  The publicly accessible dataset includes neutron and gamma-ray pulse signals
for conducting pulse shape discrimination experiments. Several traditional and
recently proposed pulse shape discrimination algorithms are utilized to
evaluate the performance of pulse shape discrimination under raw pulse signals
and noise-enhanced datasets. These algorithms comprise zero-crossing (ZC),
charge comparison (CC), falling edge percentage slope (FEPS), frequency
gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient
(LG), and het-erogeneous quasi-continuous spiking cortical model (HQC-SCM). In
addition to the pulse signals, this dataset includes the source code for all
the aforementioned pulse shape discrimination methods. Moreover, the dataset
provides the source code for schematic pulse shape discrimination performance
evaluation and anti-noise performance evaluation. This feature enables
researchers to evaluate the performance of these methods using standard
procedures and assess their anti-noise ability under various noise conditions.
In conclusion, this dataset offers a comprehensive set of resources for
conducting pulse shape discrimination experiments and evaluating the
performance of various pulse shape discrimination methods under different noise
scenarios.
