  The deviation from pure Brownian motion generally referred to as anomalous
diffusion has received large attention in the scientific literature to describe
many physical scenarios. Several methods, based on classical statistics and
machine learning approaches, have been developed to characterize anomalous
diffusion from experimental data, which are usually acquired as particle
trajectories. With the aim to assess and compare the available methods to
characterize anomalous diffusion, we have organized the Anomalous Diffusion
(AnDi) Challenge (\url{http://www.andi-challenge.org/}). Specifically, the AnDi
Challenge will address three different aspects of anomalous diffusion
characterization, namely: (i) Inference of the anomalous diffusion exponent.
(ii) Identification of the underlying diffusion model. (iii) Segmentation of
trajectories. Each problem includes sub-tasks for different number of
dimensions (1D, 2D and 3D). In order to compare the various methods, we have
developed a dedicated open-source framework for the simulation of the anomalous
diffusion trajectories that are used for the training and test datasets. The
challenge was launched on March 1, 2020, and consists of three phases.
Currently, the participation to the first phase is open. Submissions will be
automatically evaluated and the performance of the top-scoring methods will be
thoroughly analyzed and compared in an upcoming article.
