Digital networks, mobile devices, and the possibility of mining the
ever-increasing amount of digital traces that we leave behind in our
daily activities are changing the way we can approach the study of
human and social interactions. Large-scale datasets, however, are
mostly available for collective and statistical behaviors at coarse
granularities, while high-resolution data on one-to-one interactions
are generally limited to relatively small groups of individuals.  Here
we present a scalable experimental framework for gathering real-time
data on face-to-face social interactions with tunable spatial and
temporal resolution. We use active Radio Frequency Identification
(RFID) devices that assess mutual proximity in a distributed fashion
by exchanging low-power radio packets. We show results on the analysis
of the dynamics of person-to-person interaction networks obtained in
high-resolution experiments carried out at different orders of
magnitude in community size. The data sets exhibit common statistical
properties and lack of a characteristic time scale from 20 seconds to
several hours.  The association between the number of connections and
their duration shows an interesting super-linear behavior, which
indicates the possibility of defining super-connectors both in the
number and intensity of connections. The presence of super-connectors
is crucial for diffusion phenomena on social networks ranging from
disease to information spreading.