19,997 machine learning datasets
19,997 dataset results
A collaborative effort between researchers at the Vascular Imaging Lab located at the University of Calgary and the Medical Image Computing Lab located at the University of Campinas (UNICAMP) originated the Calgary Campinas public brain magnetic resonance (MR) images dataset.
Dataset for task complexity classification and the complexity score prediction
VisArgs is a densely annotated benchmark for visual argument understanding. It contains 1,611 images annotated with 5,112 visual premises (with regions), 5,574 commonsense premises, and reasoning trees connecting them into structured arguments. We propose three tasks for evaluating visual argument understanding: premise localization, premise identification, and conclusion.
RadCases Dataset This HuggingFace (HF) dataset contains the raw case labels for input patient "one-liner" case summaries according to the ACR Appropriateness Criteria. Because many of the sources of data used to construct the RadCases dataset require credentialed access, we cannot publicly release the input patient case summaries. Instead, the "cases" included in this publicly available dataset are the cryptographically secure SHA-512 hashes of the original, "human-readable" cases. In this way, the hashes cannot be used to reconstruct the original RadCases dataset, but can instead be used as a lookup key to determine the ground-truth label for the dataset.
Dataset Card for the ACR Appropriateness Criteria Corpus This dataset contains chunked guidelines and narratives from the ACR Appropriateness Criteria, an set of societal guidelines from the American College of Radiology (ACR) to help clinicians order appropriate diagnostic imaging studies for patients. The corpus is formatted similarly to the corpuses introduced in MedRAG by Xiong et al. (2024), and can therefore be similarly used for medical Retrieval-Augmented Generation (RAG).
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Test-driven benchmark to challenge LLMs to write long JavaScript React application
The dataset comprises 1641 questions and answers generated as three separate parts. The first part of the dataset contains questions and answers that test the model’s ability to understand the current status of the environment and the provided rules. The second part of the dataset contains questions that test the model’s ability to generate valid actions, both in terms of syntax (JSON format) and semantics (validity in the specific state). The 3rd part of the dataset part aims to teach the fine-tuned models how to make correct decisions given a specific environment state.
A collection of large languge model responses to tasks of propositional logic. The responses are annotated according to the following criteria:
GenAIPABench is a specialized dataset designed to evaluate Generative AI-based Privacy Assistants (GenAIPAs). These assistants aim to simplify complex privacy policies and data protection regulations, making them more accessible and understandable to users. The dataset provides a comprehensive framework for assessing the performance of AI models in interpreting and explaining privacy-related documents.
This dataset contains closed-loop position, velocity, and current trajectories from 83 motor drives, each consisting of a motor and a Harmonic Drive gearbox. It was created to aid in quantifying manufacturing variation at Mecademic Industrial Robotics. The dataset is suitable for system identification and state estimation tasks.
MediConfusion is a challenging medical Visual Question Answering (VQA) benchmark dataset, that probes the failure modes of medical Multimodal Large Language Models (MLLMs) from a vision perspective. We reveal that state-of-the-art models are easily confused by image pairs that are otherwise visually dissimilar and clearly distinct for medical experts. <br /> Our benchmark consists of 176 confusing pairs. A confusing pair is a set of two images that share the same question and corresponding answer options, but the correct answer is different for the images. <br /> We evaluate models based on their ability to answer <i>both</i> questions correctly within a confusing pair, which we call <b>set accuracy</b>. This metric indicates how well models can tell the two images apart, as a model that selects the same answer option for both images for all pairs will receive 0% set accuracy. We also report <b>confusion</b>, a metric that describes the proportion of confusing pairs where the model ha
These are the summary crosstabular data of the 2024 ISPSOS survey on which the paper, "Automation from the Worker's Perspective" is based.
With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. This paper analyzes how Twitter bots interact with scholarly articles on the platform. Spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world. In this paper, we determined whether bots are disseminating a given scholarly article based on analyzing the relationship between Twitter bots and several research factors. We developed and tested several supervised machine-learning classification models to tackle this problem. Through our analysis, we also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural networks on high-order domains such as simplicial complexes. However, progress in this field is hindered by the scarcity of datasets for benchmarking these architectures. To address this gap, we introduce MANTRA, the first large-scale, diverse, and intrinsically high order dataset for benchmarking high-order models, comprising over 43,000 and 249,000 triangulations of surfaces and three-dimensional manifolds, respectively. With MANTRA, we assess several graph- and simplicial complex-based models on three topological classification tasks. We demonstrate that while simplicial complex-based neural networks generally outperform their graph-based counterparts in capturing simple topological invariants, they also struggle, suggesting a rethink of TDL
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WinoPron is a novel dataset of Winogender-like template pairs in English, which fixes inconsistencies in Winogender Schemas and contains balanced template pairs for pronoun forms in 3 grammatical cases, which we find impacts performance and bias evaluation.
aThis dataset provides NFT ownership traces and detection of potential wash trading activities across several prominent NFT collections on the Ethereum blockchain. The dataset is derived from the methodology presented in the paper "Beyond the Surface: Advanced Wash Trading Detection in Decentralized NFT Markets".
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We introduce Vietnamese Multi-Dialect (ViMD) dataset, a novel comprehensive dataset capturing the rich diversity of 63 provincial dialects spoken across Vietnam. Our dataset comprises 102.56 hours of audio, consisting of approximately 19,000 utterances, and the associated transcripts contain over 1.2 million words.