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Data: Timely identification of event start dates from Twitter
This directory features data that is discussed in the paper: F. Kunneman, A. Hürriyetoglu, N. Oostdijk and A. Van den Bosch (2014), Timely identification of event start dates... -
Dataset: tweets and analyses related to the paper 'The (Un)Predictability of ...
This dataset features all the tweetids and labels that were used to model the language of 24 hashtags, and test the performance on predicting the hashtags in unseen tweets. This... -
Test data for MALA
This repository contains data to test, develop and debug MALA and MALA based runscripts. If you plan to do machine-learning tests ("Does this network implementation work?... -
Dataset Associated with the Study of Robust Drivers of Urban Land Surface Tem...
This dataset is associated with the study of the robust drivers of urban land surface temperature (ULST) dynamics across diverse landscape characters based on an augmented... -
AI4SmallFarms: A Data Set for Crop Field Delineation in Southeast Asian Small...
Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers.... -
Data publication: Reservoir computing on epidemic spreading: A case study on ...
Python scripts and relevant data required for reproducing the figures in the article -
NER-Modell 22 des Projekts Dehmel Digital
This dataset contains two types of resources: Firstly, one Named Entity Recognition model developed in the context of the project "Dehmel digital" for the... -
GLips - German Lipreading Dataset
The German Lipreading dataset consists of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Machine learning for metallurgy: a neural network potential for Al-Mg-Si
High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then... -
A New Kind of Atlas of Zeolite Building Blocks
We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented... -
Machine learning for metallurgy: neural network potentials for Al-Cu-Mg and A...
Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest... -
Novel techniques for characterising graphene nanoplatelets using Raman spectr...
A significant challenge for graphene nanoplatelet (GNP) suppliers is the meaningful characterisation of platelet morphology in an industrial environment. This challenge is... -
Machine learning for metallurgy: a neural network potential for Al-Cu-Mg
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Impact of quantum-chemical metrics on the machine learning prediction of elec...
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
FireNet
FireNet FireNet is an open ML training dataset for visual recognition of fire safety equipment. Our dataset directly links the objects to their respective Uniclass, the... -
Replication Package for "MEG: Multi-objective Ensemble Generation for Defect ...
This is a replication package for the paper "MEG: Multi-objective Ensemble Generation for Defect Prediction", accepted at ESEM 2022. The compressed package is ~42MB and the... -
Learning on-top: regressing the on-top pair density for real-space visualizat...
The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest...
