IEEE Internet Computing
Data at work: supporting sharing in science and engineering
GROUP '03 Proceedings of the 2003 international ACM SIGGROUP conference on Supporting group work
Drowning in data: digital library architecture to support scientific use of embedded sensor networks
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Not by metadata alone: the use of diverse forms of knowledge to locate data for reuse
International Journal on Digital Libraries
Scholarship in the Digital Age: Information, Infrastructure, and the Internet
Scholarship in the Digital Age: Information, Infrastructure, and the Internet
The Long-Term Ecological Research community metadata standardisation project: a progress report
International Journal of Metadata, Semantics and Ontologies
A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming
A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming
The conundrum of sharing research data
Journal of the American Society for Information Science and Technology
Institutional structures for research data and metadata curation
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Information and Organization
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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.