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Explore the complexities and simplicities of the research data ecosystem through insights from experts like Chuck Humphrey and Paul Edwards. Learn about the long tail of data, interoperability, and the implications for data infrastructure design. Access resources and readings to understand how to cope with data variety and enhance data integration for impactful research.
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Challenges in the Research Data Ecosystem Implications for DDI Chuck Humphrey University of Alberta October 2012
Our design paradox Complexity • Simplicity
Data infrastructure Solution spaces Paul Edwards, Steven Jackson, Geoffrey Bowker, and Cory Knobel. Understanding Infrastructure: Dynamics, Tensions, and Design (January 2007)
Long tail of data Measured in Petabytes Numbers of Datasets in 100k
Long tail of data Volume Velocity Variety
Data ecosystem Interoperability • Enabling reuse = Impact • Data integration • Data exchange • Coping with variety is a very broad yet characterizing aspect of interoperability. [Pasquale Pagano]
Context for remarks • International Polar Year (IPY), 2007-2012 • DataONE (NSF DataNet) 2010- • OECD Global Science Forum on Data and Research Infrastructure for the Social Sciences, (OECD GSF) 2010-2012 • Global Research Data Infrastructure 2020 (GRDI2020) 2010-2012 • Data Web Forum (DWF) 2012-2012Data Access and Interoperability Task Force (DAITF) 2012-2012Research Data Alliance 2012-
Readings • Edwards, Paul, Steven Jackson, Geoffrey Bowker, and Cory Knobel. Understanding Infrastructure: Dynamics, Tensions, and Design (January 2007) • GRDI2020 Final Roadmap Report. Global Research Data Infrastructures: The Big Data Challenges. http://tinyurl.com/8lcehcy • Pagano, Pasquale . Data Interoperability. http://tinyurl.com/8brx7xw • Parsons, Mark, et.al., “A Conceptual Framework for Managing Very Diverse Data for Complex, Interdisciplinary Science,” Journal of Information Science (2011, pp. 555-569)