Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo

TL;DR

Galaxy Zoo exemplifies the intersection of citizen science, peer production, and Big Data science, demonstrating how open participation and data-intensive methods advance modern astronomy within a knowledge commons framework.

Contribution

This paper applies the knowledge commons framework to analyze Galaxy Zoo as a case of integrating citizen science, peer production, and Big Data in scientific research.

Findings

Galaxy Zoo successfully engages volunteers in large-scale data analysis.

It demonstrates effective governance of digital scientific commons.

The project exemplifies the integration of citizen participation with data-intensive science.

Abstract

The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis via the Internet. In the second place Galaxy Zoo is a highly successful example of peer production, sometimes known colloquially as crowdsourcing, by which data are gathered, supplied, and/or analyzed by very large numbers of anonymous and pseudonymous contributors to an enterprise that is centrally coordinated or managed. In the third place Galaxy Zoo is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies. This chapter synthesizes these three perspectives on Galaxy Zoo via the knowledge commons framework.