  Modern large-scale datasets are frequently said to be high-dimensional.
However, their data point clouds frequently possess structures, significantly
decreasing their intrinsic dimensionality (ID) due to the presence of clusters,
points being located close to low-dimensional varieties or fine-grained
lumping. We test a recently introduced dimensionality estimator, based on
analysing the separability properties of data points, on several benchmarks and
real biological datasets. We show that the introduced measure of ID has
performance competitive with state-of-the-art measures, being efficient across
a wide range of dimensions and performing better in the case of noisy samples.
Moreover, it allows estimating the intrinsic dimension in situations where the
intrinsic manifold assumption is not valid.
