Abstract: In this paper we present Virtuoso Strings, a dataset for timing analysis and automatic music transcription (AMT) tasks requiring note onset annotations. This dataset takes advantage of real-world recordings in multitrack format and is curated as part of the Augmented Reality Music Ensemble (ARME) project which investigates musician synchronisation and multimodal music analysis. The dataset is comprised of repeated recordings of quartet, trio, duet and solo ensemble performances with different temporal expressions and leadership role assignments, providing new possibilities for developing and evaluating AMT models with respect to diverse musical performance styles. To reduce the cost of the labour-intensive manual annotation, a semi-automatic method was utilised for both annotation and quality control. The presented dataset consists of 746 tracks with a total of 68,728 onsets. Every track includes onset annotations for a single string instrument, enabling the creation of audio files with different combinations of instruments to be used in the AMT evaluation process.