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A recently initiated three year project supported by the NSF Human Social Dynamics Program (Interoperability Strategies for Scientific Cyberinfrastructure: A Comparative Study) brings together work with three established research collaborations on large-scale information infrastructures in an attempt to understand the particular configurations of technologies, communities, and organizations. Despite specific configurations of technical commitment, community involvement and organizational structure, all the projects fall under a common rubric of achieving data interoperability.
The historical notion of infrastructure as a background technical service has ramifications in research agendas today that call for interoperability of both community data efforts and scientific partnership. The goal of our ongoing study is to develop a coherent framework of analysis for large scale cyberinfrastructure development, with particular focus on interoperability mediation and achievement. Achieving data interoperability is understood here as a process that aims at information assemblage and sharing in order to enable reuse by various people, across diverging disciplines and across long periods of time.
A turn to interdisciplinary science in the 1950’s was precipitated by the recognition that the complex theoretical and policy questions before scientists could not be answered within a single discipline using traditional scientific information practices. Traditional practices frequently have tied data to a particular investigator or project. Indeed, achieving data interoperability while often viewed as a technical issue – in terms of standards choices –unfolds into a more complex concern in the face of bringing in communities, and the practicalities of human organization or technical enactment.
For this approach, our research team necessarily consists of participants from Science and Technology Studies, Communication, Sociology, Information Management, and Social Informatics. Furthermore, this research depends on working in close relations with scientific community participants over time. Our study draws from grounded theory building making use of ethnographic methods such as interviews, document analysis, participant observation, and community collaboration in order to develop a comparative perspective through cross case analysis.
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