The increasing use of automated scientific-data-collection instruments has led to an explosion in the amount of scientific data collected, challenging the ability of scientists to analyze them. We propose to develop a citizen-science system that enables non-expert volunteers to contribute to analyses of large volumes of data. Volunteers have less background knowledge than experts about the purpose, context, content, provenance and processes associated with these data. We hypothesize that a system that provides such background knowledge will enable non-experts to find and make sense of data. Our research plan will test a number of system interventions to increase the capabilities of the volunteers. The project will be set in the PI’s ongoing Gravity Spy project. In this project, volunteers classify noise events (“glitches”) produced by the Laser Interferometer Gravitational-wave Observatory (LIGO). Along with glitches observed in the main (“strain”) channel, the detectors record around 400K auxiliary channels of data that may provide information about the origins of the glitch. The goal of the project is two-fold: 1) to test our hypotheses about the kind of additional information needed to enable non-experts to productively navigate a large dynamic dataset to find related information and 2) to develop techniques to manage and efficiently process the data to identify relationships between glitch morphology in the strain and auxiliary channels.
|Effective start/end date||10/1/21 → 9/30/24|
- National Science Foundation (IIS-2107334)
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