My Art/Science practice orbits around the duality "music as data, data as music": translation of complex events in sonic material, and object for musical poiesis. On one hand, data are raw elements for a compositional process that transcends the materiality of the original information in a post-sonification praxis: the data stream is open for elaboration as principal element of a data-driven compositional environment. I use these data as a sculptor would use clay (the raw data) to mold any object or create any design (the music). On the other, I generalize the concept of musical spaces as big data networks and derive functional principles of compositional design by the direct analysis of the network topology. With the materialssoundmusic project I want to exploit the dual aspect of data in music, the analytic and the artistic, and provide open source software tools for a novel approach to the dialogue between perceptualization of data and artistic creation. To this purpose I have developed a new computer-aided data-driven composition environment for the sonification, remix and artistic reinterpretation of large data spaces as expressive media: MUSICNTWRK. MUSICNTWRK is a python library for pitch class set and rhythmic sequences classification and manipulation, the generation of networks in generalized music and sound spaces, deep learning algorithms for timbre recognition, and the sonification of arbitrary data. The software is freely available under GPL 3.0 and can be downloaded at www.musicntwrk.com.