Scratch assay evaluation is a widely used and standardized method for investigating collective cell migration and wound healing processes. Modern laboratory technology offers the possibility of high-resolution digital image acquisition leading to the use of computer vision methods for the analysis of scratch assays.Benchmarking is necessary to measure the performance of newly developed analytical tools. Publicly available data sets and software tools are needed to ensure objective and reproducible testing. This work presents a new dataset consisting of high-resolution raw images obtained with an industrial plate reader together with ground truth data manually annotated by experts to overcome the sparse availability of annotated scratch assay images. The advantage of this dataset over other publicly available datasets is its much higher resolution (up to 10 times). It also covers the entire cell layer in a well, allowing the entire wound area to be examined rather than just the central region. In addition to the dataset, a software tool will be published containing two algorithms developed to measure wound healing using the presented data. Their performance will be compared with other publicly available tools.