  Context. In the current ever increasing data volumes of astronomical surveys,
automated methods are essential. Objects of known classes from the literature
are necessary for training supervised machine learning algorithms, as well as
for verification/validation of their results. Aims.The primary goal of this
work is to provide a comprehensive data set of known variable objects from the
literature cross-matched with \textit{Gaia}~DR3 sources, including a large
number of both variability types and representatives, in order to cover as much
as possible sky regions and magnitude ranges relevant to each class. In
addition, non-variable objects from selected surveys are targeted to probe
their variability in \textit{Gaia} and possible use as standards. This data set
can be the base for a training set applicable in variability detection,
classification, and validation. MethodsA statistical method that employed both
astrometry (position and proper motion) and photometry (mean magnitude) was
applied to selected literature catalogues in order to identify the correct
counterparts of the known objects in the \textit{Gaia} data. The cross-match
strategy was adapted to the properties of each catalogue and the verification
of results excluded dubious matches. Results.Our catalogue gathers 7\,841\,723
\textit{Gaia} sources among which 1.2~million non-variable objects and
1.7~million galaxies, in addition to 4.9~million variable sources representing
over 100~variability (sub)types. Conclusions.This data set served the
requirements of \textit{Gaia}'s variability pipeline for its third data release
(DR3), from classifier training to result validation, and it is expected to be
a useful resource for the scientific community that is interested in the
analysis of variability in the \textit{Gaia} data and other surveys.
