Plans and situated actions: the problem of human-machine communication
Plans and situated actions: the problem of human-machine communication
Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers
Embedded Everywhere: A Research Agenda for Networked Systems of Embedded Computers
Journal of the American Society for Information Science and Technology
Walking the Tightrope: The Balancing Acts of a Large e-Research Project
Computer Supported Cooperative Work
Drowning in data: digital library architecture to support scientific use of embedded sensor networks
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Computer Supported Cooperative Work
The Dynamics of Material Artifacts in Collaborative Research Teams
Computer Supported Cooperative Work
Computer Supported Cooperative Work
Special Theme: Project Management in E-Science: Challenges and Opportunities
Computer Supported Cooperative Work
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Distributed sensing systems for studying scientific phenomena are critical applications of information technologies. By embedding computational intelligence in the environment of study, sensing systems allow researchers to study phenomena at spatial and temporal scales that were previously impossible to achieve. We present an ethnographic study of field research practices among researchers in the Center for Embedded Networked Sensing (CENS), a National Science Foundation Science & Technology Center devoted to developing wireless sensing systems for scientific and social applications. Using the concepts of boundary objects and trading zones, we trace the processes of collaborative research around sensor technology development and adoption within CENS. Over the 10-year lifespan of CENS, sensor technologies, sensor data, field research methods, and statistical expertise each emerged as boundary objects that were understood differently by the science and technology partners. We illustrate how sensing technologies were incompatible with field-based environmental research until researchers "unearthed" their infrastructures, explicitly reintroducing human skill and expertise into the data collection process and developing new collaborative languages that emphasized building dynamic sensing systems that addressed human needs. In collaborating around a dynamic sensing model, the sensing systems became embedded not in the environment of study, but in the practices of the scientists.