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Syracuse University School of Information Studies


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Big Data Factories book has been published!

The Big Data Factories book has been published by Springer. The book discusses how to use the increasing flood of data created as a byproduct of online behaviour as a source for research. I have a chapter entitled "Levels of Trace Data for Social and Behavioural Science Research".

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Looking for PhD students!

I am interested in finding new PhD students interested in work and technology, especially work and intelligent machines. You can find more information on the program here. Please get in touch if you have questions or are thinking about applying.

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Researchers turn to “citizen scientists” for help identifying gravitational waves

University of Alabama has a nice article about Gravity Spy.

Other popular press coverage: Citizen scientists join search for gravitational waves, Symmetry Magazine.

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National Consortium for Data Science webinar: Humans, Machines, and the Future of Citizen Science:

I recently gave a webinar for the National Consortium for Data Science on Humans, Machines, and the Future of Citizen Science.

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Gravity Spy is live!

The Gravity Spy system has gone live. A few stories about the launch:

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Recent citizen science publications


Smith, A. O., Bullard, J., Jackson, C. B., Østerlund, C., & Crowston, K. (2026). Folksonomies in crowdsourcing platforms: A cross-platform comparison. Computer-Supported Cooperative Work, 35(2). https://doi.org/10.1007/s10606-026-09537-5
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Aleman, B., & Davis, D. (2026). OmegaNeuron: Applying GravitySpy Similarity Methods to the Search for LIGO Glitch Witnesses. https://arxiv.org/abs/2602.23460
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Østerlund, C., & Jackson, C. (2026). Trace Methods: Probing the Apparatus. In B. Wurm & J. Mendling (Eds.), Digital Trace Data Research in Information Systems (pp. 81–105). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-05497-5_5
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Wu, Y., Zevin, M., Berry, C., Crowston, K., Østerlund, C., Doctor, Z., Banagiri, S., Jackson, C., Kalogera, V., & Katsaggelos, A. (2025). Advancing Glitch Classification in Gravity Spy: Multi-view Fusion with Attention-based Machine Learning for Advanced LIGO’s Fourth Observing Run. Classical and Quantum Gravity. https://doi.org/10.1088/1361-6382/adf58b
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Mackenzie, E., Berry, C., Niklasch, G., Téglás, B., Unsworth, C., Crowston, K., Davis, D., & Katsaggelos, A. (2025). Hunting for new glitches in LIGO data using community science. arXiv. https://doi.org/10.48550/arXiv.2508.13923
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