Multimodal Czech online handwriting and cognitive data from children with and without handwriting disabilities

Handwriting is a fundamental skill that significantly influences academic achievement and cognitive development. Despite its importance, open-access datasets combining dynamic handwriting signals with comprehensive cognitive profiles are scarce, particularly for children with handwriting disabilities. We present a multimodal dataset consisting of online handwriting and cognitive assessment data from 276 Czech children (ages 8–12), including 161 individuals diagnosed with dysgraphia. The data acquisition utilized a Wacom Cintiq 16 tablet and a digital pen to record 16 graphomotor and writing tasks, capturing high-resolution x-y coordinates, pressure, azimuth, and pen tilt at an average frequency of 167 Hz. Alongside these raw signals, the dataset includes scores from standardized assessments of cognitive abilities (WJ-IV), visuospatial skills (RCFT), phonological awareness (BACH), and self-reported handwriting proficiency (HPSQ-C). We provide raw data in SVC format and integrated, preprocessed records in JSON and RDS formats, supported by specialized Python and R processing scripts. This resource is intended to facilitate research in graphonomics, machine learning, and educational psychology, providing a benchmark for the objective diagnosis of handwriting disabilities.