Music Information Retrieval (MIR) develop rapidly these years, Automatic Music Transcription (AMT) and Expressive Analysis is increasingly gaining momentum on both Western and non-eurogenic music. However, the annotated datasets for non-eurogenic instruments remain scarce on 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 barely analyzed in MIR community, pipa has lots of distinctive national and local characteristics mainly including 4 classes of sophisticated playing techniques greatly enhancing the music expressiveness. Our work aims to systematically clarify an efficient procedure for the multi-modal pipa dataset creation which consists of audio, musical notations and multi-view videos of Chinese traditional solos. The use of 4-track string vibration signals captured by optical sensors paves a path to high quality annotation. A Transcription and Expressiveness Annotation System (TEAS) is transparently implemented to ensure the scalability of dataset. Finally, a series of existent and new MIR tasks enabled by this dataset are enumerated to explore in the future.