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.
Cite
@article{arxiv.1104.5513,
title = {Galaxy Zoo: Morphological Classification and Citizen Science},
author = {Lucy Fortson and Karen Masters and Robert Nichol and Kirk Borne and Edd Edmondson and Chris Lintott and Jordan Raddick and Kevin Schawinski and John Wallin},
journal= {arXiv preprint arXiv:1104.5513},
year = {2011}
}
Comments
11 pages, 1 figure; to be published in Advances in Machine Learning and Data Mining for Astronomy