Datasets provided for Open Platform for Ultrasound Localization Microscopy: Performance Assessment of Localization Algorithms. Abstract: Ultrasound Localization Microscopy (ULM) is an ultrasound imaging technique that relies on the acoustic response of sub-wavelength ultrasound scatterers to map the microcirculation with an order of magnitude increase in resolution. Initially demonstrated in vitro, this technique has matured and sees implementation in vivo for vascular imaging of organs, and tumors in both animal models and humans. The performance of the localization algorithm greatly defines the quality of vascular mapping. We compiled and implemented a collection of ultrasound localization algorithms and devised three datasets in silico and in vivo to compare their performance through 18 metrics. We also present two novel algorithms designed to increase speed and performance. By openly providing a complete package to perform ULM with the algorithms, the datasets used, and the metrics, we aim to give researchers a tool to identify the optimal localization algorithm for their usage, benchmark their software and enhance the overall image quality in the field while uncovering its limits. This article provides all materials and post-processing scripts and functions. Article to be cited: Heiles, Chavignon, Hingot, Lopez, Teston and Couture. Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy, Nature Biomedical Engineering, 2022, (doi.org/10.1038/s41551-021-00824-8). Related processing scripts and codes: github.com/AChavignon/PALA Request on data: arthur.chavignon.pro(at)gmail.com