This Working Group develops standards and best practices for quality control (QC) of neuroimaging data, including standardized protocols, easy to use tools and comprehensive manuals. Assessing the quality of neuroimaging data requires human visual inspection. Given the complex nature, diverse presentations, and three-dimensional anatomy of image volumes, this requires inspection in all the three planes and multiple cross-sections through each volume. Often, looking at raw data is not sufficient, but statistical measurements (e.g. across space or time) can greatly assist in identifying the artefacts or rating their severity. For proper QC, multiple types of visualizations and metrics often need to be taken into account, which is time-consuming and subjected to large variabilities.With sample sizes and number of modalities both increasing, there is a great need for developing appropriate QC annotation protocols and corresponding assistive tools.