Jillian C. Wallis

Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.

Data repositories rely on the deposit of materials from the communities they serve, forming a chain of stakeholders from the data creator to the repository and data user. Top‐down policies that describe the responsibilities of the depositing scientists and other stakeholders are drafted accordingly. But we see very little deposit of scientific data beyond the Big Sciences or communities for whom deposit is required by publications. As part of an ongoing data practices study, we asked scientific researchers about who would be responsible for the data collected. It is clear that researchers are not talking about who is responsible for the data. The results presented here are meant to demonstrate the need for further research into what it means to be responsible for research data and how this responsibility is delegated to members of a research team.

Collaborative research is on the rise, and presents difficult information challenges that must be overcome to make this mode of research effective. For instance distributed research is regularly plagued by the “distance matters” problem just as multi‐disciplinary research is often plagued by misaligned terminology and assumptions. These problems can potentially be alleviated using information channels and mediation in new ways, as well as balancing technology with policy and incentives. In this panel we engage with information challenges in collaborative sciences from a variety of perspectives, with the hope that a rich discussion will emerge regarding these challenges.

Scientists and engineers working with embedded networked sensing systems in the environmental sciences are acquiring data at unprecedented rates. Scientific data do not emerge from a vacuum. There is considerable contextual information that surrounds the process of data acquisition that is critical to interpret and analyze data. Current techniques for data sharing involve considerable manual effort to prepare, describe, and transfer this contextual information along with the data itself. This paper reports on a study of the UCLA‐based Center for Embedded Networked Sensing (CENS), an interdisciplinary NSF research center that supports collaborations to develop and implement innovative wireless sensor networks. We report here on the development of the CENS Deployment Center, a database for CENS deployment information. The goals of the CENSDC are to facilitate better deployment organization, and to provide a central location for key information that describes the context of data capture. We also describe our plans within CENS to use a tri‐partite approach to capture and share sensor data resources using technical recommendations currently being developed within the context of the Open Archive Initiative for Object Reuse and Exchange (OAI‐ORE).