Omkar2118/datasets huggingface dataset:
SportSett

This resource is designed to allow for research into Natural Language Generation.  In particular, with neural data-to-text approaches although it is not limited to these.

Introducing paper: SportSett:Basketball - A robust and maintainable dataset for Natural Language Generation
NucMM

**NucMM** is a dataset for segmenting 3D cell nuclei from microscopy image volumes that pushes the task forward to the sub-cubic millimeter scale. It consists of two fully annotated volumes: one electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei.

Introducing paper: NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
AxonEM

The **AxonEM** dataset consists of two 30x30x30 um^3 EM image volumes from the human and mouse cortex, respectively. It is used for 3D axon instance segmentation of brain cortical regions. The authors proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. In addition, the authors also densely annotate nine ground truth subvolumes for training, per each data volume.

Introducing paper: AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
MSJudge

This is a challenging dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case -- plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning.

Introducing paper: Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting
Cylinder in Crossflow

**Cylinder in Crossflow** is a synthetic dataset that involves unsteady laminar flow past a cylinder that generates vortex
shedding pattern known as a von Kármán vortex street. The governing equations for
this system are the incompressible Navier-Stokes equations. The cylinder
has a diameter of 1 and the free stream velocity is 1. The kinematic viscosity $\nu$ is
varied such that the Reynolds number is between 100 and 400. Symmetry boundary conditions are applied at the top and bottom edges of the domain and an open pressure boundary condition is applied at the outlet. Solutions are generated on the
unstructured mesh of 6384 quad elements.

Introducing paper: A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations