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Matthew S. Mayernik


Publications by Matthew S. Mayernik (bibliography)

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Mayernik, Matthew S., Batcheller, Archer L. and Borgman, Christine L. (2011): How institutional factors influence the creation of scientific metadata. In: Proceedings of the 2011 iConference 2011. pp. 417-425. http://dx.doi.org/10.1145/1940761.1940818

Access to high volumes of digital data offer researchers in all disciplines the possibility to ask new kinds of questions using computational methods. Burgeoning digital data collections, however, challenge established data management and analysis methods. Data management is a multi-pronged institutionalized effort, spanning technology, policies, metadata, and everyday data practices. In this paper, we focus on the last two components: metadata and everyday data practices. We demonstrate how "frictions" arise in creating and managing metadata. These include standardization frictions, temporal frictions, data sharing frictions, and frictions related to the availability of human support. Through an illustration of these frictions in case studies of three large, distributed, collaborative science projects, we show how the degree of metadata institutionalization can strongly influence data management needs and practices.

© All rights reserved Mayernik et al. and/or ACM Press

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Wallis, Jillian C., Mayernik, Matthew S., Borgman, Christine L. and Pepe, Alberto (2010): Digital libraries for scientific data discovery and reuse: from vision to practical reality. In: JCDL10 Proceedings of the 2010 Joint International Conference on Digital Libraries 2010. pp. 333-340. http://doi.acm.org/10.1145/1816123.1816173

Science and technology research is becoming not only more distributed and collaborative, but more highly instrumented. Digital libraries provide a means to capture, manage, and access the data deluge that results from these research enterprises. We have conducted research on data practices and participated in developing data management services for the Center for Embedded Networked Sensing since its founding in 2002 as a National Science Foundation Science and Technology Center. Over the course of eight years, our digital library strategy has shifted dramatically in response to changing technologies, practices, and policies. We report on the development of several DL systems and on the lessons learned, which include the difficulty of anticipating data requirements from nascent technologies, building systems for highly diverse work practices and data types, the need to bind together multiple single-purpose systems, the lack of incentives to manage and share data, the complementary nature of research and development in understanding practices, and sustainability.

© All rights reserved Wallis et al. and/or their publisher

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Borgman, Christine L., Wallis, Jillian C., Mayernik, Matthew S. and Pepe, Alberto (2007): Drowning in data: digital library architecture to support scientific use of embedded sensor networks. In: JCDL07: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries 2007. pp. 269-277. http://doi.acm.org/10.1145/1255175.1255228

New technologies for scientific research are producing a deluge of data that is overwhelming traditional tools for data capture, analysis, storage, and access. We report on a study of scientific practices associated with dynamic deployments of embedded sensor networks to identify requirements for data digital libraries. As part of continuing research on scientific data management, we interviewed 22 participants in 5 environmental science projects to identify data types and uses, stages in their data life cycle, and requirements for digital library architecture. We found that scientists need continuous access to their data from the time that field experiments are designed through final analysis and publication, thus reflecting a broader notion of "digital library." Six categories of requirements are discussed: the ability to obtain and maintain data in the field, verify data in the field, document data context for subsequent interpretation, integrate data from multiple sources, analyze data, and preserve data. Three digital library efforts currently underway within the Center for Embedded Networked Sensing are addressing these requirements, with the goal of a tightly coupled interoperable framework that, in turn, will be a component of cyberinfrastructure for science.

© All rights reserved Borgman et al. and/or ACM Press

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