  One of the goals of Space Weather studies is to achieve a better
understanding of impulsive phenomena, such as Coronal Mass Ejections (CMEs), in
order to improve our ability to forecast them and mitigate the risk to our
technologically driven society. The essential part of achieving this goal is to
assess the performance of forecasting models. To this end, the quality and
availability of suitable data are of paramount importance. In this work, we
have merged already publicly available data of CMEs from both in-situ and
remote instrumentation in order to build a database of CME properties. To
evaluate the accuracy of such a database and confirm the relationship between
in-situ and remote observations, we have employed the drag-based model (DBM)
due to its simplicity and inexpensive cost of computational resources. In this
study, we have also explored the parameter space for the drag parameter and
solar wind speed using a Monte Carlo approach to evaluate how well the DBM
determines the propagation of CMEs for the events in the dataset. The dataset
of geoeffective CMEs constructed as a result of this work provides validation
of the initial hypothesis about DBM, and solar wind speed and also yields
further insight into CME features like arrival time, arrival speed, lift-off
time, etc. Using a data-driven approach, this procedure allows us to present a
homogeneous, reliable, and robust dataset for the investigation of CME
propagation. On the other hand, possible CME events are identified where DBM
approximation is not valid due to model limitations and higher uncertainties in
the input parameters, those events require more thorough investigation.
