Datasets of the LeQua 2024 Learning to Quantify Data Challenge read more of Learning to quantify: LeQua 2024 dataset

This data set comprises a labelled training set used in the experimentation of the paper "Binary Quantification and Dataset Shift: An Experimental Investigation". read more of Product Reviews Dataset

This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "Ordinal Quantification Through Regularization", which we have published at ECML-PKDD 2022. read more of Product Reviews for Ordinal Quantification

The aim of the LeQua 2022 dataset is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual ... read more of Learning to quantify: LeQua 2022 datasets

The Aristotle University of Thessaloniki (hereinafter, AUTH) created the dataset ‘3D-Flood’, within the context of the project TEMA that was funded by the European Commission-European Union [Grant Agreement number: 101093003; start date: 01/12/2022; end d... read more of 3D-Flood Database

AIS data collected by the University of Piraeus' AIS receiver read more of The Piraeus AIS Dataset for Large-scale Maritime Data Analytics

The Aristotle University of Thessaloniki (hereinafter, AUTH) created the following dataset, entitled ‘Flood Master Database’, within the context of the project TEMA that was funded by the European Commission-European Union [Grant Agreement number: 1010930... read more of Flood Master Database

A large-scale, diverse posture-based distracted diver dataset, with more than 470K images taken by 4 cameras observing 100 drivers over 79 hours from 5 vehicles. read more of 100-Driver: A Large-scale, Diverse Dataset for Distracted Driver Classification

The Aristotle University of Thessaloniki (hereinafter, AUTH) created the following dataset, entitled ‘Blaze’, within the context of the project TEMA that was funded by the European Commission-European Union [Grant Agreement number: 101093003; start date: ... read more of Blaze Fire Classification – Segmentation Dataset

A Dataset with Energy Efficiency Measures available for renovations provided by REA (Riga Energy Agency) read more of Latvian Residential Buildings Energy Efficiency Measures Dataset