A suite of interdisciplinary advanced training opportunities for social scientists at the Cambridge DTC

The Cambridge Doctoral Training Centre will run a linked series of training courses for postgraduate and early career social scientists. These courses will 1) explore the use of large datasets in the social sciences; 2) examine approaches to the evaluation of social problems and 3) offer opportunity for participants to learn about advanced analytical techniques in relation to geographical information about social issues, Course 4 will involve participants learning about ways to utilise and analyse different types of visual data.

One distinctive feature of these courses is that they will involve 'hands on' activity, drawing on established big data sets, for example, as well as researchers' own data.

They are cutting edge courses, designed to develop research capacity and possibilities for research engagement in a changing context for research

As indicated, the direct beneficiaries of this training proposal will be graduate students and early career researchers in the social sciences and those working in other disciplines which have an interface with the social sciences. These will be drawn from within the Cambridge DTC, and more widely from the social sciences within the University (particularly from social scientists working within ESRC Research Centres) as well as from the national social science community as part of ESRC's Advanced Training Network.

Our proposed suite of advanced skills will enhance the potential for impact, as those trained through these courses will be expected to have increased ability to generate and communicate impact. If social scientists can broaden their repertoire of skills it is expected that this will create new opportunities for research with Government and other collaborating partners, as well as facilitating more effective communication, dissemination and policy development.

In terms of broader impact, given that our aim is to achieve wide participation, we expect there to be eventual impact on academics within other Higher Education Institutions, and social scientists in the policy arena (e.g. government-based, private or third sector/voluntary sectors) as course participants take back and try out the new approaches and techniques which they have learned in their home disciplines and institutions, in collaborative ventures with government and non-government organisations, and subsequently in industry and other employment settings when participants complete their Ph.Ds and seek employment. We very much see this suite of courses as contributing to capacity building in terms of new advances in research methodology - with a subsequent trickle-down effect across disciplines and organisations. We think that there is particular need to train a new generation of social scientists in evaluation techniques as the public sector provision of services is opened up to new providers, for instance. To illustrate the issue, whereas the provision of community based interventions for offenders (programmes) has hitherto been through the state criminal justice system (the probation service), it is now being opened up to new private and third sector providers. These new providers will require input from social scientists to guide evaluation. Similarly, as the collection and format of data changes, and as new data sets come on stream, there is need for a new generation of social science researchers who can engage with big data and can deploy a range of approaches to the presentation and analysis of data.

Our proposal will directly address the challenges of changing social research contexts and evolving forms of media, equipping social scientists with a wide range of approaches. There will be direct applicability of this enhanced capacity; through engagement with collaborative activity within the DTC we will be able to ensure that benefits accruing from this training are translated into the external sphere. Dissemination in this way will be complemented by sharing through the DTC network our evaluation of this training and learning points identified.