Omkar2118/datasets huggingface dataset:
EXPLICIT 3D CHANGE DETECTION USING RAY-TRACING IN SPHERICAL COORDINATES

Real and simulated lidar data of indoor and outdoor scenes, before and after geometric scene changes have occurred. Data include lidar scans from multiple viewpoints with provided coordinate transforms, and manually annotated ground-truth regarding which parts of the scene have changed between subsequent scans.

Introducing paper: Explicit 3D Change Detection using Ray-Tracing in Spherical Coordinates
ACFR Orchard Fruit Dataset

ACFR Orchard Fruit Dataset is an agricultural dataset containing images and annotations for different fruits, collected at different farms across Australia. The dataset was gathered by the agriculture team at the Australian Centre for Field Robotics, The University of Sydney, Australia.

Source: [ACFR Orchard Fruit Dataset](https://data.acfr.usyd.edu.au/ag/treecrops/2016-multifruit/)

Introducing paper: Deep Fruit Detection in Orchards
University of Washington/Northwestern University (UW/NU) Corpus

The University of Washington/Northwestern University (UW/NU) Corpus contains recordings and textgrids of Pacific Northwest and Northern Cities speakers reading a subset of the IEEE "Harvard" sentences. The UW/NU Corpus Version 1.0 has been used to study the effects of dialectal variation on speech intelligibility, while version 2.0 is being used in ongoing research in speech intelligibility and gender interaction. Development is supported by the National Institutes of Health, National Institute on Deafness and Other Communication Disorders grant R01-DC006014. The PN/NC Corpus is well suited for both clinical and research studies where high-fidelity recordings and regional accent control are desirable.
UNITOPATHO

Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. The slides are acquired through a Hamamatsu Nanozoomer S210 scanner at 20× magnification (0.4415 μm/px)

Introducing paper: UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading