19,997 machine learning datasets
19,997 dataset results
A comprehensive Turkish dataset for question-answering tasks in medical domain
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The datasets used and analysed from the glucose clamp study are available in this Excel file. They include pseudonymised information on the participants, somatometric data, biomarkers of lipid metabolism and parameters of insulin-glucose homeostasis, i.e. concentrations of insulin, glucose and c-peptide as well as data from glucose-clamp experiments, HOMA, SPINA Carb parameters (SPINA-GBeta and SPINA-GR), Matsuda index, insulinogenic index, disposition index and McAuley index.
The datasets used and analysed from the glucose clamp study are available in this DIF file. They include pseudonymised information on the participants, somatometric data, biomarkers of lipid metabolism and parameters of insulin-glucose homeostasis, i.e. concentrations of insulin, glucose and c-peptide as well as data from glucose-clamp experiments, HOMA, SPINA Carb parameters (SPINA-GBeta and SPINA-GR), Matsuda index, insulinogenic index, disposition index and McAuley index.
Code and Data for Replication of "Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent"
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The EQN framework is a micro-emotion annotation and detection system that realizes the automatic micro-emotion annotation of text with energy level scores for the first time. The text emotion datasets it annotates are no longer simple single-label or multi-label, but macro-emotions and micro-emotions with continuous values of emotion intensity. The labeling of emotion datasets has changed from discrete to continuous. It plays an important role in the subtle research of emotions in fields such as emotional computing, human-computer alignment, humanoid robots, and psychology.
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This dataset is being used to evaluate PerfSim accuracy and speed against a real deployment in a Kubernetes cluster based on sfc-stress workloads.
We introduce a dataset consisting of 1314 samples, including users’ tweets and bios. The user’s job title is found using Wikipedia crawling. The challenge of multiple job titles per user is handled using a semantic word embedding and clustering method. Then, a job prediction method is introduced based on a deep neural network and TF-IDF word embedding. We also use hashtags and emojis in the tweets for job prediction. Results show that the job title of users in Twitter could be well predicted with 54% accuracy in nine categories.
Sentiment analysis is pivotal in Natural Language Processing for understanding opinions and emotions in text. While advancements in Sentiment analysis for English are notable, Arabic Sentiment Analysis (ASA) lags, despite the growing Arabic online user base. Existing ASA benchmarks are often outdated and lack comprehensive evaluation capabilities for state-of-the-art models. To bridge this gap, we introduce ArSen, a meticulously annotated COVID-19-themed Arabic dataset, and the IFDHN, a novel model incorporating fuzzy logic for enhanced sentiment classification. ArSen provides a contemporary, robust benchmark, and IFDHN achieves state-of-the-art performance on ASA tasks. Comprehensive evaluations demonstrate the efficacy of IFDHN using the ArSen dataset, highlighting future research directions in ASA.
Dataset to reproduce results for the paper Detecting CSV file dialects by table uniformity measurement and data type inference, DOI ds240062.
SoliDiffy Differencing Contract Pairs and Edit Scripts Dataset The project creates and maintains two main datasets to assist with research and evaluation of Solidity smart contract differencing:
GPTKB is a large general-domain knowledge base (KB) constructed entirely from a large language model (LLM). It demonstrates the feasibility of large-scale KB construction from LLMs, while highlighting specific challenges arising around entity recognition, entity and property canonicalization, and taxonomy construction.
The notebooks folder contains basic analysis of the organizational affiliation data for the contributors per open source project.
A large collection of human-written natural language questions and their corresponding SPARQL queries over federated bioinformatics knowledge graphs (KGs) collected for several years across different research groups at the SIB Swiss Institute of Bioinformatics. The collection comprises more than 1000 example questions and queries, including 65 federated queries. We propose a methodology to uniformly represent the examples with minimal metadata, based on existing standards. Furthermore, we introduce an extensive set of open-source applications, including query graph visualizations and smart query editors, easily reusable by KG maintainers who adopt the proposed methodology.
IM-SportingBehaviors Dataset Dataset Overview The IM-SportingBehaviors dataset, developed by researchers at Air University Pakistan, provides detailed motion data from participants engaged in various sports activities. This dataset captures human movement through triaxial accelerometers attached to multiple parts of the body, specifically the knee, wrist, and below-neck areas. The dataset includes motion data from six sports: cycling, badminton, skipping, basketball, football, and table tennis, with participants comprising both professional and amateur athletes aged between 20 and 30 years, weighing between 60 and 100 kilograms.
This study collected data from the major social media platform Weibo, screening 11,338 valid users from over 50,000 individuals with diverse MBTI personality labels and acquiring 566,900 posts along with the user MBTI personality tags. Using the EQN method, we compiled a multi-label Chinese affective computing dataset that integrates the same user's personality traits with six emotions and micro-emotions, each annotated with intensity levels. This dataset is designed to advance machine recognition of complex human emotions and provide data support for research in psychology, education, marketing, finance, and politics.
The first and large scale dataset to solve multi-object tracking and Re-identification problem with wild animals using UAVs.
MovieNet-TeViS is a synopsis-storyboard pair dataset to facilitate the Text synopsis to Video Storyboard (TeViS). In the TeViS task, we aim to retrieve an ordered sequence of images from large-scale movie database as video storyboard to visualize an input text synopsis.