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© The Author(s) 2018. All rights reserved. Good quality research depends on good quality data. In multidisciplinary projects with quantitative and qualitative data, it can be difficult to collect data and share it between partners with diverse backgrounds in a timely and useful way, limiting the ability of different disciplines to collaborate. This chapter will explore two examples of the impact of data collection and sharing on analysis in a recent Horizon 2020 project, RealValue. The main insight is that it is not only projects but also the processes within them such as data collection, sharing and analysis that are socio-technical. We shall examine two examples within the project-validating the models and triangulating the qualitative data-to examine data synergy across four dimensions: time (synchronising activities), people (managing and coordinating actors), technology (in this case focusing mainly on connectivity) and quality. Recommendations include developing a data protocol for the energy demand community built on these four dimensions.

Original publication





Book title

Advancing Energy Policy: Lessons on the Integration of Social Sciences and Humanities

Publication Date



63 - 81