Introduction

Open government data is a cornerstone of transparency, productivity, accountability, and evidence-based policymaking in the United States (US) and abroad. Open government data enables researchers, journalists, policymakers, and the public to monitor government performance, accelerate discovery, evaluate programs, and safeguard democratic institutions. These data represent an important, non-excludable and non-rivalrous, public good. Yet over the past decade, open government data has faced growing threats, including program disruption from funding shortfalls, political interference, and an erosion of trust government.

Background

In the last year alone political interference led to the manipulation, suppression, or outright removal of federal data assets on topics ranging from climate change, to economics, to LGBTQ+ issues, to public health. This interference threatens to undermine public trust in federal data by limiting the ability of civil society to hold government accountable, eroding the trust businesses hold in federal statistical data, and revoking access to one-of-a-kind data resources to researchers for innovation and discovery.

At the same time, chronic under-funding, lack of interoperability across agencies, and outdated technical infrastructure have compounded the risks to open government data. Even absent political interference, federal data is often fragile and subject to degradation, disappearance, or diminished accessibility due to poor stewardship and a lack of ecosystem sustainability. This issue transcends any single Administration; however, it is compounded during the current moment when program cuts are an ever-present threat to the federal data landscape.

Since shortly after the 2025 US Presidential inauguration, many observers tried to quantify the scale of federal data loss and manipulation. Some suggested only a few hundred datasets have been affected by the actions of the current Administration, while others claimed thousands. As a result, estimates of how many federal data assets were affected by disruptions are uncertain. This is, in part, because of a lack of consistent, transparent methodologies –– including definitions — used by each stakeholder. This report seeks to resolve that confusion by producing a rigorous, transparent analysis and a replicable methodology that stakeholders can use to continually track the state of open government data.

While the primary focus of this report is on open government data assets, which are data made freely available to the public with few-to-no encumbrances as defined in Title II of the Foundations for Evidence-Based Policymaking Act of 2018 known as the OPEN Government Data Act (United States Congress, 2019; Stuessy & Knoedl, 2026), disruptions to restricted federal data assets (such as confidential statistical data) and data tools (such as EJScreen) are also briefly discussed due to their importance in the federal data ecosystem. However, challenges to federal data integrity that involved reported violations of internal agency policies on restricted-access data (Fowler et al., 2026), inappropriate use of data (Schilling & Slowey, 2026), or other violations of the privacy and confidentiality of data providers is out of scope. As articulated in the Federal Framework for Scientific Integrity (National Science and Technology Council, 2023) there is a distinction between inappropriate interference and appropriate political influence in the production of data by the government. Therefore, proposals to modify data collections or methodologies based on Administration priorities that follow appropriate procedures within the context of statute or regulation—including those required by the Paperwork Reduction Act or the Administrative Procedures Act—are also out of scope (examples include testing a U.S. Citizenship question on the Decennial Census (Wang, 2026) or suspending foreign researcher access to NIH data repositories (Stone, 2025)). The exception to this latter exemption is data collection discontinuation, which is discussed as a risk to the integrity of federal data (see below and the related chapter).

A Summary of Reporting on the Integrity of Federal Data from 2025 to 2026

Across 2025 and into early 2026, reporting and analysis detailed actual and perceived disruptions to how the federal government collects, maintains, and publishes data products (Dayak & Kramer, 2026). These accounts spanned multiple domains, including public health surveillance (Rabin & Mandavilli, 2025), economic and statistical information (Heckman, 2025; Kiersz, 2026), climate and environment resources and tools (Brady, 2025) (Hirji, 2025), and administrative records (Hartman, 2025). Disruptions manifested in various ways, ranging from datasets disappearing from public view to delayed statistical series, rewritten or removed webpages, altered metadata, modified survey questions, discontinued collections, and halted disclosure and research proposal reviews for restricted data access (Jones, 2025; Levenstein & Kubale, 2025; Dayak & Kramer, 2026).

Reports frequently attempted to quantify the scale of the disruption, though reporters and researchers acknowledged the immense difficulty of tracking exact figures (Dayak & Kramer, 2026). Publications cited large numeric estimates to illustrate the breadth of the problem, frequently relying on changes to the topline dataset count reported on Data.gov to demonstrate the decline, even as experts cautioned that the dynamic nature of the portal made this an imperfect metric (Data Foundation, 2025). Within the first two weeks of the new administration, observers noted a sudden drop of over 2,000 datasets from the portal, falling from approximately 307,854 to 305,564 (Koebler, 2025). By early February, legislative statements cited a reduction of 1,055 datasets (Rep. Don Beyer, 2025), while independent trackers noted the decline had grown to 3,379 entries later that month (Kutz, 2025). By June 2025, reports estimated that over 3,000 taxpayer-funded datasets had been removed across various agencies (Palmer, 2025). Audits of specific agencies revealed severe operational interruptions, with an analysis finding that nearly half of the frequently updated databases in the public health sector had been paused without explanation, and at least 146 specific files were documented as removed or modified to replace terminology (Robbins, 2025).

Furthermore, the scale of the threat to data was often framed by the sheer volume of material within the purview of data preservation efforts as independent groups rushed to archive information. Journalists routinely noted that an exact census of the losses remained elusive because many cuts occurred quietly without public announcement, forcing the press to rely on approximations and independent monitoring groups to estimate the total impact (Dayak & Kramer, 2026). To preserve public information, Harvard Law School’s Library Innovation Lab preserved 311,000 datasets by systematically crawling agency dataset download links from Data.gov, totaling 16 terabytes of data (Satter, 2025). Other large-scale efforts like the Data Rescue Project archived more than 1,200 data products originating from over 80 distinct government agencies (O’Leary, 2025). Specialized data rescue initiatives also focused on scraping and archiving environmental justice data directly from federal sites to ensure continued public access (Mandel, 2026; Willson, 2025).

Litigation also emerged as a critical mechanism for data restoration. In response to the widespread removal of federal climate and environmental justice resources, advocacy groups launched targeted legal challenges against the administration to force the restoration data and data tools. In February of 2025, a coalition representing farmers and environmental organizations sued the U.S. Department of Agriculture (USDA) for unlawfully purging climate-related agriculture resources and the interactive “Climate Risk Viewer” from its websites (Garza, 2026). The USDA subsequently agreed to restore the webpages, and a March 2026 legal settlement required the agency to share the underlying raw datasets to ensure permanent public access, even if the government websites were taken offline again. Separately, environmental and consumer watchdog groups filed a federal lawsuit challenging the administration’s sudden deletion of several key environmental justice mapping tools, including the EPA’s EJScreen and the Council on Environmental Quality’s Climate and Economic Justice Screening Tool (CEJST), arguing that the unannounced removals violated administrative procedures and unlawfully deprived vulnerable communities of crucial pollution data (Noor, 2025). This suit was subsequently dismissed, with the presiding judge ruling that the groups did not have standing to sue (Clark, 2026). Another major lawsuit resulted in a legal settlement requiring the Department of Health and Human Services to restore over 100 specific datasets, webpages, and tools that had been removed from public access (Alder, 2025) (HHS ended up restoring more than 300 such resources). Additionally, federal accountability watchdogs determined that the takedown of a key budget apportionment website violated federal law, leading to a court order that forced the administration to republish the spending transparency data (Hill, 2025; Katz, 2025). Subsequent appeals court rulings reinforced that such clamp-downs on spending data defied congressional authority (Cheney & Gerstein, 2025). Finally, outside groups also went to the courts when news of orders to destroy classified and personnel records at USAID came to light (Beitsch, 2025).

These lawsuits reveal insights into the value of federal open government data. While many data rescue efforts focus on data preservation at scale largely under a value system of archiving data not just for its use but as a cultural artifact, plaintiffs in lawsuits exercising a private-right-of-action operate out of immediate, tangible necessity for specific data. The willingness to endure the grueling, costly process of federal litigation serves as a de facto “market test” for a dataset’s value. It reveals exactly which data civil society relies on to hold the government accountable, ensure equitable access to resources and benefits, or protect public health and safety. Ultimately, while bulk preservation safeguards the existence of the data, the significant investment required to litigate underscores its active, indispensable role in the functioning of democracy.

Research Methods

A mixed-methods approach for gathering and reporting evidence was used in this report. This approach included statistical analysis, literature review, forensic auditing, and confidential interviews with key actors. Most statistical analyses were conducted in R and all programming for data collection and forensic auditing was done in Python. Unless otherwise disclosed, the data and scripts for reproducing this work (and to support future auditing of the federal data ecosystem) are provided in the GitHub repository supporting this work. No interview notes or transcripts were retained to protect the identities of trusted confidants and the information obtained during those interactions was largely confirmatory (of evidence gathered or otherwise publicly reported) in nature. Efforts were made to rely on government documents and publicly accessible (or open access) reference and source materials. However, since much of the reporting about disruptions to federal data occurred in the press or on proprietary blogs, paywalls or other barriers may be encountered in attempting to access references listed in the bibliography. A list of more than 150 sources from news media, scholarly publications, and civil society websites, can be found in the project data repository in the file named: data-integrity-news.csv.

The research and forensic auditing supporting this report makes substantial use of the Internet Archive’s Wayback Machine (WBM). The Wayback Machine (WBM) functions by deploying automated “crawlers” that traverse the web, downloading publicly accessible pages and documents, processing and archiving them, and making them publicly accessible on their website. These captures (called snapshots) are timestamped and organized into an index of a website’s in situ history, allowing users to enter a URL and navigate through a calendar of such snapshots to see how a site looked during specific points-in-time. The WBM creates a permanent, searchable record of the internet’s history, preserving content that would otherwise be lost to link rot, page changes and removals, or server shutdowns. Critically, the WBM snapshots often capture datasets during its crawls. The Application Programming Interfaces (APIs) that the Internet Archive provides for the WBM make make it accessible and adaptable to data auditing routines (see the chapter on auditing, for instance). This is an invaluable resource and the work could not have been done without it.

In addition to the source code and data provided in the GitHub repository, the chapter titled Auditing Open Government Data Assets includes additional details on the methods used to assess changes in federal data. These methods were used throughout the development of this report, providing much of the data and evidence described in the substantive chapters and forming the basis of questions asked to key actors during those interviews and conversations. This chapter also includes a full workflow and a use-case, which aims to assist others in applying these same routines to future monitoring efforts.

Defining Disruption: Deletion, Access Removal, and Discontinuation

To accurately assess the federal data ecosystem, one must precisely define the mechanisms of data loss and disruption. Public discourse frequently conflates different administrative actions under the umbrella term “deletion.” However, federal data disruptions typically fall into three distinct categories with vastly different implications for preservation and recovery. These are deletion, access removal, and discontinuation. Moreover, there is a complex of statutes and policies that create a regulatory framework around which the federal data ecosystem is supposed to operate, including those that establish lawful methods to effectuate each of these aspects of data management. This regulatory framework, and its limitations, is discussed in detail by a new 2026 Congressional Research Service report (Stuessy & Knoedl, 2026).

Data Deletion Data deletion refers to the actual destruction or erasure of underlying records from federal servers and databases. True deletion is rare due to federal records retention laws, but when it occurs, it represents a permanent loss of historical information. If raw data files are permanently purged from an agency database without prior archiving, the fundamental integrity of that historical record is destroyed and the agency has likely run afoul of their obligations under the Federal Records Act.

There is very little evidence of actual data deletion - one example, however, may have occurred when USAID was ordered to destroy records during the chaotic dismantling of the agency (Malesky, 2025; Beitsch, 2025). Many USAID open government data assets were removed from public access (see below), but the extent of actual data deletion is unknown (and, according to sources familiar with the subject, unlikely).

Public Access Removal Public access removal occurs when data continues to exist on internal federal servers but the public facing portals, dashboards, or download links are taken offline. In these instances, the agency retains the data for internal operational use or archiving, but external researchers, journalists, local governments, and the general public lose visibility and access. When this occurs, data are not deleted, but their utility as a public good is reduced. One of the transparency tools that the public has at its disposal with respect to public access removal is OMB Circular A-130 which requires an agency to provide:

“…adequate notice when initiating, substantially modifying, or terminating dissemination of significant information that the public may be using;”

There are many examples of public access removal during 2025. For example, the Homeland Infrastructure Foundation-Level Data dataset was pulled from public access, altering how non-federal actors could map infrastructure and plan disaster responses (Dayak & Kramer, 2026). Another critical example was the unlawful removal of apportionment data by OMB (Hill, 2025) that was later restored by court-order (Cheney & Gerstein, 2025).

Discontinuation Data collection discontinuation involves halting the ongoing gathering of new information that supports the growth or revision of exiting data assets. Historical data may remain perfectly intact and publicly accessible after discontinuation, but the pipeline for new data is severed. While the Paperwork Reduction Act (PRA) provides a framework for public transparency into collection discontinuation for a large tranche of federal data, there are blindspots including collections not associated with a federal rulemaking and any scientific or programmatic data that are not subject to the PRA.

Like public access removal, discontinuations were widespread during 2025. An example of this is the Department of Agriculture terminating a long running report on household food security (Smith, 2025). The Department amplified its decision in a press-release and justified it as an exercise in eliminating wasteful spending on redundant collections. However, this justification was largely decried by advocacy groups as a red-herring and, frankly, inaccurate (FitzSimons, n.d.).

Structure of this report

This report was created using a custom GitHub pages deployment based on JustTheDocs and jekyll-scholar. This allows for a more dynamic reading experience and facilitates collaborative updating in the future. A version of the entire report suitable for viewing and printing is available here. Each chapter can be read separately - there are no linear dependencies between the chapters of this report.

Chapter Contents

This Introduction and the Executive Summary are intended to provide sufficient overview of this project. Additional substantive chapters lavish more detail into specific topics relevant to the research conducted in support of this report. They may be read in any order without loss of context. These chapters contain information specifically on:

  • The Federal Data Catalog: This section provides a history of Data.gov and the Federal Data Catalog (FDC). It details information policy history from the 2009 Presidential Memorandum on Transparency to the Evidence Act of 2018 to the release of the Open Government Data Act implementation guidance in 2025. It also discusses limitations of using Data.gov for evidence of data integrity issues: such as the harvesting model, incomplete metadata, and the distinction between a metadata catalog and a data repository. Read chapter.
  • Administrative Risks to Data: This group of chapters examines several administrative risks to federal data and information collections.
    • The Richardson Waiver Rescission: This chapter provides a specific case study related to data integrity challenges that could result from the recent decision from the US Department of Health and Human Services to avoid the certain Administrative Procedures Act processes. Read chapter.
    • The Paperwork Reduction Act Exemptions: This chapter explains the legal requirements for federal information collection and how certain exemptions to those requirements pose a significant risk to federal data oversight and integrity. Read chapter.
    • Information Collection Discontinuation and Revision: This chapter analyzes the processes and impacts of ending specific data collections through the standard Paperwork Reduction Act and Administrative Procedures Act processes. Read chapter.
    • Resourcing and Staffing: This chapter focuses on the impacts of funding and staffing changes at federal agencies necessary to maintain data assets. Read chapter.
  • Data Tools: This chapter reviews removal of the various tools and platforms used by agencies to disseminate data and information to the public. Read chapter.
  • Agency Case Studies: These chapters provide detailed examinations of cases of data integrity issues at three specific agencies:
    • Office of Management and Budget: Describes the federal apportionments data takedown and restoration timeline by OMB. Read chapter.
    • United States Agency for International Development: Describes how the dismantling of USAID resulted in a large tranche of public access removal to data assets. Read chapter.
    • Department of Veterans Affairs: An examination of VA metadata and dataset changes that replicates (and augments) the findings of previous research. Read chapter.
  • Auditing Open Government Data Assets: This chapter describes the workflow and methods used to audit the Federal Data Catalog, individual datasets, agency comprehensive data inventories, and provides a replicable use-case. Read chapter.
  • References: This section includes a comprehensive list of references cited throughout the report View references.
  • Glossary: The section provides a comprehensive glossary of terms either directly referenced within the report or otherwise relevant to federal data. View glossary.

Artificial Intelligence Use Disclosure

No artificial intelligence system (AI) was used in the writing of the text included in this report. However, AI large-language models were used in several other ways that contributed to the quality of this report. These included:

  • Google Gemini: Generating bibtex entry citations from URLs and uploaded documents using a custom-build agent with Google Lab’s Gem/Opal available here.
  • ChatGPT: Monitoring RSS and news feeds for new developments in the press on data integrity issues relevant to this report using OpenAI’s “Pulse” with the following prompt: “Search for newly published (January 2026 onward) news stories, blog posts, academic papers, and Federal Register notices related to federal data, including statistical data, scientific data, privacy, removal, deletion, or integrity concerns, and notify me with a concise summary.”
  • Claude Code and Google Gemini Pro: Assisting with code generation, debugging coding errors, and designing API navigation routines linked to the project’s GitHub repository. All AI-generated code is disclosed in the comments of relevant scripts.
  • Google Gemini Fast: Creating figures from presentation slides using Google Slides’s ‘beautify this slide’ functionality. All images created this way are disclosed in the captions.
  • Google Gemini Pro: Creating a graphical abstract of the whole project for the Executive Summary. This was done by granting Google Gemini Pro Model access to the repository and instructing with the prompt to: “carefully read the report and pay particular attention to the /report/_chapters/execsum.md. Create an infographic that could be used as a graphical abstract of the whole project to include in the executive summary.”

References

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