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Neuroscientists need to visualise data from laboratory experiments in which measurements exist within a hierarchy of levels of interdependence, and where multiple sources of batch effects may influence experimental results. For example, when investigating the electrophysiological properties of neurons in a rodent model of a neurodevelopmental condition, measurements are made on diverse individual neurons whose behaviour is not necessarily independent of each other — multiple neurons may be probed within an individual brain slice, multiple slices originate from a single brain, and multiple animals be tested which may or may not be litter mates or have shared cages. 

Influences at any level within this hierarchy can impact groups of measurements, and can be difficult to disentangle from the effects of interest being investigated. Moreover, data may be gathered by different investigators on several dates using diverse experimental equipment, which sources of batch effects may further complicate data interpretation.