Big Data, Little Data, or No Data? Systematic Reviews in an Age of Open Data

Keynote speakers: Christine Borgman

Synoposis:  Medicine, like much of science, is applying “big data” methods to address problems where critical masses of data are available. In areas where evidence is scarce and labor-intensive to acquire, “little data” remains the norm. Societal goals of open data policies, such as those promoted by funding agencies and journals, are to enable communities to mine and combine research data to ask new scientific questions. However, data release is often a complex, contentious, and expensive process. Data reuse is even harder to accomplish due to necessary investments in metadata, provenance documentation, contextual information, software, equipment, and human resources. Until these larger questions of knowledge infrastructures and stewardship are addressed, “no data” remains the norm for many fields. Shifts in policy and practice toward open data may have profound effects on the conduct of systematic reviews such as those pioneered by the Cochrane Collaboration.

This talk will explore considerations such as methods for data release, use, and reuse; credit for data and for reanalyses; data curation, integration, and stewardship; and selection of medical problems worthy of systematic review. Material will be drawn from the presenter’s book, Big Data, Little Data, No Data: Scholarship in the Networked World  (MIT Press, 2015) and subsequent research in biomedicine.