Single-molecule localisation microscopy (SMLM) allows the mapping of the positions of individual molecules in cells with nanometre precision. As such, the data format is a point cloud, rather than a conventional microscopy image. We have developed a range of tools for extracting biologically relevant descriptors of the distribution of biomolecules from SMLM data sets, principally, the degree of molecular clustering. Here, we demonstrate a community-led approach to the evaluation of the performance of these cluster analysis algorithms. Additionally, we have launched a publically accessible, curated database for SMLM data storage, sharing and dissemination. A new algorithm for comparing the similarity of point clouds allows searching this databases for experimental conditions that show similar statistical character.