  We present MAPFF1.0, a determination of unpolarised charged-pion
fragmentation functions (FFs) from a set of single-inclusive $e^+e^-$
annihilation and lepton-nucleon semi-inclusive deep-inelastic-scattering
(SIDIS) data. FFs are parametrised in terms of a neural network (NN) and fitted
to data exploiting the knowledge of the analytic derivative of the NN itself
w.r.t. its free parameters. Uncertainties on the FFs are determined by means of
the Monte Carlo sampling method properly accounting for all sources of
experimental uncertainties, including that of parton distribution functions.
Theoretical predictions for the relevant observables, as well as evolution
effects, are computed to next-to-leading order (NLO) accuracy in perturbative
QCD. We exploit the flavour sensitivity of the SIDIS measurements delivered by
the HERMES and COMPASS experiments to determine a minimally-biased set of seven
independent FF combinations. Moreover, we discuss the quality of the fit to the
SIDIS data with low virtuality $Q^2$ showing that, as expected, low-$Q^2$ SIDIS
measurements are generally harder to describe within a NLO-accurate
perturbative framework.
