  This paper introduces a notion of data informativity for stabilization
tailored to continuous-time signals and systems. We establish results
comparable to those known for discrete-time systems with sampled data. We
justify that additional assumptions on the properties of the noise signals are
needed to understand when sampled versions of continuous-time signals are
informative for stabilization, thereby introducing the notions of square
Lipschitzness and total bounded variation. This allows us to connect the
continuous and discrete domains, yielding sufficient conditions to synthesize a
stabilizing controller for the true continuous-time system on the basis of
sampled data. Simulations illustrate our results.
