From Green Peas to STEVE: Citizen Science Engagement in Space Science

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The past two decades has seen a tremendous rise in citizen science and crowdsourcing techniques as a means to carry out ground-breaking research while at the same time engage the general public in the wonders of space science. This article reviews some of the recent advances made in this realm as well as lessons learned from the unique perspective of the author’s role as a cofounder of the Zooniverse citizen science platform and practicing astrophysics researcher. I briefly describe the factors that led to the recent rise of citizen science including the formation of governance bodies at national and international levels, and the adoption by Federal Agencies within the United States government. I address concerns raised by research colleagues on the validity of citizen science as a research methodology, and then describe several key metrics for the success of citizen science including the link between data quality and publications, and the critical role that motivation and engagement of volunteer participants play in project success. I use the Green Pea galaxies discovered by Galaxy Zoo volunteers and an aurora-like phenomenon known as STEVE discovered by Aurorasaurus volunteers as examples of how, with the right tools and support, non-professional volunteers can make key contributions to space science. I then describe the role that machine learning can play when judiciously teamed with citizen scientists to tackle the ever-growing challenge of big data and close with some reflections on what it takes to support and manage a large platform like the Zooniverse.

Original languageEnglish (US)
Title of host publicationSpace Science and Public Engagement
Subtitle of host publication21st Century Perspectives and Opportunities
PublisherElsevier
Pages185-219
Number of pages35
ISBN (Electronic)9780128173909
DOIs
StatePublished - Jan 1 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.

Keywords

  • Citizen Science
  • Crowdsourcing
  • Machine learning
  • Volunteer engagement

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