Omkar2118/datasets huggingface dataset:
CHQ-Summ
Full name: Consumer Healthcare Question Summarization

Contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media.

Introducing paper: CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
MultiDex

We collect a large-scale synthetic dataset for robotic hands with [Differentiable Force Closure(DFC)](https://sites.google.com/view/ral2021-grasp/). It covers 436,000 diverse and stable grasps for 58 household objects from ContactDB and YCB datasets across 5 robotic hands including EZGripper, Barrett Hand, Robotiq-3Finger, Allegro Hand and Shadowhand.

Introducing paper: GenDexGrasp: Generalizable Dexterous Grasping
DR.BENCH
Full name: Diagnostic Reasoning Benchmark for clinical natural language processing

**DR.BENCH** is a dataset for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation.

Introducing paper: DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
Satimage

The resources for this dataset can be found at https://www.openml.org/d/182

Author: Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde
Source: UCI - 1993
Please cite: UCI

The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number.

One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels.

The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel.

Each pixel is categorized as one of the following classes:
1. red soil
2. cotton crop
3. grey soil
4. damp grey soil
5. soil with vegetation stubble
6. mixture class (all types present)
7. very damp grey soil

NB. There are no examples with class 6 in this dataset.

The data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset.