Endurance running is a popular activity due to its accessibility. However, participation is sometimes prevented by individuals experiencing respiratory problems. Monitoring breathing with body area networks can tackle these issues by tracking respiration during exercise and providing immediate, guiding feedback. Common breathing guidance systems rely on observational data from past breath cycles and consequently inherit disruptively lagging guidance interventions if breathing pattern suddenly change.

This dataset is constructed in a study that addresses the gap between text summarization and content readability for diverse Turkish-speaking audiences. It contains paired original texts and corresponding summaries optimized for different readability levels using the YOD (Yeni Okunabilirlik Düzeyi) formula.

This dataset provides a comprehensive collection of various resources, including the results from Computational Fluid Dynamics (CFD) simulations, the associated CFD processing code, and the dataset along with the source code used for training Convolutional Neural Networks (CNNs). Additionally, it includes data generated by genetic algorithms and the corresponding source code for implementing these algorithms.

RetinaX dataset is built by selectively combining four publicly available datasets: the STARE dataset, ARIA dataset, RFMiD dataset, and RFMiD 2.0 dataset. It contains a total of 2,514 images and 24 distinct labels, covering nearly all common and rare retinal diseases.