Omkar2118/datasets huggingface dataset:
Autocast

Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits.
UTIAS Multi-Robot Cooperative Localization and Mapping Dataset

This 2d indoor dataset collection consists of 9 individual datasets. Each dataset contains odometry and (range and bearing) measurement data from 5 robots, as well as accurate groundtruth data for all robot poses and (15) landmark positions. The dataset is intended for studying the problems of cooperative localization (with only a team robots), cooperative localization with a known map, and cooperative simultaneous localization and mapping (SLAM).
Foggy KITTI

The Foggy KITTI dataset extends the KITTI dataset to include challenging weather conditions, aiming to support research in real-world applications such as autonomous driving. It contains synthetic fog images with different levels of intensity and is divided into training and testing sets, providing a useful resource for developing and evaluating models in practical scenarios.

Introducing paper: MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection