The following blog was published on 14th May 2019.
Universities are in the process of gathering evidence and creating documentation to support the submissions to REF, the system for assessing the quality of research in the UK’s higher education system. The target is the best possible rating. This is a vital process that directly affects a university’s future funding and their economic impact on their local communities and, therefore, it is critical they put forward their best team. But, what’s the best and most objective way in which to show this?
IDM’s analysis of traditional assumptions and metrics has shown that perceived wisdom, particularly relating to numbers and quality of research publication output (which accounts for 60% of the REF assessment), works well at an aggregated national level. That is to say that, if UK plc provides more funding to researchers, the general trend is that more research papers are produced, more spin-outs created and so on. However, we’ve dug a little deeper into this and have found that this strong relationship breaks down when the individual record of a researcher is examined rather than the national average. Our deeper analysis of the UK academic landscape uncovered different characteristics of researchers that can be used to validate this observation.
Using a variety of different tests and methods (both supervised and unsupervised machine learning methods), with data for grant funding (number and size of awards), research papers, intellectual property and company spin-outs, we uncovered a clear grouping of researchers into four distinct clusters. These clusters neatly define an individual’s approach to research and industrial engagement based on their historical record of activity.
In the context of the REF, this clustering delineates quite clearly. Superstars (Cluster 1) should be included in REF documents as they exhibit outputs and impacts across the range of measures evaluated. Cluster 2 researchers are those that would need to be assessed as to where that can make the greatest contribution to a REF submission. Cluster 3 researchers will have a solid contribution to make to outputs and impact in REF terms, with positive benefits to the environment scores, whilst Cluster 4 researchers will have high impact scores.
This analysis has implications not only for how you select your teams for REF, but also how you develop your organisational make-up through hiring the type of people you need.
How would you use this to develop your research teams or assess whether a future research relationship will bear fruit?
Would you like us to create a map of your researchers?
Get in touch for more information about how we can work together to have the impact you need for your organisation.
