ENID (ENdometrial Implants DAtaset) comprises 160 images taken from 100+ gynecologic laparoscopy surgeries and is purposefully created to be utilized for a variety of automatic content analysis problems in the context of Endometriosis recognition. ENID is the result of experiments on reorganizing previously published dataset GLENDA in terms of visual similarity. The dataset includes region-based annotations of a specific visual endometriosis manifestation: dark endometrial implants. Annotations are created for single video frames, which can contain multiple annotations. Each single annotation is exported as a pixel-based image mask: the annotated regions are colored, while the background is left black. Additionally a particular dataset augmentation is provided: ground truth annotations are tracked over time using the original videos. The tracking results are acquired via kernelized correlation filters (KCF). You are kindly requested to cite the original work that led to the creation of the dataset: https://doi.org/10.1007/s11042-021-11730-1. For the latest updates, please visit the dataset's homepage: http://ftp.itec.aau.at/datasets/ENID.