Who’s Got the Data? Interdependencies in Science and Technology Collaborations
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Science and technology always have been interdependent, but never more so than with today’s highly instrumented data collection practices. We report on a long-term study of collaboration between environmental scientists (biology, ecology, marine sciences), computer scientists, and engineering research teams as part of a five-university distributed science and technology research center devoted to embedded networked sensing. The science and technology teams go into the field with mutual interests in gathering scientific data. “Data” are constituted very differently between the research teams. What are data to the science teams may be context to the technology teams, and vice versa. Interdependencies between the teams determine the ability to collect, use, and manage data in both the short and long terms. Four types of data were identified, which are managed separately, limiting both reusability of data and replication of research. Decisions on what data to curate, for whom, for what purposes, and for how long, should consider the interdependencies between scientific and technical processes, the complexities of data collection, and the disposition of the resulting data.
Key wordscyberinfrastructure data curation data practices escience scientific collaboration, scientific software development technology research sensor networks environmental sciences
Research reported here is supported in part by grants from the National Science Foundation (NSF): (1) The Center for Embedded Networked Sensing (CENS) is funded by NSF Cooperative Agreement #CCR-0120778, Deborah L. Estrin, UCLA, Principal Investigator; (2) CENS Education Infrastructure (CENSEI), under which much of this research was conducted, is funded by National Science Foundation grant #ESI-0352572, William A. Sandoval, Principal Investigator and Christine L. Borgman, co-Principal Investigator. (3) Towards a Virtual Organization for Data Cyberinfrastructure, #OCI-0750529, C.L. Borgman, UCLA, PI; G. Bowker, Santa Clara University, Co-PI; Thomas Finholt, University of Michigan, Co-PI; (4) Monitoring, Modeling & Memory: Dynamics of Data and Knowledge in Scientific Cyberinfrastructures: #0827322, P.N. Edwards, UM, PI; Co-PIs C.L. Borgman, UCLA; G. Bowker, SCU; T. Finholt, UM; S. Jackson, UM; D. Ribes, Georgetown; S.L. Star, SCU.
We also are grateful to Microsoft Technical Computing and External Research for gifts in support of this research program. The authors would also like to thank Archer Batcheller, David Fearon, George Mood, Alberto Pepe, Katie Shilton, Elizabeth Rolando, and Laura Wynholds for their thoughtful comments on prior drafts of this paper.
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