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  1. null (Ed.)
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    Impact Statement The structure of social and organizational relationships in commercial building workplaces is a key component of work processes. Understanding this structure—typically described as a network of relational ties—can help designers of workspaces and managers of workplaces make decisions that promote the success of organizations. These networks are complex, and as a result, our traditional means of measuring them are time and cost intensive. In this paper, we present a novel method, the Interaction Model, for learning these network structures automatically through sensing data. When we compare the learned network to network data obtained through a survey, we find statistically significant correlation, demonstrating the success of our method. Two key strengths of our proposed method are, first, that it uncovers network patterns quickly, requiring just 10 weeks of data, and, second, that it is interpretable, relying on intuitive opportunities for social interaction. Data-driven inference of the structure of human systems within our built environment will enable the design and operation of engineered built spaces that promote our human-centered objectives. 
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  3. Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting. 
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