Other talks also reflected the shift towards a data-centric approach, with a focus on building quality data sets. The notion of data annotation quality was central, and many speakers discussed the challenges of achieving high-quality data sets. As a community, we have a clear understanding of how to measure the quality of models. However, the quality of the data set is somehow still a vague and largely unexplored problem. To bring some light to the topic, some speakers proposed measuring errors in a data set as one of the most important quality measurements.