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
Fitibit's wristbands have collected 150 billion hours' worth of heart-rate data from people around the world. For the first time, the company offered a look inside that data, to see how lifestyle, location, age, and gender affects our health and longevity.
Crowdsourcing biomedical research: leveraging communities as innovation engines
Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed.