CCC Workshop Report Charts Future of Computational Citizen Science
Human-machine teams could solve pressing global problems through citizen science.
The Computing Community Consortium (CCC) convened a visioning workshop on April 8-9, 2025, in Washington, D.C., bringing together multidisciplinary experts to chart a path for the convergence of computational and citizen science. The resulting report, now published on arXiv, outlines a research agenda for how humans and machines can team up to tackle the world's most pressing scientific challenges. Citizen science already delivers measurable economic value—millions of dollars in volunteer labor—while extending government agency capacity in areas like disaster management, public health, water resources, energy, and workforce development.
The workshop report emphasizes that 21st-century scientific infrastructure requirements for citizen science mirror those for computational science more broadly. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world testbed for robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. Key findings from the workshop point to future research directions including human-AI collaboration frameworks, scalable cyberinfrastructure, and ethical data governance. The report concludes with actionable recommendations for funding agencies, researchers, and policymakers to accelerate this convergence.
- The April 2025 CCC workshop brought together experts across disciplines to develop a shared research agenda for computational and citizen science convergence.
- Citizen science generates millions of dollars in volunteer labor value and extends government capacity in disaster management, public health, and energy.
- The infrastructure needs of citizen science—security, privacy, adaptability, transparency—mirror those of computational science, making it a demanding real-world use case.
Why It Matters
Merging citizen science with computational methods could unlock massive human-AI problem-solving capacity for global challenges.