IRC GtR+ Secondary Data Analysis Funding Call – The Winning Projects

This project will provide causal evidence of the value of public transport infrastructure for research productivity. Modern science and innovation projects are inherently collaborative. Despite progress in digital means of communication, face-to-face interaction remains crucial. UKRI regularly funds research consortia involving actors from different regions in the UK. The project will investigate whether rail connectivity impacts the outputs of such research projects. Project participants often anticipate, and indeed budget for, significant travel for collaborative in-person meetings. To estimate the impact of connectivity, we exploit random variation caused by significant train delays. Given the size and geography of the UK, trains are often the preferred means of travel for researchers. However, in recent years, many rail routes have been marred by delays, potentially hindering collaboration. The project will utilise GtR+ data to measure collaboration and project outcomes and combine this with Office of Rail and Road data. We will test whether an unexpected (post-award) increase in delays on routes between project participants has an impact on project outcomes, including the number of publications or patents, the quality of such outcomes (e.g. citations), and the collaborative nature of such outcomes (e.g. whether different investigators are represented equally in such outputs).

Project lead: Dr Carolin Ioramashvili

Research teams bring together people with different perspectives, skills, and experiences. Yet, while diversity is recognised as both morally important and potentially beneficial for creativity, there is limited quantitative evidence on how it improves research outcomes. This project will examine whether research teams with greater cultural ad ethnic diversity produce more interdisciplinary work—research that combines knowledge from multiple fields to tackle complex problems. Using the new Gateway to Research Plus (GtR+) dataset, linked with OpenAlex, we will identify the composition of UK research project teams and analyse the diversity of their outputs. Employing an
innovative diversity index inspired by ecology, and advanced bibliometric measures, we will quantify how origin diversity influences interdisciplinarity. We will also test when and where this relationship is strongest—for example, under funding schemes that emphasise diversity or equality. The findings will provide evidence-based insights for DSIT, and UKRI, showing when diversity acts as a strategic resource for innovation. Outputs will include a policy-ready report, reproducible analytical tools, and recommendations for designing inclusive, high-performing research programmes.

Project lead: Dr Abdullah Gok

The transition between technology creation in academia and technology adoption within industry is critical to the impact of research and delivery of economic growth. Small and Medium Enterprises (SMEs) are a key element of this transition however the diversity and scale of their innovation interests make them a challenging cohort to study.

Over the last fifteen years the use of Artificial Intelligence (AI) has become widespread during a global, macro-innovation event. To benefit from this emerging disruptive technology UK SMEs often needed to access external skills, and many looked to the HE sector utilising schemes such as Innovate UK’s Knowledge Transfer Partnerships (KTP) to provide the knowledge they required. The project will access an archive of 4,000 KTP Final Reports, a unique record of SME innovation practice spanning over 20 years, containing a detailed summary of more than 400 Ai projects. Topic modelling and data fusion techniques, drawing upon recently added information within GtR+, will identify patterns in innovation pathways, academic expertise and partnership dynamics to establish key characteristics of SME:academic engagements. The findings will inform Innovate UK and DSIT policy by providing actionable insights into effective SME–academic engagement, supporting strategic goals for high-value growth and AI-driven innovation.

Project team: Prof Andy Treen and Dr Abdullah Gok

This research project aims to understand how UKRI-funded research projects differ across the UK and how these differences affect regional socio-economic development. The main goal is to use the GtR+ database for abstracts of funded projects to see if certain regions specialise in specific topics, like artificial intelligence or green technology. The study will then investigate if this specialisation helps a region’s economy grow. To do this, the researchers will apply topic modelling techniques to the text data from the project abstracts to automatically identify the main themes. They will track how these themes change over time in different areas. Finally, they will use statistical models to see if there is a link between a region’s research topics and its socio-economic performance. The findings are intended to help policymakers make smarter decisions. This evidence could lead to better funding allocation, ensuring money is invested in areas that build on a region’s existing strengths. This project also aims to create a recurrent evaluation methodology for evaluating the regional innovation strength and place-based policy effectiveness. Ultimately, this should help boost productivity and inform the UK’s innovation policy of the UK.

Project team: Dr Qijun Zhou, Dr Kai Xu and Prof Nuran Acur

Innovate UK faces a persistent strategic dilemma: when to fund innovation. Intervening early may secure a first-mover advantage but risks backing immature technologies. Intervening later supports more developed solutions but may cede markets to global competitors. This ambiguity hinders optimal funding strategy. This project directly addresses this gap by building an evidence-based framework on R&D maturity, funding timing, and commercial success. It leverages the GtR+ dataset, linking it with OpenAlex and comprehensive business data (ORBIS, Crunchbase). The research is driven by questions exploring the link between research maturity (classified via FRASCATI) and subsequent innovation market performance (firm survival, growth, follow-on funding). The methodology involves analysing UKRI research projects, which we consider as the underpinning R&D base of Innovate UK projects, assessing the competitive density of their target markets, and tracking the performance of the commercialising firms. The final output will attempt to identify predictive signals for success. This tool will provide Innovate UK and DSIT with a “what works, where, and why” guide, enabling smarter, evidence-based funding decisions to maximise the impact of public investment.

Project team: George Richardson, Christopher Edgar and David Ampudia Vicente