Big Qual - Why We Should Be Thinking Big About Qualitative Data for Research, Teaching and Policy
When social scientists think about big data, they often think in terms of quantitative number crunching. However, the growing availability of ‘big’ qualitative datasets presents new opportunities for qualitative research.
Up to now, STI (Science, Technology, Innovation) studies are either rich but small scale (qualitative case studies) or large scale and under-complex. However, progress in the STI research field depends in our view on the ability to do large-scale studies with often many variables specified by relevant theories: There is a need for studies which are at the same time big and rich. To enable that, combining and integration of STI data and beyond is needed – in order to exploit the huge amount of data that are ‘out there’ in an innovative and meaningful way.
The aim of the Semantically Mapping Science (SMS) platform as the technical core within the RISIS EU project is to produce richer data to be used in social research – through the integration of heterogeneous datasets, ranging from tabular statistical data to unstructured data found on the Web.
Fitbit's 150 Billion Hours of Heart Data Reveal Secrets About Health
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Crowdsourcing biomedical research: leveraging communities as innovation engines
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