Omkar2118/datasets huggingface dataset:
AVQA

Audio-visual question answering aims to answer questions regarding both audio and visual modalities in a given video. For example, given a video showing a traffic intersection where the light turns red and the parking stick drops, and the question “why did the stick fall in the video?”, it requires to combine the visual information “the stick dropping” and the audio information of a train whistle to answer the question as “Here comes the train”. To achieve an accurate reasoning process and get the correct answer, it is essential to extract cues and contexts from both audio and visual modalities and discover their inner causal correlations.

Real-life scenarios contain more complex relationships between audio-visual objects and a wider varieties of audio-visual daily activities. AVQA is an audio-visual question answering dataset for the multimodal understanding of audio-visual objects and activities in real-life scenarios on videos. AVQA provides diverse sets of questions specially designed considering both audio and visual information, involving various relationships between objects or in activities.
Heteroatom Doped Graphene Supercapacitor

Heteroatom doped graphene supercapacitor feature data is gathered from various literatures for use in machine learning tasks. Main motivation is to optimize supercapacitors and to gain knowledge into models for electrochemistry tasks.

Introducing paper: How False Data Affects Machine Learning Models in Electrochemistry?
ARCADE
Full name: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. 

ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.
Vision-Automatic-Band-Gap-Extractor-Data

Hyperspectral data for a MA/FA lead iodide perovskite gradient.

Introducing paper: Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
Vision-Stability-Measurement

Controlled 2-hour degradation optical time series data for a MA/FA lead iodide perovskite gradient.

Introducing paper: Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
kitab

KITAB is a challenging dataset and a dynamic data collection approach for testing abilities of Large Language Models (LLMs) in answering information retrieval queries with constraint filters. A filtering query with constraints can be of the form "List all books written by Toni Morrison that were published between 1970-1980".

Introducing paper: KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval