Galaxy Zoo: Morphological Classification and Citizen Science

TL;DR

Galaxy Zoo and Zooniverse leverage citizen science and human pattern recognition to classify galaxy morphologies, producing valuable scientific insights and demonstrating the effectiveness of community-based research methods.

Contribution

This paper provides an overview of Galaxy Zoo and Zooniverse, highlighting their methodology, scientific achievements, and lessons learned for future citizen science projects.

Findings

Successful application of citizen science in galaxy classification

Generation of significant scientific results from public contributions

Insights into developing and managing community-based research projects

Abstract

We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.