In March 2026, more than a hundred information and data experts gathered in a converted Christian Science church to confront a problem most Americans never see, but that shapes nearly every public debate we have. The nonprofit Internet Archive convened this national Information Stewardship Forum at their San Francisco headquarters because something fundamental is breaking: the country’s shared foundation of facts.
For decades, the United States has relied on a vast ecosystem of federal data on health, climate, the economy, education, demographics, scientific research, and more. This data is the backbone of journalism, policymaking, scientific discovery, and public accountability. It is how we know whether the air is safe to breathe, whether unemployment is rising or falling, whether a new disease is spreading, or whether a community is being left behind.
But over the past year, that foundation has proven far more fragile than anyone imagined.
Across agencies, data collections have been altered, discontinued, or quietly removed from public view. Analytic teams have been fired. Advisory committees have been disbanded. Climate and environmental datasets have vanished from federal websites. LGBTQ+ data has been dropped from surveys. Long‑running scientific projects have been defunded or re‑scoped. Even the leadership of national statistical agencies can now be replaced at will.
The open data movement—once focused on improving access and usability—has moved into a defensive mode. Thousands of volunteers have stepped up to download and archive endangered datasets. Lawsuits from physicians, farmers, and advocacy groups have restored some information. Congress has rejected some of the most extreme cuts. But the deeper lesson is unmistakable: America’s national data infrastructure can be undermined and damaged far more easily than anyone anticipated.
And when trusted data disappears, something else disappears with it: the possibility of a shared reality.
At a moment when Americans across the political spectrum already distrust institutions, losing reliable national data accelerates the slide toward fragmentation. Without a common set of facts, we cannot solve shared problems—or even agree on what those problems are.
A growing coalition of organizations, researchers, technologists, and civic leaders is working to save and preserve national data on many levels. Now it’s time to bring those lines of work together. We need a coordinated, national program to protect essential data and build alternatives where federal sources fail.
Such a program can begin by acknowledging that we cannot save everything. Data.gov, the federal portal for all the government’s public data, provides access to more than 400,000 datasets. Not all are equally important, equally used, or equally at risk. The challenge is to identify the most essential datasets—such as the ones that underpin public health, climate science, economic stability, education, and democratic accountability—and determine which are vulnerable.
A practical, scalable strategy can include several steps:
1. Track what we’ve lost. We need a thorough, AI-enabled scan of the federal data ecosystem to see what’s already been lost or changed, and set up automated monitoring to detect even subtle changes going forward.
2. Build coalitions in key domains. Public health experts know which datasets matter most to disease surveillance. Climate scientists know which environmental indicators are irreplaceable. Education researchers know which federal surveys track opportunity. These experts must work alongside data scientists, AI specialists, and philanthropic partners to map what truly counts.
3. Prioritize core datasets. Through interviews, surveys, and quantitative analysis—such as tracking citations in research or journalism—coalitions can identify a “core canon” of essential datasets in each field.
4. Assess the risks. Tools like the Data Checkup, developed by dataindex.us, can assess threats to federal datasets. This work can be automated and scaled with AI.
5. Determine the federal role. Some federal data—like satellite observations, national health surveillance, or economic indicators—cannot be replicated by states or private actors. Other data can be supplemented or replaced by state and local sources, private‑sector datasets, crowdsourcing, or nontraditional data sources.
6. Take action to save essential data. When federal data is essential, coalitions can pursue advocacy, public comments, direct engagement with agencies, or litigation. When alternatives exist, they can be developed, benchmarked, and scaled.
7. Put the data to work. The best way to defend data is to use it. Publishing use cases, visualizations, tools, and plain‑language insights helps the public see why this information matters. Generative AI can make federal and open data accessible to millions of non‑technical users.
8. Think globally. The threats to data go beyond the U.S. We need to track the international impacts of U.S. data loss, study how international sources might replace U.S. data, and share lessons learned with other countries.
9. Strengthen institutional protections. In addition to managing today’s immediate problems, we need to develop policies, laws, governance strategies, and guardrails for more stable, reliable data in the future.
10. Sustain the cycle. The threats will evolve. So must the response.
The United States needs a durable, collaborative, and forward‑looking strategy to protect the information that underpins democratic decision‑making. The alternative is a future where facts become optional—and where the loudest voices, not the most accurate data, shape public life.
This essay draws from CODE’s longer, 3,000‑word white paper detailing the full scope of the challenge and a concrete proposal for action. That white paper draws on the work of many dedicated, expert organizations that are already protecting essential data in real time. It outlines how to build an integrated, collaborative, and scalable program that unites these efforts—combining expert judgment, a clear decision framework, rapid response capacity, and both human and AI‑enabled analysis. CODE hopes that this paper can be a starting point to encourage alliances and communities of practice that bring together subject‑matter experts, data advocates, technologists, and philanthropic partners.
Read the full report with the complete proposal for action at
https://bit.ly/NationsDataProgram.
Joel Gurin is president and founder of the Center for Open Data Enterprise (CODE).
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