  Deviations from Brownian motion leading to anomalous diffusion are
ubiquitously found in transport dynamics, playing a crucial role in phenomena
from quantum physics to life sciences. The detection and characterization of
anomalous diffusion from the measurement of an individual trajectory are
challenging tasks, which traditionally rely on calculating the mean squared
displacement of the trajectory. However, this approach breaks down for cases of
important practical interest, e.g., short or noisy trajectories, ensembles of
heterogeneous trajectories, or non-ergodic processes. Recently, several new
approaches have been proposed, mostly building on the ongoing machine-learning
revolution. Aiming to perform an objective comparison of methods, we gathered
the community and organized an open competition, the Anomalous Diffusion
challenge (AnDi). Participating teams independently applied their own
algorithms to a commonly-defined dataset including diverse conditions. Although
no single method performed best across all scenarios, the results revealed
clear differences between the various approaches, providing practical advice
for users and a benchmark for developers.
