Social Sciences, Social Data and the Semantic Web (S3W)

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Recent years have seen phenomenal growth in quantity and range of digital data that might be used for social research. The ESRC has already invested in harnessing administrative and business data as well as 'new and emerging forms of data' (e.g. social media and sensor data) for the social sciences. Now a new opportunity arises. 'Semantic linked data' (SLD) offers a new method for structuring and organizing digital data, which promises to have a profound effect on research capacity for data linkage and analysis across multiple, heterogeneous sources, at hitherto unimaginable speed and scale. Indeed, within the Computer Sciences, the proponents of SLD argue that if data are published following shared standards and protocols the Web will be transformed from a library of documents into a single linked data base, described as the 'semantic web'. The value of data linkage is already well established in the social sciences, but existing methods are labour intensive, involve the retrospective matching of records, across small numbers of data sets, and are done to address particular pre-determined questions with the linkage made for that specific purpose. In contrast, SLD techniques focus on the prospective production of data to allow the on-going matching and accumulation of information about people, places, businesses, artefacts and even conceptual categories and like 'race' or 'class' to be drawn together, however the subsequent user determines, at the scale of the World Wide Web (Halford, Pope and Weal 2012).However, whilst SLD offers great promise to the social sciences there is - to date - negligible use of SLD by social scientists. The agenda for SLD is being driven by computer scientists and demonstrations are based on relatively straightforward examples such as transport timetables or estates data. Whilst these work well technically, they offer no substantial investigation of how appropriate the techniques might be in addressing more complex social science questions. At a time of financial constraint, when funding for major new data collection is uncertain, it is essential that we explore these opportunities. The research proposed here will be the detailed investigation into if and how SLD might be harnessed for social science research. To achieve this we have drawn together a strong team of social and computational scientists, with a well-established track record of collaboration. This team will be supported by an outstanding Advisory Group of experts, who have already agreed to participate in this project (see Impact Summary below). We will explore three research questions: (i) What are the implications of using SLD methods to describe social data? (ii) What does SLD contribute to our capacity to understand health inequalities across the life course?(iii) What are the implications of SLD for data archiving and re-use? To answer these questions we will: (i) Carry out a detailed study of the processes involved in converting existing data into SLD. This will be undertaken by the research team, with the participation of experts from our Advisory Group. Specifically, we will work with the English Longitudinal Survey of Ageing and the Great British Class Survey and other related data already in the 'linked data cloud'(ii) Develop a 'demonstrator' of SLD (using the data sets developed at (i) above) to examine the specific question of health inequalities across the life-course (iii) Collaborate with the UK Data Service and the GESIS-Leibniz Institute in Germany (which provides a similar data infrastructure to UKDS) to explore the opportunities for data archiving. In this way, we seek to engage social science in the ongoing development of SLD and the emerging Semantic Web; and to explore the implications of SLD for building next generation data infrastructures in the social sciences.