  We present a determination of the parton distributions of the nucleon from a
global set of hard scattering data using the NNPDF methodology: NNPDF2.0.
Experimental data include deep-inelastic scattering with the combined HERA-I
dataset, fixed target Drell-Yan production, collider weak boson production and
inclusive jet production. Next-to-leading order QCD is used throughout without
resorting to K-factors. We present and utilize an improved fast algorithm for
the solution of evolution equations and the computation of general hadronic
processes. We introduce improved techniques for the training of the neural
networks which are used as parton parametrization, and we use a novel approach
for the proper treatment of normalization uncertainties. We assess
quantitatively the impact of individual datasets on PDFs. We find very good
consistency of all datasets with each other and with NLO QCD, with no evidence
of tension between datasets. Some PDF combinations relevant for LHC observables
turn out to be determined rather more accurately than in any other parton fit.
