Website: http://aurorasaurus.org/
NSF Grant: 1344296
Investigators
- Elizabeth MacDonald, National Aeronautics and Space Administration (Principal Investigator)
- Michelle Hall, Science Education Solutions (Co-Principal Investigator)
- Andrea Tapia, Penn State University (Co-Principal Investigator)
Description
Aurorasaurus was a a two-year inter-disciplinary project that pursued tightly coupled goals within human centered computing, citizen science, and space weather research. The aurora borealis of the northern hemisphere and its twin, the aurora australis of the southern hemisphere, are among the most beautiful and awe-inspiring of natural phenomena. As the aurora is a visible manifestation of space weather, observations of aurora are potentially a means of forecasting its catastrophic extremes. We capitalized on public curiosity by creating an instrument to bridge a gap in realtime forecasting of the Aurora Borealis and Aurora Australis. This system allows interested citizens to collect, analyze, and redistribute the location of the Aurora around the world in real time.
We combined expertise in space weather science, human-computer interface design, and informal science education to realize an interconnected set of goals.
For space science, Aurorasaurus provided a new source of data for auroral observations. Additionally, these data allow for real-time forecast-model verification. The creation of this Early-Warning System inspired citizen science product has transformed our understanding of how crowd-sourced knowledge and labor come together for scientific discovery for the field of human-centered computing. The problem of making social media actionable in real time has been of particular interest within human-centered computing and Aurorasaurus has been a testing ground for new techniques and frameworks. Finally, the education approach used the beauty of the Aurora as a means to connect to audiences via social media. The observation of interested citizen sciences interacting with educational tools has provided an array of possibilities to build on.
Overall, this low-cost system has improved forecasting of geomagnetic storms. In addition, it has resulted in new approaches to engaging non-professional science enthusiasts that have and will continue to have value for a wealth of other ongoing citizen science programs.
Publications
Case, N., MacDonald, E., McCloat, S., Lalone, N., & Tapia, A. (2016). Determining the accuracy of crowdsourced tweet verification for auroral research. Citizen Science: Theory and Practice, 1(2).
LaLone, N., Tapia, A., MacDonald, E., Case, N., Hall, M., Clayton, J., & Heavner, M. J. (2015, February). Harnessing Twitter and crowdsourcing to augment aurora forecasting. In Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (pp. 9-12). ACM.
Case, N. A., MacDonald, E. A., Heavner, M., Tapia, A. H., & Lalone, N. (2015). Mapping auroral activity with Twitter. Geophysical Research Letters, 42(10), 3668-3676.
Tapia, Andrea H., et al. “AURORASAURUS: Citizen Science, Early Warning Systems and Space Weather.” Second AAAI Conference on Human Computation and Crowdsourcing. 2014.
Tapia, Andrea H., et al. “Crowdsourcing rare events: Using curiosity to draw participants into science and early warning systems.” Proceedings of the 11th ISCRAM (2014). (Best Paper Nominee)
Case, N., MacDonald, E., & Viereck, R. (2015, December). A comparison of modeled auroral boundaries with observations from citizen scientists. In AGU Fall Meeting 2015.
MacDonald, E., Hall, M., & Tapia, A. (2013, December). Aurorasaurus: Citizen Scientists Experiencing Extremes of Space Weather. In AGU Fall Meeting Abstracts.