Abstract. 
Due to the development of sensors of various types and the use
of digital media and computational devices, we increasingly leave digital
traces of our daily activities. The scale at which such data can be
gathered and analyzed affords a novel, data-driven approach in the
investigation of various aspects of human behavior. In this talk, I will
focus on the research done within the SocioPatterns project 
(www.sociopatterns.org), in which we have developed the
SocioPatterns sensing platform to obtain longitudinal datasets on
face-to-face contact events between individuals in a variety of contexts
ranging from scientific conferences to museum, school or hospitals. I will
describe some properties of the gathered datasets, which reveal interesting
similarities and differences of human interaction patterns across contexts.
The collected data can also inform epidemiologcal models by giving access
to contact matrices and contact networks. I will also show an example of
data-driven simulation of an epidemic spreading phenomenon.