  Large data sets give rise to the `fourth paradigm' of scientific discovery
and technology development, extending other approaches based on human
intuition, fundamental laws of physics, statistics and intense computation.
Both experimental and simulation data are growing explosively in plasma science
and technology, motivating data-driven discoveries and inventions, which are
currently in infancy. Here we describe recent progress in microparticle cloud
imaging and tracking (mCIT, $\mu$CIT) for laboratory plasma experiments. Three
types of microparticle clouds are described: from exploding wires, in dusty
plasmas and in atmospheric plasmas. The experimental data sets are obtained
with one or more imaging cameras at a rate up to 100k frames per second (fps).
A physics-constrained motion tracker, a Kohonen neural network (KNN) or
self-organizing map (SOM), the feature tracking kit (FTK), and U-Net are
described and compared with each other for particle tracking using the
datasets. Particle density and signal-to-noise ratio have been identified as
two important factors that affect the tracking accuracy. Fast Fourier transform
(FFT) is used to reveal how U-Net, a deep convolutional neural network (CNN)
developed for non-plasma applications, achieves the improvements for noisy
scenes. The fitting parameters for a simple polynomial track model are found to
group into clusters that reveal the geometry information about the camera
setup. The mCIT or $\mu$CIT techniques, when enhanced with data models, can be
used to study the microparticle- or Debye-length scale plasma physics. The
datasets are also available for ML code development and comparisons of
algorithms.
