Omkar2118/datasets huggingface dataset:
Liver-US
Full name: Liver Ultrasound Dataset for Medical Image Classification

The Liver-US dataset is a comprehensive collection of high-quality ultrasound images of the liver, including both normal and abnormal cases. This dataset is designed to facilitate research in medical image classification, with a focus on liver-related conditions. It includes a diverse range of ultrasound images acquired from multiple clinical settings, providing a robust foundation for developing and validating machine learning models in medical image analysis.
Detailed Dataset Description

Dataset Characteristics:
The Liver-US dataset contains735 ultrasound images of the liver, including both normal and abnormal cases. The dataset includes a variety of liver conditions, such as Hepatocellular Carcinoma (HCC), liver cysts, hemangiomas, and other focal liver lesions. The images were acquired using state-of-the-art ultrasound equipment, ensuring high resolution and contrast for accurate diagnosis.

Motivations and Content Summary:
The primary motivation behind the creation of the Liver-US dataset is to provide a comprehensive and diverse resource for researchers working on medical image classification task related to the liver. The dataset was compiled in collaboration with leading medical institutions and includes images from a diverse patient population, ensuring representativeness and generalizability. The annotations were performed by experienced radiologists, adhering to strict quality control standards to ensure accuracy and reliability.

Potential Use Cases:
Research and Development: The dataset is ideal for researchers and developers working on machine learning models for medical image classification and feature extraction.
Clinical Applications: It can be used to train and validate computer-aided diagnosis systems for early detection and diagnosis of liver conditions.
Educational Purposes: The dataset serves as a valuable resource for training medical professionals and students in the interpretation of ultrasound images and the diagnosis of liver conditions.
Benchmarking: It provides a standardized benchmark for comparing the performance of different algorithms and models in the field of medical image analysis.

Introducing paper: A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma
STURM-Flood
Full name: STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery

The repository hosts the STURM-Flood dataset, an open-access resource designed for flood extent mapping using Sentinel-1 and Sentinel-2 satellite imagery. The dataset comprises 21,602 Sentinel-1 tiles and 2,675 Sentinel-2 tiles, each of size 128 × 128 pixels at a resolution of 10 meters, along with corresponding water masks covering 60 flood events globally. This curated dataset is optimized for deep learning applications and provides ground-truth data from the Copernicus Emergency Management Service to facilitate robust model development. We invite researchers and developers to utilize this resource for advancing flood mapping techniques in disaster management. For further details on the methodology, results, and implementation, please refer to our study published in Big Earth Data (2096-4471): https://doi.org/10.1080/20964471.2025.2458714 .
CPU

The CPU dataset, first introduced by Rahimi and Recht (2007) and then used by Balog et al (2016). 

Available at the following Dropbox link: https://www.dropbox.com/scl/fi/f0zb1sllf5147xyr4fxgu/cpu.mat
MultiTQ

MULTITQ is a large-scale dataset featuring ample relevant facts and multiple temporal granularities.
TimeQuestions

Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much attention in research. We present EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions.

Introducing paper: Complex Temporal Question Answering on Knowledge Graphs
TempQuestions

Here, we take a key step in this direction and release a new benchmark, TempQuestions, containing 1,271 questions, that are all temporal in nature, paired with their answers.

Introducing paper: TempQuestions: A Benchmark for Temporal Question Answering
TIQ

Existing benchmarks for temporal QA focus on a single information source (either a KB or a text corpus), and include only few questions with implicit constraints. we devise a new method for automatically creating temporal questions with implicit constraints, with systematic controllability of different aspects, including the relative importance of different source types (text, infoboxes, KB), fractions of prominent vs. long-tail entities, question complexity, and more.

Introducing paper: Faithful Temporal Question Answering over Heterogeneous Sources
Complex-CronQuestions

A filtered version of CronQuestions and which can better demonstrate the model’s inference ability for complex temporal questions.