  The social sciences have produced an impressive body of research on
determinants of fertility outcomes, or whether and when people have children.
However, the strength of these determinants and underlying theories are rarely
evaluated on their predictive ability on new data. This prevents us from
systematically comparing studies, hindering the evaluation and accumulation of
knowledge. In this paper, we present two datasets which can be used to study
the predictability of fertility outcomes in the Netherlands. One dataset is
based on the LISS panel, a longitudinal survey which includes thousands of
variables on a wide range of topics, including individual preferences and
values. The other is based on the Dutch register data which lacks attitudinal
data but includes detailed information about the life courses of millions of
Dutch residents. We provide information about the datasets and the samples, and
describe the fertility outcome of interest. We also introduce the fertility
prediction data challenge PreFer which is based on these datasets and will
start in Spring 2024. We outline the ways in which measuring the predictability
of fertility outcomes using these datasets and combining their strengths in the
data challenge can advance our understanding of fertility behaviour and
computational social science. We further provide details for participants on
how to take part in the data challenge.
