3,148 machine learning datasets
3,148 dataset results
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GenoTEX (Genomics Data Automatic Exploration Benchmark) is a benchmark dataset for the automated analysis of gene expression data to identify disease-associated genes while considering the influence of other biological factors. It provides analysis code and results for solving a wide range of gene-trait association (GTA) analysis problems, encompassing dataset selection, preprocessing, and statistical analysis, in a pipeline that follows computational genomics standards. The benchmark includes expert-curated annotations from bioinformaticians to ensure accuracy and reliability.
This dataset is a patched version of The Taste & Affect Music Database by D. Guedes et al. It is a set of captions that describe 100 musical pieces and associate with them gustatory keywords on the basis of Guedes findings.
Government fiscal policies, particularly annual union budgets, exert significant influence on financial markets. However, real-time analysis of budgetary impacts on sector-specific equity performance remains methodologically challenging and largely unexplored. This study proposes a framework to systematically identify and rank sectors poised to benefit from India's Union Budget announcements. The framework addresses two core tasks: (1) multi-label classification of excerpts from budget transcripts into 81 predefined economic sectors, and (2) performance ranking of these sectors. Leveraging a comprehensive corpus of Indian Union Budget transcripts from 1947 to 2025, we introduce BASIR (Budget-Assisted Sectoral Impact Ranking), an annotated dataset mapping excerpts from budgetary transcripts to sectoral impacts.
In the realm of social media, understanding and predicting post reach is a significant challenge. Our paper presents a Crowd Reaction AssessMent (CReAM) task designed to estimate if a given social media post will receive more reaction than another, a particularly essential task for digital marketers and content writers. We introduce the Crowd Reaction Estimation Dataset (CRED), consisting of pairs of tweets from The White House with comparative measures of retweet count.
Predicting stock market prices following corporate earnings calls remains a significant challenge for investors and researchers alike, requiring innovative approaches that can process diverse information sources. This study investigates the impact of corporate earnings calls on stock prices by introducing a multi-modal predictive model. We leverage textual data from earnings call transcripts, along with images and tables from accompanying presentations, to forecast stock price movements on the trading day immediately following these calls. To facilitate this research, we developed the MiMIC (Multi-Modal Indian Earnings Calls) dataset, encompassing companies representing the Nifty 50, Nifty MidCap 50, and Nifty Small 50 indices. The dataset includes earnings call transcripts, presentations, fundamentals, technical indicators, and subsequent stock prices. We present a multimodal analytical framework that integrates quantitative variables with predictive signals derived from textual and v
We present two multi-modal datasets, one for Main Board IPOs, and the other for Small and Medium Enterprises (SME) IPOs. It consists of various features relating to the company going for IPOs, and other macroeconomic factors. The objective is to estimate the direction and under pricing with respect to opening, high and closing prices of stocks on the IPOlisting day.
Open-source dataset
Collected by cleaning data from daily Xinwen Lianbo transcripts over the past three months and processing it using reverse engineering techniques.
A synthetic dataset from an automobile manufacturer datasource.
The FairTranslate Dataset includes 2,418 sentence pairs, each centered around an occupation, designed to assess gender expression and translation in English-French contexts. Each English sentence appears in three gender variants (male, female, inclusive), allowing for direct counterfactual comparisons. This structure supports fairness evaluations and helps analyze how models handle grammatical gender, inclusive forms, and coreference resolution in translation.
MediBeng Dataset The MediBeng dataset contains synthetic code-switched dialogues in Bengali and English for training models in speech recognition (ASR), text-to-speech (TTS), and machine translation in clinical settings. The dataset is available under the CC-BY-4.0 license.
Filipino CrowS-Pairs and Filipino WinoQueer assess sexist and homophobic biases in language models handling Filipino.
The TVPReid dataset contains 6559 pedestrian videos, each of which is annotated with two text descriptions, for a total of 13118 descriptions. The sentence descriptions are in a natural language style and contain rich details about the pedestrian's appearance, actions, and environmental elements that the pedestrian interacts with. The average sentence length of the TVPReid dataset is 30 words, and the longest sentence contains 83 words.
The StudyAbroadGPT-Dataset is a collection of conversational data focused on university application requirements for various programs, including MBA, MS in Computer Science, Data Science, and Bachelor of Medicine. The dataset includes interactions between humans asking questions about application processes (e.g., "How do I write a strong SOP for MS in Data Science at MIT?") and an assistant providing detailed responses. Covering prestigious institutions such as MIT, Oxford, Cambridge, and Stanford, this dataset serves as a valuable resource for understanding the informational needs of prospective students applying to study abroad.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
100 videos with varying danger levels (on a scale of 0-10) and different scenarios, annotated by 18 human annotators using our annotation pipeline to represent human perception and respective Vision Language model summaries for each of the videos as benchmarks for testing LLMs' danger perceptions.
PreRAID is a structured dataset designed to evaluate the diagnostic capabilities of Large Language Models (LLMs) in Rheumatoid Arthritis (RA) diagnosis. This dataset provides real-world patient data, offering insights into RA prediction and reasoning accuracy.
TF1-EN-3M: Three Million Synthetic Moral Fables for Open Language Models TF1-EN-3M is a large-scale synthetic dataset of 3,000,000 English-language moral fables, generated by instruction-tuned language models with no more than 8 billion parameters. The stories are aimed at child-friendly educational and moral reasoning applications and follow a consistent six-part narrative scaffold: character → trait → setting → conflict → resolution → moral.