  The Quijote simulations are a set of 44,100 full N-body simulations spanning
more than 7,000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b},
h, n_s, \sigma_8, M_\nu, w \}$ hyperplane. At a single redshift the simulations
contain more than 8.5 trillions of particles over a combined volume of 44,100
$(h^{-1}{\rm Gpc})^3$; each simulation follow the evolution of $256^3$, $512^3$
or $1024^3$ particles in a box of $1~h^{-1}{\rm Gpc}$ length. Billions of dark
matter halos and cosmic voids have been identified in the simulations, whose
runs required more than 35 million core hours. The Quijote simulations have
been designed for two main purposes: 1) to quantify the information content on
cosmological observables, and 2) to provide enough data to train machine
learning algorithms. In this paper we describe the simulations and show a few
of their applications. We also release the Petabyte of data generated,
comprising hundreds of thousands of simulation snapshots at multiple redshifts,
halo and void catalogs, together with millions of summary statistics such as
power spectra, bispectra, correlation functions, marked power spectra, and
estimated probability density functions.
