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Big Data, Little Data, or No Data? A Social Science Perspective on Data Science [Presentation slides]

  • Author(s): Borgman, Christine L.
  • et al.
Abstract

One person’s signal is another’s noise. Data exist in the eye of the beholder; they are neither products nor commodities. This talk is based on two decades of studying how scientists collect, make, manage, use, reuse, and lose their data. Scientific communities have built large knowledge infrastructures that encompass observatories, telescopes, sensor networks, data archives, technical standards, software tools, institutions, and scholarly societies. These infrastructures evolve over long periods of time; no one is really in charge. Data practices are local, varying from field to field, individual to individual, and country to country. Many of the essential practices necessary for knowledge and data production are invisible, resulting in fragile infrastructures that are difficult to maintain. The ability to share, reuse, and sustain access to scientific data depends on these fragile systems and relationships. Data scientists tend to focus on “big data,” whereas “little data,” “scarce data,” and “no data” are often the norm. The talk is illustrated with empirical examples from astronomy, environmental sciences, sensor networks, biomedicine, and other fields, drawing upon the presenter’s book, Big Data, Little Data, No Data: Scholarship in the Networked World (MIT Press, 2015), and subsequent research.

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