Omkar2118/datasets huggingface dataset:
DesignQAR

A curated dataset of 221 question-answer-rationale triples capturing visualization design decisions and the reasoning behind them, derived from real-world student-authored narratives.
Each entry includes:

id: Unique identifier (e.g., QAR_001)
concept: One of 9 visualization design concepts (e.g., Color, Layout, Interaction)
question: A natural language question probing a design choice
answer: A short factual answer
rationale: A natural language explanation of the reasoning behind the choice
images: List of filenames associated with the visualization(s)

Introducing paper: Capturing Visualization Design Rationale
VB-DemandEx

Uses same clean speech as VoiceBank+Demand but more noise types. Features much lower SNRs ([−10, −5, 0, 5, 10, 15, 20] dB) compared to VoiceBank+Demand and has babble and speech shaped noise in both train, validation, and test set.

Introducing paper: MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
CaloHadronic

https://zenodo.org/records/15301636

Introducing paper: CaloHadronic: a diffusion model for the generation of hadronic showers
GameQA
Full name: GameQA-140K

GameQA is a large-scale, diverse, and challenging multimodal reasoning dataset designed to enhance the general reasoning capabilities of Vision Language Models (VLMs). Generated using the innovative Code2Logic framework, it leverages game code to synthesize high-quality visual-language Chain-of-Thought (CoT) data. The dataset addresses the scarcity of multimodal reasoning data, critical for advancing complex multi-step reasoning in VLMs. Each sample includes visual game state, targeted question, original analysis, augmented step-by-step reasoning (refinement) and final answer, derived from the logical structures inherent in game code.

Paper: Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning

Code: https://github.com/tongjingqi/Code2Logic

Introducing paper: Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
Dataset for Cell-to-Cell Communications

The dataset evaluates the number of vesicles observed in a Tcell that is close to a tumor cell. It stores the number of vesicles over time and for various distances between the Tcell and the tumor cell. The dataset is a matrix stored in a "mat" file (Matlab), where each row represents a distance and each column corresponds to a time index. The distance ranges from 2 to 10 micrometers, and the time varies from 0 to approximately 160 minutes. We also provide the code to generate this dataset and to plot the corresponding curves.

Introducing paper: Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things