.

Andy Newing, Associate Professor in Applied Spatial Analysis at the University of Leeds, who led this study, said:

“These inequalities in online groceries delivery availability are driven by the challenges of providing services within out most remote and rural areas, alongside the predominantly urban and suburban nature of investment in e-groceries by retailers.”

Recounting Crime – helping to improve the accuracy of crime estimates

The back of two police officers wearing high vis police vests

Recounting Crime – helping to improve the accuracy of crime estimates

Recounting crime is a research project funded by the ESRC Secondary Data Analysis Initiative. The project team are using data from the CDRC to explore new statistical methods to help improve the accuracy and precision of crime estimates.

About the Project

Getting an accurate picture of the true extent of crime is a central task of police forces, the Home Office, and Office for National Statistics. Annual counts of crime are used to determine the costs of crime, which is in turn used to allocate resources to the police and public services, performance manage the police, and evaluate crime reduction initiatives.

The regular publication of crime figures is also a key determinant of public confidence which in turn facilitates greater reporting of crime, whilst also being used by governments to justify – and retract – major policy initiatives. Recent reports about the rise of knife crime, and the large uptick in hate crime post-Brexit both relied on close reference to crime statistics, and the proposed extension of stop and search powers across police forces in England and Wales has also been justified with reference to these apparent increases in crime.

The problems with recorded crime figures are well known, including inconsistencies in recording practices in different forces, incentives to ‘no-crime’ incidents that are unlikely to be solved, and differences in the willingness of the public to report certain crimes (which is itself dependent on the relationship between the police and the public). So severe are these problems that police recorded crime figures recently lost their official statistics designation (following a substantial review in 2014). 

In this project the team will make use of recent developments in the study of measurement error and small area estimation to better understand the nature of the gaps in coverage of the two sources of crime data, explore the implications of relying on crime estimates prone to measurement error, develop adjustment methods and estimate new ‘corrected’ crime counts at the local area level. 

Find out more

Visit the Recounting Crime website.

Follow Recounting Crime on Twitter.

Watch the Recounting Crime project team discuss the impact of measurement error in police crime records at the UK Data Service – Crime Surveys User Conference 2020 on 8 December.

DUG Conference: Data Analysis in a Crisis, plus CDRC Masters Dissertation Scheme

DUG Logo

DUG Conference: Data Analysis in a Crisis, plus CDRC Masters Dissertation Scheme

On Tuesday 10th November, the retail industry DUG (Data Analysts User Group) hosted its annual conference on the theme of Data Analysis in a Crisis. Consonant with this theme, the usual industry-led event could not take place at the usual Royal Society venue this year, but nonetheless attracted an audience of 70 participants online using WebEx.

Full details of the programme and activities are available on the DUG website, including videos of the presentations. DUG Director Tim Drye opened the proceedings with an overview of the science and art underpinning the Data Analyst role, illustrating that foundations from each are essential to understanding data and presenting them to an audience in an intelligible manner.

Mark Stern, Eoin Gleeson and Fraser Gray from Ladbrokes Coral then addressed the organisational setting to high performance data analytics through effective team-building, drawing upon their many varied experiences.

Prof. Paul Longley then introduced the CDRC Masters Dissertation Scheme, noting upcoming launch of the 2021 scheme and the opportunities that it offers for career-enhancing interactions between business, academia and student-centred problem-solving. (The website has more information and can be used to make enquiries or submit projects.) Four selected students who took part in the 2020 Masters Dissertation Scheme then presented their collaborative work:

– Lucy (Ludmila) Sabelnikova, City University worked with Movement Strategies, in an evaluation of the ways in which footfall and mobile network data can be used to predict consumer behaviour at events – view Lucy’s presentation and project overview

– Samuel Li, UCL also worked with Movement Strategies, on an assessment of the Impact of weather upon shipping movements, as evidenced using AIS data and weather APIs – view Samuel’s presentation and project overview

– Nombuyiselo Murage, University of Liverpool worked with Tamoco UK Ltd., to derive spatio-temporal geographies of activity patterns from mobile GPS data – view Nombuyiselo’s presentation and project overview

– Taeyang Jung, Imperial College worked with the Phoenix Partnership to identify and evaluate barriers to use of electronic health records in applied settings – view Taeyang’s presentation and project overview

All of this year’s Masters Dissertations were submitted to the annual national CDRC competition, judged this year by Sarah Hitchcock (Geolytix) and Martin Squires (Pets at Home and UCL Visiting Industrial Professor). This year’s winner of the £500 cash prize was awarded to Lucy (Ludmila) Sabelnikova, and the two runner-up prizes were awarded to Nombuyiselo Murage and Samuel Li. Nombuyiselo also won the Presentation Prize for her contribution to the DUG conference, with honourable mentions also going to Samuel and Ludmila.

Congratulations to all the prize winners, and thank you to Lucy, Sam, Nombuyiselo and Taeyang for the excellent presentations., that will be made available as part of the conference proceedings.

The presentations were followed by a presentation from Dr Andrew Larner that took stock of how local councils are adapting to the Coronavirus, bringing together a range of experiences from across the globe. The contribution of the National Statistician, Professor Sir Ian Diamond was unfortunately cancelled because of technical issues.

Gary Cole highlighted the benefits of DUG membership and outlined how DUG is now moving forward, and Tim Drye wrapped things up sharing his reflections from the meeting.

It was a great opportunity to hear from industry, and see how the CDRC Masters Dissertation Students completed their projects over the summer. If you are interested in submitting projects for next years Scheme, please have a look at our website. If you have any questions, please email projects@cdrc.ac.uk .

Written by Nick Bearman, Project Delivery Manager.

CDRC Data Scientist Interns

CDRC welcomes new Data Science Interns

Last month we welcomed five members of the LIDA Data Scientist Internship Programme to the Centre. Over the next six months George Breckenridge, Stuart Ross, Seb Heslin-Rees, Rosie Martin and Simon Leech will be working with CDRC researchers on the following projects:

Analysing COVID-19 Mobility Responses through Passively Collected App Data – George Breckenridge and Stuart Ross

‘Lockdown’ policies restricting mobility have caused mass disruption to the normal operation of daily activity in cities across the COVID-19 pandemic. They mark the first time in recent memory that national and global populations have been caused to simultaneously re-evaluate transport choices, whilst also causing wholesale changes in the location and spatial footprint of most social and economic activity. The understanding of such dislocated geographies will underpin urban and transport planning policies for maintaining low virus transmission risk and for revitalising the UK economy, far into 2021 and beyond.

The emergence of passively collected anonymous mobility datasets, produced though mobile phone apps in cases where user permission is granted, makes it possible to explore these transportation responses at fine spatial and temporal scales – before, during and after the COVID-19 lockdown(s). The aggregation of these contemporary UK patterns – which will be required to maintain user anonymity – allows for the exploration of hundreds of thousands of users whilst simultaneously protecting privacy. Indeed, the utility of privacy-enhanced outputs for policy will be a lead project focus. Phone data will be provided by partner Cuebiq through their secure online platform, which enables only the export of aggregated outputs to suitable spatial units.

In order to investigate such unprecedented changes in mobility using Cuebiq’s data, we expect to employ a variety of machine learning (ML) methods to extract features. A journal paper documenting these patterns as the COVID-19 crisis evolves is the anticipated output for this CDRC-funded project

New insights into workplace and retail dynamics for English and Welsh cities – Seb Heslin-Rees

This project will be using Whythawk data on commercial properties in England and Wales, at Lower Super Output Area. It will make use of existing methodologies applied to a different scenario to produce new insight on commercial property rent and spatial location.

A commercial geodemographic classification of workplace zones

This research endeavour will utilise the newly available Whythawk dataset to construct a model for presenting and thus, understanding the spatial distributions of workers and workplaces across England and Wales. Largely, this will involve clustering workplaces of similar characteristics to distil a set of key workplace types, which can subsequently be mapped and analysed. In addition, the dataset has made available details of workplaces that have not been present in previous workplace datasets, such as distinguishing different workplace functions within multi-level building complexes. Consequently, this could provide additional insights and novel avenues for academic research and policy initiatives.

Predicting commercial rents using novel Machine Learning approaches

Using novel big data, this study will assess mass market appraisal within the English and Welsh commercial rental market. Mass market appraisal is the valuation of properties at a given time, and is required to ensure each property makes the appropriate tax contribution. This study will use a large volume of data on commercial business type, rental and rateable values and numerous external environmental variables. A range of machine learning algorithms will be used to predict and appraise the commercial rental market in England and Wales.

The outputs will include academic papers focused on methodologies employed, CDRC datasets and detailed maps. These projects are expected to help property professionals better understand commercial rental pricing and businesses who use and occupy these spaces and also researchers who are interested in how property values interact with other aspects of the environment.

Isolation and Inclusion in a post-social distancing COVID world – Rosie Martin 

The disparate impacts of COVID-19 and the associated lockdown have been much discussed recently, particularly in terms of age, deprivation, or employment sector. As the UK and other countries emerge from quarantine, it is equally apparent that the after-effects are likely to be long-lasting, whether through continued mitigation efforts such as social distancing or the economic impacts of economic shutdown, and that these after-effects are likely to further unevenly impact some groups over others. There are many dashboards reporting information on COVID cases and deaths, but information on the impacts on general population and businesses is missing.

Our main objective is to advance understanding of the social and spatial impacts of emergence from lockdown, identifying those households and places at risk of further isolation, under a scenario of continued social distancing, high unemployment, and a potential contraction of local service provision, including public transport. We have three research questions:

1. Are some typical household structures more vulnerable than others as a result of social distancing (and what are they vulnerable to, e.g., unemployment, social isolation, decreased service provision versus decreased access to existing services, decreased mobility, etc.)?

2. Is there a critical intersection of mobility, employment status and social distancing rules that predispose households with particular structures to isolation?

3. Based on current neighbourhood patterns and planning, what is the geography of isolation vulnerability?

It is expected that each of these scenarios have a particular geography. The creation of this dashboard should help predict where geographies of isolation under intersecting scenarios occur. Identifying areas at risk of isolation and exclusion through this project could prove invaluable to local councils who will be working to ensure all individuals are given access to relevant levels of assistance and resources during COVID-19 recovery, rather than allowing pre-existing disparities to widen.

CDRC Masters Dissertation Scheme at Registry Trust: skills, experience and employability

CDRC Masters Dissertation Scheme at Registry Trust: skills, experience and employability

Millie Corless completed her masters dissertation through the CDRC Masters Dissertation Scheme (MDS) with the Registry Trust. I spoke with her after she completed her degree to find out what it was like. The scheme really appealed to her, and it was one of the parts of the MSc Geospatial Data Science (GDS) degree at University of Liverpool that convinced her to apply for that Masters programme. Our industry collaborations allow us to provide projects that have real world impact, giving students the experience of working with a real world dataset, and feeding into the industry partners work.

The Registry Trust is a small company (~ 30 staff) and had one person in their data analytics team. Millie gave them the extra capacity to ask a MSc GDS student with skills and experience in GDS and coding to take one of their data sets which they hadn’t had much work done on it, and spend a significant amount of time analysing the data. The Registry Trust were fairly sure the data set had a good story within it, about the scale and patters of county court judgements for indebtedness but they didn’t have the time or expertise to dig into this and find out the details. Offering the project through the CDRC MDS allowed them to get someone with the skills and time to do this.

Millie Corless completed her masters dissertation through the CDRC Masters Dissertation Scheme (MDS) with the Registry Trust.

Throughout the scheme, the projects vary but they always have some degree of flexibility for the student to focus the project on their areas of interest. For example, Millie is very interested in health and she looked at the CCJ data and explored its’ relationship with health. She met with a number of different people to develop and refine the project proposal, including her industry supervisor (the current data analyst) and others from Registry Trust, including the CEO and Chair of the company. One of the benefits of working with a small company (30 staff) is that she was able to work closely with a range of staff members and they gave her some great insights into working in industry. She felt like she was working within a bigger team for her dissertation; there was a group of people she could go to for advice, including her academic supervisor, her industry supervisor, and others within and outside the Registry Trust.

This project also had a great real world impact; the analysis Millie completed has fed into a blog post by the Registry Trust, and future projects and their policy recommendations. The real world impact was one of the elements that Millie really liked about the Masters Dissertation Scheme, and the Registry Trust project in particular. The fact this project already existed was a great help for Millie – “it allowed me to concentrate on the project rather than needing to come up with a topic. I also knew I was interested in health, so the flexibility within the project allowed me to include that in my research questions and analysis which was great.”

After the scheme, the position of Data Analyst within in the Registry Trust became available, Millie applied and is now working full time for the Registry Trust. “I applied and was interviewed with a number of other candidates, but having taken part in the Masters Dissertation Scheme, I already knew the data sets they were working with and the type of analyses they were interested in which gave me an advantage.” Her role as Data Analyst is developing the in-house skill set that the Registry Trust can utilise and will feed into a number of projects and outputs over the coming year.

Her recommendation to anyone considering applying for the Masters Dissertation Scheme is to “go for it”. The scheme gave her great experience and looks good on her CV, and going forward into any career (in industry or academia or elsewhere) the Masters Dissertation Scheme shows you are interested in the application of the skills you have learnt and gives you experience of working with others in industry.

The Scheme will be open soon (November) for businesses proposing projects, and then available in the new year for masters students to apply. Please have a look at https://www.cdrc.ac.uk/education-and-training/masters-dissertation-scheme/ for more details.

More details on Millie’s research are available in the Registry Trust blog post and in her dissertation.

Written by Dr Nick Bearman, Data Services Manager.

CDRC Open Data Survey & Prize Draw

CDRC Open Data Survey & Prize Draw

The CDRC is currently conducting a review of past and ongoing applications of our data sets.

Users of our open data services are invited to participate in a short survey. Completing the survey will automatically enter you into a prize draw, with a chance of winning one of four Amazon gift vouchers:

1 x £200

1 x £100

2 x £50

We will contact the winning participants with details of how to claim their prize shortly after the survey closes on November 13th 2020.

We are gathering information to track the applications of our data services and to better develop our services with our users’ needs in mind. As an open and accessible data service provider, user feedback is crucial to improve the service CDRC provides and to maintain CDRC as a user-centric platform.

All of those users who have registered to access our open datasets should have received an email for the survey. If you have not, and would like to contribute, the survey is available online at https://liverpool.onlinesurveys.ac.uk/cdrc-open-data-survey .

Please contact james.brookes@liverpool.ac.uk or info@cdrc.ac.uk if you have questions about completing the survey.

Beginner’s Python Review

Beginner’s Python for Data Analysis – Review

Adam Keeley, Analyst at Leeds Institute for Data Analytics, shares his thoughts on our recent online Beginner’s Python for Data Analysis course.

The CDRC Beginner’s Python for Data Analysis training course was outstanding. I’m actually not a complete novice when it comes to Python, and I suspect a few others in the class also had varying levels of experience already. The course was structured such that it covered off the basics of object oriented language without dwelling on them. This helped fill in the gaps in my baseline understanding and ensured we were all quickly up to a similar standard.

The delivery of the content was well paced. When new concepts were introduced we were talked through some worked examples before being asked to work through a set of exercises on our own. Except we were never on our own; Fran and her demonstrators were always on hand to offer help and guidance in a friendly and constructive way. The course structure was logical and methodical with each new concept built on principles previously established and embedded through active application. There were no great conceptual leaps so at no point did I feel lost or wondering ‘how on earth did we get to that?’.

Inevitably when remotely delivering practical training of this nature, there were some technical issues. Impressive efforts were made to proactively sidestep these through the use of online, containerised environments. Every one of us began working from a standardised online environment, set up in advance with all of the required software and package dependencies available. Unfortunately the internet connections of the class were of varying reliability and some of us failed to connect consistently. The order of the course schedule was changed to get us set up on our own machines early but again, the remote delivery meant that every machine was different and the subsequent technical issues threatened to dominate the experience. I was extremely impressed with how well the delivery team handled the situation, providing technical support to all of us both as a group and individually when required. We were soon working with data again and probably with a greater understanding of Python as a result.

One benefit of the online delivery was the open chat enabled us to help each other out as we found solutions, not just troubleshooting the Python environment but in the exercises too. I found my fellow students friendly and keen to offer help when they were able. Coupled with the delivery teams knowledgeable and amiable nature this helped foster an environment where asking questions was easy.

By the end of the two day course many of us were thinking of ways to implement what we’d learnt in our workplace or include it in our research. Many of us were asking questions about the techniques and concepts we’d learnt, in terms of specific application to data and problems we face in the working world. These questions were answered helpfully and with a breadth of knowledge, with tips on where to learn more.

I would definitely recommend this course to anyone curious if Python might be useful to them, or for whom programming does not feel accessible. This course will let you in on the big secret: programming doesn’t have to be difficult!

A classification for English Primary Schools using open data

Two girls writing at school

A classification for English Primary Schools using open data


CDRC researchers, Dr Stephen Clark, Dr Nik Lomax and Professor Mark Birkin have developed a new classification for English primary schools to encourage better collaboration and enable more nuanced benchmarking.  

England has statutory regulations in place that ensure state funded schools deliver broadly the same curriculum. However there still exists a wide range of contexts in which this education takes place, including: the management of schools; how the schools chose to spend their budgets; individual policies in regards to staffing, behaviour and attendance, and perhaps most importantly, the composition of the pupil population in the school. Given these contexts, one outcome of interest is the attainment profile of schools, and it is important that this performance is judged in context, for the benefit of pupils, parents and schools.  

Developing a new classification  

To help provide this context CDRC researchers, Dr Stephen Clark, Dr Nik Lomax and Professor Mark Birkin have developed a new classification using contemporary data for English primary schools.  Thanks to the European Regional Science Association, they have made available a resource that identifies families of primary schools in England, where schools share common characteristics.  

The recently published study allocates schools into one of 32 sub-groups, allowing schools to compare their performance, either academically or financially with similar schools. These groupings allow the identification of “families of schools”, to act as a resource to foster better collaboration between schools and enable more nuanced benchmarking. 

Users are able to search by location to view schools in their area.

A novel approach to classifying schools  

Dr Stephen Clark explains “We identified the two most important aspects as firstly, the ethnic composition of the school, either from the background of the pupils or the number of pupils where English is not a first language.  

The second being the level of affluence, measured by neighbourhood characteristics or the number of pupils eligible for free school meals. Other important aspects include the degree of oversubscription for popular schools, and the number of authorised or unauthorised absences. 

In this study the academic performance of the school is not ’baked into’ the classification, so that differences in schools performance can exist, and be identified and investigated or shared.”  


Looking at the Midland’s City of Derby, we see that for a school like Reigate Park Primary they would be better to look to schools further afield, such as St Mary’s Catholic or Ashgate schools for appropriate peers to share experiences with, rather than the closer by Brackensdale school. 

Map of Derby highlighting that Reigate Park Primary would be better to look to schools further afield than their immediate neighbour Brackensdale Primary
Map of Derby – Background Map © OpenStreetMap contributors


Using open data  

The data for the study comes from the Department for Education in England, collected each year during the Spring term’s Annual Census of Pupils and Schools.   

This regularity of data allows the groupings to be revised over time, which is useful as the circumstances of a school are not fixed over time.  We would expect to see some changes to the groupings as schools are transformed through new leadership or by changes to catchment areas and their population.   

View the data

Find out more  

Secure Labs Reopening and Remote Data Service

Secure Labs Reopening and Remote Data Service

It has been a very interesting few months, with many of our working practices changing, with both positive and negative changes. I am very happy to announce that our secure labs in London and Liverpool are now re-open, with Covid safe rules to allow users safe access to the labs. We will be in touch with lab users, do please contact us if you have any questions.  

As one of the Economic and Social Research Council’s data infrastructure investments, CDRC was asked to join a recent meeting discussing how we have been able to respond to COVID-19, both in terms of what our research has been used for, and how we have pivoted to provide more services online. We have had to adapt to and change how we work, often on a relatively short timescale, but hopefully for a better experience overall.  

New Remote Secure Data Facilities 

One of our main developments which is being rolled out is secure, remote access to some of our Secure data sets. Making data available through UCL’s Data Safe Haven allows us to provide access to some of our secure data sets which were previously only available within our secure labs, requiring a physical visit to London or Liverpool. We have had to renegotiate our data licensing agreements with our data partners to enable this, so currently only some secure datasets are available using this method.  

Data Safe Haven is an ISO 27001 accredited facility, with 2 factor authentication ensuring that only those who are allowed to access the data can. We have also implemented our standard secure data output checking, ensuring that any outputs from the lab are secure and non-disclosing.  

Remote Working 

All of our staff are now working from home which has required us to update our working practices. Both UCL and University of Liverpool have now adopted Microsoft Teams, and working within the Teams framework has allowed us to simplify and rationalised our collaboration, scheduling and document management processes. We must always remember the variety of people’s opinions, with some of our staff very keen on home working, and some very keen to get back to the office as soon as possible. With many of our staff in London, space at home is often at a premium, particularly for a full time home office, which many of us never envisaged before. 

One change this has precipitated is a move to electronic signatures for signing user agreements. Spearheaded by UCL Legal, we are now able to accept electronic signatures (using Adobe’s DocuSign process) on our user agreements, removing the need to print, physically sign and scan documents.  

The last six months have brought new ways of working, and new approaches to all of our lives – who would have thought that everyone wearing face coverings would become accepted in everyday life? We will continue to keep you up to date with developments with our Secure Labs and new remote secure data technologies.  

Remote Training 

We have also been able to move all of our training provision online, with a number of courses run through Zoom recently to enable online delivery. We are also in the process of developing two new courses (Advanced GIS Methods Training: AHAH and Multi-Dimensional Indices and Advanced GIS Training Methods: IUC and K-means Clustering) which will be delivered online in the autumn. Check out the links for more details. Whilst online training is not the same as in person training, it does have the advantages of not requiring travel, and overnight stays, which is a bit positive to many people.  

We will continue to provide updates to how our service changes and develops. If you would like to use our data, or if you have any questions, please do get in touch via at nick.bearman@ucl.ac.uk or info@cdrc.ac.uk.  

Join the CDRC team: Research and Impact Manager

Join the CDRC team: Research and Impact Manager

Are you an experienced and capable research manager who has an interest in working in the field of data analytics?

We’re looking for a talented and highly motivated Research and Impact Manager who can help us deliver impact from our data assets and affiliated projects. We are looking for someone who can oversee a portfolio of projects and deliver innovative support for both new and ongoing research.

Working with our team of academics and researchers you will help us to deliver excellent research which utilises our unique data assets. You will be involved in the complete lifecycle of the project, from inception and planning, through to delivery and dissemination of outputs. As such you will have the opportunity to be involved in the delivery of substantial projects which achieve wide reaching impact and ultimately raise the profile of the CDRC.

As Research and Impact Manager you will oversee the delivery of CDRC research projects and maximize their wider impact. You will be responsible for developing a project initiation process that ensures research delivers appropriate outcomes and impact, and for defining a framework for collating data and narratives on research impact. With these frameworks in place, you will track the progression of research projects, supporting academics and researchers in delivering objectives, and promoting collaboration with stakeholders.

Working with the wider CDRC team, you will help coordinate the delivery of diverse, high-impact research outputs, from journal papers and conference publications, to blog posts and tweets.

You will also play an important role in advising the Centre Manager and Directors on financial matters, producing forecasts and plans for expenditure throughout the grant.

Further information and Candidate Brief