  The Reservoir Computing (RC) framework states that any non-linear,
input-driven dynamical system (the reservoir) exhibiting properties such as a
fading memory and input separability can be trained to perform computational
tasks. This broad inclusion of systems has led to many new physical substrates
for RC. Properties essential for reservoirs to compute are tuned through
reconfiguration of the substrate, such as change in virtual topology or
physical morphology. As a result, each substrate possesses a unique `quality'
-- obtained through reconfiguration -- to realise different reservoirs for
different tasks. Here we describe an experimental framework to characterise the
quality of potentially any substrate for RC. Our framework reveals that a
definition of quality is not only useful to compare substrates, but can help
map the non-trivial relationship between properties and task performance. In
the wider context, the framework offers a greater understanding as to what
makes a dynamical system compute, helping improve the design of future
substrates for RC.
