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Large data sets give rise to the `fourth paradigm' of scientific discoveryand technology development, extending other approaches based on humanintuition, fundamental laws of physics, statistics and intense computation.Both experimental and simulation data are growing explosively in plasma scienceand technology, motivating data-driven discoveries and inventions, which arecurrently in infancy. Here we describe recent progress in microparticle cloudimaging and tracking (mCIT, $\mu$CIT) for laboratory plasma experiments. Threetypes of microparticle clouds are described: from exploding wires, in dustyplasmas and in atmospheric plasmas. The experimental data sets are obtainedwith 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) orself-organizing map (SOM), the feature tracking kit (FTK), and U-Net aredescribed and compared with each other for particle tracking using thedatasets. Particle density and signal-to-noise ratio have been identified astwo 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 noisyscenes. The fitting parameters for a simple polynomial track model are found togroup into clusters that reveal the geometry information about the camerasetup. The mCIT or $\mu$CIT techniques, when enhanced with data models, can beused to study the microparticle- or Debye-length scale plasma physics. Thedatasets are also available for ML code development and comparisons ofalgorithms.

Microparticle cloud imaging and tracking for data-driven plasma science