Omkar2118/datasets huggingface dataset:
EDT

The EDT dataset is designed for corporate event detection and text-based stock prediction (trading strategy) benchmark.

1. Corporate Event Detection
It includes 9721​ news articles with token-level event labels. Including 11 event types:
Acquisitions, Clinical Trials, Guidance Changes, New Contracts, Stock Repurchases, Stock Split, Reverse Stock Split/ADS Ratio Change, Regular Dividend, Special Dividend, Dividend Cut, Dividend Increase

2. Text-Based Stock Prediction Benchmark
It includes 303893​ first-hand news articles from high-quality sources. Each news article is assigned a minute-level timestamp and comprehensive stock price labels.

Please see this [Github Link](https://github.com/Zhihan1996/TradeTheEvent/tree/main/data) and [paper](https://aclanthology.org/2021.findings-acl.186.pdf) for more details.

Introducing paper: Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
ARD-16
Full name: Ati Real-world Dataset

We create ARD-16 (Ati Realworld Dataset), a
first of its kind real-world paired correspondence dataset, by applying our dataset generation method on 16-beam VLP-16 Puck LiDAR scans on a slow-moving Unmanned Ground Vehicle. We obtain ground truth poses by using fine resolution brute force scan matching, similar to Google's Cartographer. It was captured in outdoor environment at Robert Bosch centre, IISc
with no moving objects during static run and several moving objects (1 car, 1 2-wheeler, few pedestrians) during dynamic run. It consists of 1.5k scans/run and we collected 10 dynamic and 5 static runs. This gives about 14k LiDAR scan pairs
for training, validation and testing.

Introducing paper: DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
CARLA-64

We create 64-beam LiDAR dataset with settings similar to Velodyne VLP-64 LiDAR on the CARLA simulator. It contains no moving objects during static run and several moving objects (cars, 2-wheelers, pedestrians) during dynamic runs. It consists of 16 dynamic runs and 8 static runs. This gives about 32k LiDAR scan pairs for training, validation ad tesing.

Introducing paper: DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
Custom FINNgers

A dataset with 3200 images (200 for each number quantity on each hand).

Introducing paper: FINNger -- Applying artificial intelligence to ease math learning for children