19,997 machine learning datasets
19,997 dataset results
U2-BENCH is the first large-scale benchmark for evaluating Large Vision-Language Models (LVLMs) on ultrasound imaging understanding. It provides a diverse, multi-task dataset curated from 40 licensed sources, covering 15 anatomical regions and 8 clinically inspired tasks across classification, detection, regression, and text generation.
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This is the dataset released along with the publication:
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The dataset from the study "A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit". The dataset comprises annotated transcripts and surveys (including self-reported readiness to quit smoking) from 106 conversations between human smokers and MIBot v6.3A — a motivational interviewing (MI) chatbot built using OpenAI's GPT-4o.
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Military Vehicles for Hierarchical Multi-label Classification (HMC)
ASyMOB (pronounced Asimov, in tribute to the renowned author), is a novel assessment framework focused exclusively on symbolic manipulation, featuring 17,092 unique math challenges, organized by similarity and complexity. ASyMOB enables analysis of LLM failure root-causes and generalization capabilities by comparing performance in problems that differ by simple numerical or symbolic "perturbations".
Steerability probe example for text-rewriting.
GLARE is an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews from 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.
Introduction
TimeGraph is a comprehensive suite of synthetic datasets designed to benchmark causal discovery algorithms on time-series data. The dataset captures real-world complexities by incorporating temporal dynamics such as trends, seasonality, and nonstationarity, as well as sampling challenges including irregular time intervals and structured missingness. It features diverse noise types, including Gaussian, heavy-tailed, and heteroskedastic variations, and supports scenarios with latent confounding to enable evaluation under partially observed systems. The underlying causal structures span both linear and nonlinear relationships, including polynomial and trigonometric forms.
Dataset for studying the relationship between music and lighting in live music performances
This dataset is derived from the Waymo Motion dataset and focuses on capturing the interactions between autonomous vehicles (AVs) and traffic control devices such as traffic lights and stop signs. It addresses a critical gap by providing real-world trajectory data that reflects how AVs interpret and respond to traffic control signals, supporting research in AV behavior modeling, traffic simulation, and the design of intelligent transportation systems.
The prospective upper body thermal images SARS-CoV2 association study was designed to test the hypothesis that thermal videos may aid in the early diagnosis of COVID-19. The study recorded a set of measurements from 252 participants regarding PCR results, demographics, vital signs, participant activities, medications, respiratory symptoms, and a thermal video session where the volunteers performed simple breath-hold in four different positions. The acquired data may be used to test clinical association questions regarding temperature patterns, demographics, and vital signs. Furthermore, it could be valuable to develop new computer algorithms for extracting useful scientific information from thermal videos.
NoMusic - The Norwegian Multi-Dialectal Slot and Intent Detection Corpus https://aclanthology.org/2024.vardial-1.9/
LEMONADE is a large, expert-annotated dataset for event extraction from news articles in 20 languages: English, Spanish, Arabic, French, Italian, Russian, German, Turkish, Burmese, Indonesian, Ukrainian, Korean, Portuguese, Dutch, Somali, Nepali, Chinese, Persian, Hebrew, and Japanese.
B-XAIC consists of 50K small molecules represented as graphs and includes 7 graph classification tasks, each with ground truth labels and corresponding explanations.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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