Omkar2118/datasets huggingface dataset:
Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification
Full name: chestxray dataset

Dataset of validated OCT and Chest X-Ray images  described and analyzed in "Deep learning-based classification and referral of treatable human diseases". The OCT Images are split into a training set and a testing set of independent patients. OCT Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL.
ULI-RI
Full name: Unreal Labeled Images for Person Re-ID

The ULI-RI dataset is generated using the Unreal Engine 4 to simulate various outdoor environments with 115 high-quality 3D human models. 
For each person identity, we controlled and quantitatively labeled the illumination intensity, view point (model z-rotation angle), and background to create 512 images.
There are total 115 x 512 = 58880 images in the ULI-RI dataset.

Introducing paper: Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification
Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks

Dataset used in the publication of Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks. The paper is published at npj Computational Materials (https://www.nature.com/articles/s41524-023-01125-1)

Introducing paper: Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design
Wikidata5M

Wikidata5m is a million-scale knowledge graph dataset with aligned corpus. This dataset integrates the Wikidata knowledge graph and Wikipedia pages. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities.

The dataset is distributed as a knowledge graph, a corpus, and aliases. We provide both transductive and inductive data splits used in the original paper.

Introducing paper: KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation