Note: updated version of this dataset contains cleaned up typing data where unused (and incorrect) derivative columns were removed. You can derive these from the raw data yourself. ========= This dataset contains motion capture, keylog, eye tracking, and video data of 30 participants, transcribing regular sentences. It is part of the following publication: Anna Maria Feit, Daryl Weir, Antti Oulasvirta. 2016.How We Type: Movement Strategies and Performance in Everyday Typing.In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 4262-4273 The paper revisits the present understanding of typing, which originates mostly from studies of trained typists using the tenfinger touch typing system. Our goal was to characterise the majority of present-day users who are untrained and employ diverse, self-taught techniques. In a transcription task, we compared self-taught typists and those that took a touch typing course. We reported several differences in performance, gaze deployment and movement strategies. The most surprising finding was that self-taught typists can achieve performance levels comparable with touch typists, even when using fewer fingers. Motion capture data exposed 3 predictors of high performance: 1) unambiguous mapping (a letter is consistently pressed by the same finger), 2) active preparation of upcoming keystrokes, and 3) minimal global hand motion. The dataset is free for non-commercial use. Please cite the above work. Note that participants wrote in either Finnish or English.