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Accepted Paper:
Paper short abstract:
Different types of research data are becoming increasingly available. They are used to conduct analyses of the research portfolios of organisations and funding agencies. These portfolio analyses can be used in for opening up deliberations on priority setting - although current use remains unclear.
Paper long abstract:
Funding agencies use the term research portfolio analysis to describe analyses that map the ensemble their activities. Data providers offer data infrastructure on publications, funded grants, patents, twitters of publications and clinical trials. In parallel, some funding agencies have set up internal mechanisms for portfolio analysis. By classifying this research data into suitable categories (disciplinary, disease type, institutional type), it is now possible to make estimates of the amount of resources spent on given topics, or performed by given units or organisations.
Most agencies are not open about the extent and goals of portfolio analyses conducted. Anecdotal evidence from interviews suggest that the main purposes of portfolio analysis are: i) to provide agencies of baseline information about the areas they support and ii) to internally justify resources spent in terms of outputs obtained.
In this study, however, we aim to explore how portfolio analysis can also be used as part of wider deliberative processes of priority setting. We will present some examples WHO and the Dept. of Health of Catalonia. These wider practices of priority setting require the development of technical expertise in order to manage research data as well as estimates of societal needs. Equally, they also require institutional learning for managing processes of deliberation and integration of knowledge from diverse stakeholders.
We propose that portfolio analysis may constitute an example of how large data can be garnered to visualise opportunities for making research choices - highlighting ambiguities and uncertainties- and thus facilitate deliberative processes of priority setting.
Data worlds? Public imagination and public experimentation with data infrastructures
Session 1