This dataset encapsulates a comprehensive collection of eye movement recordings captured during sleep, exceeding 100 distinct episodes. The recordings are primarily categorized into Rapid Eye Movement (REM), Slow Eye Movement (SEM), and non-movement phases, providing a rich resource for sleep research. Each video is meticulously recorded in high-definition .mp4 format, ensuring clarity and precision in capturing subtle ocular dynamics.

The database presented consists of a set of images of the human hand making signs (20) at various angles, corresponding to the Colombian alphabet of signs established by the National Institute for the Deaf (INSOR). These signs are characterized by being static, that is, they do not require movement to be performed.

The Deepfake-Synthetic-20K dataset significantly contributes to digital forensics and deepfake detection research. It comprises 20,000 high-resolution, synthetic human face images generated using the advanced StyleGAN-2 architecture. This dataset is designed to support the development and evaluation of machine-learning models that can differentiate between real and artificially synthesized human faces. Each image in the dataset has been meticulously crafted to ensure a diverse representation of age, gender, and ethnicity, reflecting the variability seen in global human populations.

The foundation of detection relies on the surface micro-defect images of KDP, and the effectiveness of the detection model depends on the quality of these images. Higher quality images can pinpoint the shape details and boundary features of defects, thereby enhancing the overall detection capability.