INSPIRE: Teaming Citizen Science with Machine Learning to Deepen LIGO's View of the Cosmos

Submitted by crowston on Mon, 2015-10-19 16:56

This project (INSPIRE 15-47880) has developed a citizen science system--Gravity Spy ()--to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. LIGO has opened up the window of gravitational wave observations on the Universe.

Previous grants

Submitted by crowston on Mon, 2015-10-19 16:55

Our research on citizen science has been funded by three earlier NSF awards. You can see the descriptions linked below.

Gravity Spy featured in New Scientist

Submitted by crowston on Fri, 2021-11-12 10:47

Gravity Spy was just featured in New Scientist magazine. You can see the story .

Occasional Groups in Crowdsourcing Platforms

Harandi, M. (2021). Occasional Groups in Crowdsourcing Platforms. In School of Information Studies. Syracuse University, Syracuse, NY, USA.

Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research

Submitted by crowston on Mon, 2021-04-05 09:26

The Computing Research Association (CRA) Computing Consortium Community (CCC) has released our Quadrennial Paper research policy brief "“ (with Lea A. Shanley, Lucy Fortson, Tanya Berger-Wolf & Pietro Michelucci).

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

Shifting forms of Engagement: Volunteer Learning in Online Citizen Science

Jackson, C. B., Østerlund, C., Harandi, M., Crowston, K., & Trouille, L.. (2020). Shifting forms of Engagement: Volunteer Learning in Online Citizen Science. Proceedings Of The Acm On Human-Computer Interaction, (CSCW), 36. https://doi.org/10.1145/3392841

Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training

Jackson, C., Østerlund, C., Crowston, K., Harandi, M., Allen, S., Bahaadini, S., et al.. (2020). Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training. Computers In Human Behavior, 105, 106198. https://doi.org/10.1016/j.chb.2019.106198

Linguistic adoption in online citizen science: A structurational perspective

The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning

Harandi, M., Crowston, K., Jackson, C., & Østerlund, C.. (2020). The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719

Pages