We present a work-in-progress project named DAMUS, a collaborative modular and data-driven system that aims to algorithmically support the composers and choreographers to generate original and diverse development for their creative variations and continuities. The project name DAMUS means “we offer” in Latin. Meanwhile, it is also a combined ter- minology with DA, the dance module, and MUS, the mu- sic module. Conventional dance-music collaborations regu- larly take a significant amount of time for collaborators to adapt to one other’s creative languages; occasionally, collab- orations can become overly exclusive, resulting in collabora- tions between only certain artistic groups. Nowadays, fast- paced productions and more inclusive creative collaboration environments necessitate an efficient solution that can pre- serve a significant amount of artistic authenticity while also facilitating rapid “brainstorming.” DAMUS, the compound collaborative authoring system, aims to build a collaborative foundation for choreographers and composers through algo- rithms. Using DAMUS will reduce time consumption on communication and motivate dynamic expressions by treating their complete or scattered creative ideas as preliminary units. We leverage machine learning, evolutionary computation, and creative constraints to produce dance and music variations ei- ther for a single user or for multiple users, where they can interact with each other and express themselves dynamically. We will present details of our system design, data collection, the DAMUS components (the dance module and the music module), as well as the underlying logic for each. Further- more, we would like to share our preliminary creative out- puts through case studies involving three distinct dance gen- res: ballet, modern dance, and Chinese Tang Dynasty dance.