271 machine learning datasets
271 dataset results
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.
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.
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.
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.
DOI: https://doi.org/10.7910/DVN/O4CRXK
PsOCR is a large-scale synthetic dataset for Optical Character Recognition in low-resource Pashto language.
CSTS: Correlation Structures in Time Series CSTS is a comprehensive synthetic benchmarking dataset designed specifically for evaluating correlation structure discovery in time series data. The dataset systematically models known correlation structures between time series variables and enables rigorous assessment of clustering algorithms and validation methods.
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.
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.
Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades [1], AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL [2], [3], two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combina
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.
This dataset contains demographic and personal health information for individuals, along with the corresponding medical insurance charges billed to them. It is commonly used to build predictive models for insurance costs and to explore relationships between factors such as age, BMI, smoking status, and region on medical expenses.
To check the validity of the ai4st ontology, an adapted, lightweight systematic literature review (SLR) was conducted to analyse related research. This SLR protocol was followed:
๐ฌ๐๐๐๐ ๐๐ ๐๐๐๐๐ ๐๐๐ ๐๐ ๐ ๐ ๐๐ ๐๐๐ ๐๐๐๐. ๐พ๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐ ๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐ ๐๐๐-๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐, ๐๐ ๐๐๐ ๐๐ค๐ฌ ๐ฉ๐๐๐ฉ ๐ฌ๐๐ก๐ก ๐๐๐ช๐จ๐ ๐จ๐ช๐๐๐๐ง ๐ฎ๐ค๐ช ๐จ๐ช๐๐๐๐ง ๐๐ฃ๐ ๐ฌ๐๐ก๐ก ๐๐๐ช๐จ๐ ๐๐ง๐ ๐จ๐ช๐๐๐๐ง ๐ฎ๐ค๐ช. ๐๐-๐ฏ๐๐๐ ๐ญ๐๐๐ ๐ช๐๐๐๐๐๐๐๐๐๐๐: ๐ฐ๐ ๐๐๐ ๐๐๐๐ ๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐ Yes Do you like it, tell me [ [+1-888-829-0881 (time) (time)] ] If you like it โ๐[+1-888-829-0881 (time) (time)] Will I get a refund if I cancel?
Here the dataset described in Hitchhiking Rides Dataset: Two decades of crowd-sourced records on stochastic traveling(https://arxiv.org/abs/2506.21946) is published.
TeleSim is a network-aware hardware-in-the-loop dataset designed to evaluate the performance of telerobotic systems under varying network conditions. It includes 300 fine-manipulation trials using a 6-DoF robotic arm and simulated networks in OMNeT++. Each trial captures:
This paper constructs 7-digit product Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy using microdata from the Annual Survey of Industries (ASI) for the period 2016-2021. We outline the methodology for generating input flows and reconciling registered and unregistered sector data via NPCMS-NIC concordance. The transition from SUTs to IOTs is explained using the Industry Technology Assumption. We apply this framework to analyse the economic impactโspecifically Domestic Value Added (DVA) and employment influenced by production and exports. A case study of India's mobile phone sector reveals significant output growth, import substitution, an increase in exports, a shift in DVA/FVA shares, notable employment growth, with a leaning towards contractual labour, and increased female participation. These tables are valuable for analysing sectoral interdependencies and industrial policy effectiveness in India.
The Reddit COVID Dataset is a dataset of 4.51M Reddit posts and 17.8M comments - all mentions of COVID until 2021-10-25 across the entire Reddit social network. Both were procured with SocialGrep's export feature and released as part of SocialGrep Reddit datasets. The posts are labeled with their subreddit, title, creation date, domain, selftext, and score. The comments are labeled with their subreddit, body, creation date, sentiment (calculated for you using a VADER pipeline), and score.
We present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users.