Music information retrieval (MIR) is developing these years rapidly. As the fundamental MIR tasks, automatic music transcription (AMT) and expressive analysis (EA) are gaining momentum in both Western and non-European music. However, the annotated datasets for non-Eurogenic instruments remain scarce in terms of quantity and feature diversity so that general evaluations and data-driven models on various tasks cannot be well explored. As one of the most popular traditional plucked string instruments in Asia, which is barely studied in the MIR community, pipa has lots of distinctive national and local characteristics including the fake nails, intrinsic pitch shift, rubato, as well as a high diversity of sophisticated playing techniques that greatly enhance the music expressiveness. Our work aims to systematically clarify a creation procedure of a pipa dataset with audio, musical notation and multiview video modalities for traditional Chinese solos. The use of 4-track string vibration signals captured by optical sensors paves a path for high quality annotations. Furthermore, a transcription and Expressiveness Annotation System (TEAS) was transparently implemented to ensure the scalability of dataset. Three expressive analysis approaches in this system were newly proposed and evaluated in this paper. Finally, two AMT models were investigated and a series of the existing and emerging MIR tasks enabled by this dataset were enumerated for the future exploration.INDEX TERMS Multimodal dataset creation,automatic music transcription, playing technique analysis, optical sensoring, annotation system.