Omkar2118/datasets huggingface dataset:
Med-EASi

Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. It contains 1979 expert-simple text pairs in medical domain, spanning a total of 4478 UMLS concepts across all text pairs. The dataset is annotated with four textual transformations: replacement, elaboration, insertion and deletion.

Introducing paper: Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts
Real-World Stereo Color and Sharpness Mismatch Dataset

A real-world stereo video dataset, containing 1200 frame pairs with real-world color and sharpness mismatches caused by beam splitter.

Color and sharpness mismatches between views of the stereoscopic 3D video can decrease the overall video quality and may cause viewer discomfort and headaches. To eliminate this problem, there are correction methods that aim to make the views consistent.

We propose a new real-world dataset of stereoscopic videos for evaluating color-mismatch-correction methods. We collected it using a beam splitter and three cameras. A beam splitter introduces real-world mismatches between stereopair views. Similar mismatches can appear in stereoscopic movies filmed with a beam-splitter rig. Our approach used a beam splitter to set a zero stereobase between the left camera and the left ground-truth camera, allowing us to create a distorted ground-truth data pair.

Introducing paper: Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method