  Identifying collisionless shock crossings in data sent from spacecraft has so
far been done manually. It is a tedious job that shock physicists have to go
through if they want to conduct case studies or perform statistical studies. We
use a machine learning approach to automatically identify shock crossings from
the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those
crossings including various spacecraft related and shock related parameters for
each event. Furthermore, we show that the shocks in the database have
properties that are spread out both in real space and parameter space. We also
present a possible science application of the database by looking for
correlations between ion acceleration efficiency at shocks and different shock
parameters such as $\theta_{Bn}$ and $M_A$. Furthermore, we investigate
statistically the ion acceleration efficiency. We find no clear correlation
between the acceleration efficiency and $M_A$ and we find that quasi-parallel
shocks are more efficient at accelerating ions.
