  We introduce an intrinsic estimator for the scalar curvature of a data set
presented as a finite metric space. Our estimator depends only on the metric
structure of the data and not on an embedding in $\mathbb{R}^n$. We show that
the estimator is consistent in the sense that for points sampled from a
probability measure on a compact Riemannian manifold, the estimator converges
to the scalar curvature as the number of points increases. To justify its use
in applications, we show that the estimator is stable with respect to
perturbations of the metric structure, e.g., noise in the sample or error
estimating the intrinsic metric. We validate our estimator experimentally on
synthetic data that is sampled from manifolds with specified curvature.
