The Semantically Mapping Science (SMS) Platform: Towards an Open Linked Data Infrastructure for Social Science Research

Social phenomena are generally complex. Understanding them, and designing public policies that may affect them, requires integrating and analyzing data from multiple sources. Currently, social research is mostly either rich but small scale (qualitative case studies) or large scale and under-complex (because it generally uses a single dataset - often a survey or administrative data). Progress in the social sciences depends on the ability to do large-scale studies with many variables specified by relevant theories: There is a need for studies which are at the same time big and rich, and this requires high quality linked and enriched data, that can be accessed through user-friendly interfaces. The Semantically Mapping Science (SMS) platform, presented in this paper, is a user-centric platform for data enrichment, integration, exploration and analysis with focus on open access to research data and services to tackle this challenge. We show the added value of the SMS platform through a number of illustrative use-cases. The SMS platform focuses on the data needs of researchers, policy makers, and managers in the area of science, technology, and innovation policies, but it generalises to data in other social science domains.