Omkar2118/datasets huggingface dataset:
Vulpi et al. 2021
Full name: San Cassiano Terrain Classification Dataset

Recorded with a Husky A200 wheeled UGV, the Vulpi 2021 dataset contains 13 min of Inertial Measurement Unit (IMU), motor current, and wheel odometry data, focusing on agricultural terrains.
The dataset includes experiments on concrete, a dirt road, a ploughed terrain and an unploughed terrain that were all recorded on an experimental farm in San Cassiano, Lecce, Italy.
SatBird
Full name: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data

SatBird is a dataset and benchmark for the task of predicting bird species encounter rates jointly at a specific location using remote sensing data. The dataset was obtained from publicly available eBird bird observation records, Sentinel-2 satellite data, and WorldClim and SoilGrids environmental data.
SatBird is composed of 3 sub-datasets: (i) USA summer dataset, generally corresponding to the breeding season, (ii) USA winter dataset, the nonbreeding season, (iii) Kenya dataset, as an example of a low-data regime. 670 and 1054 bird species are considered for the USA and Kenya respectively.

Introducing paper: SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data
Chem-FINESE

The dataset contains two few-shot chemical fine-grained entity extraction datasets, based on human-annotated ChemNER+ and CHEMET.
For each dataset, we randomly sample a subset based on the frequency of each type class. Specifically, given a dataset, we first set the number of maximum entity mentions $k$ for the most frequent entity type in the dataset. We then randomly sample other types and ensure that the distribution of each type remains the same as in the original dataset. We choose the values $6, 9, 12, 15, 18$ as the potential maximum entity mentions for $k$. The ChemNER+ and CHEMET few-shot datasets contain 52 and 28 types respectively.

Introducing paper: Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction