Tokenizer, POS Tagger, Lemmatizer and Parser models for 169 treebanks of 93 languages of Universal Depenencies 2.17 Treebanks, created solely using UD 2.17 data (http://hdl.handle.net/11234/1-6036). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_217_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

A regular survey conducted as part of the long-term monitoring of the quality of working life in the Czech Republic, carried out using the research tool SQWLi (https://www.pracovnipohoda.cz/o-projektu-kpz/o-projektu/indikator-sqwli/). Monitoring has been conducted since 2011, usually once a year, allowing the data to be linked into time series.

An updated and expanded version of the dataset was created to investigate the speech and cognitive performance of people with varying degrees of cognitive impairment, primarily dementia. The dataset contains a comprehensive set of data including the results of standardized neuropsychological tests (RBANS, ALBA, POBAV, MASTCZ), speech tasks focused on comprehension, memory, naming, and repetition, and demographic data (age, gender, education). Participants were divided into four groups based on clinical assessment: healthy individuals, healthy individuals with possible mild cognitive impairment, patients with mild cognitive impairment, and patients with dementia. All recordings and examinations were managed as part of routine clinical practice in the neurological outpatient clinic – Memory Clinic at the Department of Neurology at the Faculty Hospital Královské Vinohrady. The dataset containing 371 examinations was divided into a training and test part using stratification by clinical group, age, gender, and level of education to ensure an even distribution of these key characteristics in both parts of the data. Additionally, Manually Engineered Features and Scores were added to the previous version of the dataset. The aim of the dataset is to support the development of methods for automated detection of cognitive disorders based on speech analysis and cognitive performance. The data are suitable for research in the areas of clinical neuropsychology, computational linguistics, and machine learning. The dataset is intended for non-commercial research purposes.