In a new workshop report published by the Computing Community Consortium (CCC), Grand Challenges for the Convergence of Computational and Citizen Science Research, dozens of experts across disciplines came together to examine the ways in which advanced computing and citizen science mutually enrich each other, fundamentally altering our potential for advancement in numerous scientific fields for the better. 

From users mapping biodiversity on iNaturalist to analyzing protein folding configurations to advance drug discovery on Foldit, we have seen the value of engaging the public in scientific research projects. This report also presents the opportunity of citizen science and computing research, together, to maximize the potential of tools with broad applications like artificial intelligence (AI). On a large scale, such convergence is capable of directly addressing some of our most pressing national challenges and priorities.

Supporting National Interests

The impact of the convergence of citizen science with advanced computing aligns directly with a wide array of national priorities:

• It would enhance national competitiveness due to the increased productivity, agility, and reach of scientific disciplines across the board.

• The extensive volunteer efforts involved in convergence would likewise increase economic value and increase government efficiency by reducing the duplication of efforts, streamlining workflows, and lowering the cost of data collection and analysis across state and federal agencies.

• Citizen science offers incredible workforce skills development for those who participate, enhancing scientific literacy among the population as well as serving as accessible entry-points for STEM careers.

• Large-scale convergence requires investment in robust infrastructure that may help address local, state, federal, or even international scientific challenges, including security and privacy concerns.

• An increase in convergence harnesses public data in a way that enhances the credibility of scientific research, accessing perspectives and local knowledge that traditional scientific research methods may overlook as well as emphasizing appropriate data sharing and reuse.

• Participation in citizen science establishes a sense of civic identity and public agency by offering participants a feeling of ownership over their contributions and the eventual results and innovations they enable.
  

• Data collected by citizen scientists is especially valuable in improving natural disaster response, where individuals can provide fast and specific data points about the impacts of events like hurricanes, earthquakes, or tornadoes.

Developing Critical Infrastructure

Large-scale convergence requires investment and coordination to reach its full potential. Workshop attendees crafted a strategic roadmap for the sustained platforms, governance systems, and physical/cyber architecture required to support scalable, trustworthy, and nationwide convergence efforts:

• Encourage and incentivize cross-agency collaboration: Break down silos by integrating citizen science and crowdsourcing with government agency AI, cloud, and technology strategies.

• Create permanent federal funding streams for convergence infrastructure: Ensure stable funding for long-term platform and infrastructure maintenance and evolution.

• Build interoperable and AI-ready data infrastructure for participatory sciences: Define national standards (FAIR data, APIs, metadata) for data exchange across platforms and projects.

• Develop scalable provenance frameworks for participatory AI systems: Fund research on lightweight, privacy-aware provenance capture that spans human actions, AI model evolution, and distributed execution, enabling reproducibility, auditing, and long-term reuse at the national scale.

• Invest in privacy and security-preserving data frameworks: Implement and evaluate mechanisms (e.g., federated learning, data trusts, open standards) that are core to participatory science infrastructure, and formalize the necessary governance and legal structures for secure data use.

• Establish national guidance for explainable, transparent, and trustworthy AI in citizen science: Develop research-backed guidelines for interpretable outputs, community consent, and auditability.

• Develop next-generation participatory AI governance: Co-create frameworks that give participatory science communities real agency over AI deployment decisions, data use, and model evolution.

• Launch a National Citizen Science & AI Convergence Hub: Establish a central, virtual hub to share tools, standards, best practices, case studies, and training materials, thus reducing duplication and accelerating adoption.

Read the Full Report

For a full picture of the impact of large-scale convergence, as well as key recommendations across sectors for how to make it a reality, we encourage you to read the Grand Challenges for the Convergence of Computational and Citizen Science Research report below.

Read the Full Report Here

Join the Community Chat

For another way to engage with the findings in this report, join us for an upcoming CCC Community Chat on May 20th, 2026 at 3:30pm. This 45-minute Zoom webinar, hosted by the report’s authors, will dive into key findings, follow up with a series of lightning talks with experts from citizen and computational science, and end with a Q&A. Key speakers to be announced shortly!

Register and submit your questions here.

Tune in to the CCC LinkedIn Showcase Page for updates and more reports like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.