Knitting a fabric of sensor data and literature. in Information Processing in Sensor Networks

Alberto Pepe
Christine L. Borgman, University of California, Los Angeles
Jillian C. Wallis
Matthew S. Mayernik

Abstract

With applications ranging from ecological to urban deployments, the Center for Embedded Networked Sensing (CENS) is a dynamic research environment that fully encapsulates the rapidly advancing interdisciplinary and variegated nature of modern embedded sensing. As the amount of data generated at CENS become more significant, it is important to identify the challenges of the current data management practices and provide efficient solutions to improve data production, curation and sharing. We have identified three major digital resources - deployment information, sensor data and scientific publications - that are currently organized and managed as separate entities in disjointed data repositories. Despite the heterogeneity of content, we believe that these resources are all building blocks of the same scholarly production chain. In this paper, we introduce the basic internal requirements that need to be achieved in order to make CENS data a testbed for the evaluation of the OAI-ORE model: a framework that would allow distributed digital resources to inter-operate beyond the barriers of their hosting repositories. We believe that “knitting” sensor data resources has the potential to both enhance scientists’ data practices - enabling cross-repository data reuse and sharing, and leverage generation of scholarly output - by preserving the value chains of the scholarly communication process.