Jaime Snyder
University of Washington, WA·Computer Science
Millions of Americans take part in citizen science, helping professional researchers by collecting real data, often by identifying plants and animals in the wild. For their observations to be useful, volunteers need identification guides that are both accurate and easy to use.
- Fields
Computer Science
Social Science
Ecology & Evolution
- Topics
data visualization
information visualization
human-computer interaction
- Grants
- 1 known grant, through Sep 2027
What they work on
- Study how COASST citizen scientists use current identification materials, and probe their values, mental models and expectations for marine mammals, through observation, interviews and focus groups.
- Co-design marine mammal identification elements for a data-driven guidebook with volunteers and program leaders through design workshops, using grounded visualization design.
- Assess how well the new materials support accurate identification of beach-cast marine mammals and fit volunteers' lived experience, through an opt-in appropriation study.
More about the research
This project asks how to design such visual guides so that members of the public collect reliable data while also connecting emotionally and analytically with nature.
Why it matters
Better identification tools could improve the accuracy of biodiversity data collected by volunteers, starting with COASST's beach surveys of marine mammals. They could also bridge scientific and local ways of judging coastal ecosystem health. The design lessons could guide other citizen science projects.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Alexander Kale
University of Chicago, IL·Computer Science·Early-career
Develop a theory that defines visualization design goals as trade-offs among kinds of information loss, made computable through mathematical formalisms derived from example analysis, synthesis and expert interviews.
Paul Parsons
Purdue University, IN·Computer Science·Early-career
Characterize decision making and cognitive errors, such as fixation on early ideas, in how professional data visualization designers frame problems and generate solutions, using design probes, diary studies and design challenges.
Christopher Bryan
Arizona State University Campus Immersion, AZ·Computer Science
Develop a visualization grammar, a way to specify chart designs without low-level programming, that supports privacy constraints and data sanitization actions.
The school
University of Washington: 81 labs on PI Pages, $1.7B a year on research, 694 research doctorates a year.
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