  The Square Kilometre Array Observatory (SKAO) will explore the radio sky to
new depths in order to conduct transformational science. SKAO data products
made available to astronomers will be correspondingly large and complex,
requiring the application of advanced analysis techniques to extract key
science findings. To this end, SKAO is conducting a series of Science Data
Challenges, each designed to familiarise the scientific community with SKAO
data and to drive the development of new analysis techniques. We present the
results from Science Data Challenge 2 (SDC2), which invited participants to
find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data
product representing a 2000~h SKA MID spectral line observation from redshifts
0.25 to 0.5. Through the generous support of eight international supercomputing
facilities, participants were able to undertake the Challenge using dedicated
computational resources. Alongside the main challenge, `reproducibility awards'
were made in recognition of those pipelines which demonstrated Open Science
best practice. The Challenge saw over 100 participants develop a range of new
and existing techniques, with results that highlight the strengths of
multidisciplinary and collaborative effort. The winning strategy -- which
combined predictions from two independent machine learning techniques to yield
a 20 percent improvement in overall performance -- underscores one of the main
Challenge outcomes: that of method complementarity. It is likely that the
combination of methods in a so-called ensemble approach will be key to
exploiting very large astronomical datasets.
