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Our goal is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multi-dimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough, based on Data Science as a pivot for interdisciplinary dialog, may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand the relationship between physical phenomena and user behaviors. This multidisciplinary project involves computer scientists, social and urban scientists, and thermal scientists. The aim of this paper is to give a synthetic presentation of our methodology, and an overview of our main results

Data Science Approach for a Cross-disciplinary Understanding of Urban Phenomena: Application to Energy Efficiency of Buildings