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      Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human–machine systems

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      Proceedings of the National Academy of Sciences

      Proceedings of the National Academy of Sciences

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          Abstract

          Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with research in a direct, authentic fashion and by doing so promotes a better understanding of the processes of science. To take full advantage of the onslaught of data being experienced across the disciplines, it is essential that citizen science platforms leverage the complementary strengths of humans and machines. This Perspectivespiece explores the issues encountered in designing human–machine systems optimized for both efficiency and volunteer engagement, while striving to safeguard and encourage opportunities for serendipitous discovery. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. While these examples make clear the promise of human–machine integration within an online citizen science system, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery. Throughout we investigate the tensions that arise when designing a human–machine system serving the dual goals of carrying out research in the most efficient manner possible while empowering a broad community to authentically engage in this research.

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          Most cited references 51

          • Record: found
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          The future of citizen science: emerging technologies and shifting paradigms

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            The history of public participation in ecological research

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              Is Open Access

              Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey

              In order to understand the formation and subsequent evolution of galaxies one must first distinguish between the two main morphological classes of massive systems: spirals and early-type systems. This paper introduces a project, Galaxy Zoo, which provides visual morphological classifications for nearly one million galaxies, extracted from the Sloan Digital Sky Survey (SDSS). This achievement was made possible by inviting the general public to visually inspect and classify these galaxies via the internet. The project has obtained more than 40,000,000 individual classifications made by ~100,000 participants. We discuss the motivation and strategy for this project, and detail how the classifications were performed and processed. We find that Galaxy Zoo results are consistent with those for subsets of SDSS galaxies classified by professional astronomers, thus demonstrating that our data provides a robust morphological catalogue. Obtaining morphologies by direct visual inspection avoids introducing biases associated with proxies for morphology such as colour, concentration or structual parameters. In addition, this catalogue can be used to directly compare SDSS morphologies with older data sets. The colour--magnitude diagrams for each morphological class are shown, and we illustrate how these distributions differ from those inferred using colour alone as a proxy for morphology.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                February 05 2019
                February 05 2019
                February 05 2019
                February 05 2019
                : 116
                : 6
                : 1902-1909
                Article
                10.1073/pnas.1807190116
                © 2019

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