Stefaan Verhulst

CRS Report: “Federal data can provide valuable information for various audiences—from farmers seeking to protect bats that eat crop-harming insects to local efforts determining where to rebuild to avoid coastal flooding. In 2013, the Office of Management and Budget (OMB) described openly available federal data and statistical information as “a valuable national resource and strategic asset” that, when made accessible, discoverable, and usable by the public, “can help fuel entrepreneurship, innovation, and scientific discovery.”

Efforts to make federal data more readily available have evolved over time. Such data may have been stored and filed in hard and paper copies and later in software and electronic formats. Today, certain data may be retrieved through agency websites or on Data.gov. Data.gov itself is a case study for open data, intended to demonstrate that making federal data available can help agencies avoid duplicative internal research, enable the discovery of complementary datasets held by other agencies, and empower employees to make better-informed, data-driven decisions, among other benefits.

Throughout 2025, media reports have suggested that the availability of federal data has been reduced. Some observers are also tracking the removal of specific datasets, variables, and tools. In parallel, changing public perspectives on data availability may demand new levels of data access, such as making data available for predictable periods of time, in a variety of software-compatible formats, and with appropriate descriptive metadata for easing findability and usability of the information. While statute discusses when and how information is to be added to Data.gov, it does not explain whether and how information may be removed. Although researchers and the public may derive value from being able to trace data over time to determine changes in trends or collection methods, the statute does not explicitly consider versioning requirements for agency data. However, requiring these attributes for Data.gov may help address or clarify difficulties in measuring data availability. Congress may be interested in determining whether there are trends to certain data becoming available or to when data is altered and removed. Such trends may provide insight and direction for Congress to further examine agency activities or make decisions to support new data use cases.

Information availability, of which data availability is a type, can be considered the intersection of when and how information is released. Section 3552 of Title 44 of the U.S. Code defines information availability as “ensuring timely and reliable access to and use of information.” Generally, statute and associated OMB guidance contemplates two types of information availability in terms of timing: (1) proactive disclosure and information dissemination and (2) request-based disclosure. Certain types of data have specific requirements in terms of formatting and structure to ensure that the information can be made available and potentially archived.

This report examines the variables of federal data availability and its policy underpinnings. The report discusses the state and concept of federal data availability and explains the information life cycle framework. It explains how information may be made available proactively or upon request through existing mechanisms and also explains statutory requirements for information dissemination, preservation, and whether and when information can be removed. The report concludes with policy options for Congress, including a review of efforts to preserve federal data through web captures; examining controls to assess data versioning, sourcing, and modifications; and, finally, considerations for implementing data governance and transparency mechanisms throughout agency structures…(More)”.

Article by Carl Zimmer: “Scientists publish more than 10 million studies and other publications a year. Some of those findings will add to humanity’s storehouse of knowledge. But some will be wrong.

To assess a study, scientists can replicate it to see if they get the same result. But seven years ago, a team of hundreds of scientists set out to find a faster way to judge new scientific literature. They built artificial intelligence systems to predict whether studies would hold up to scrutiny.

The project, funded by the Defense Advanced Research Projects Agency, or DARPA, was called Systematizing Confidence in Open Research and Evidence — SCORE, for short. The idea came from Adam Russell, then a program manager for the agency. He envisioned generating a kind of credit score for science.

“People can say, ‘Hey, this is likely to be robust, we can premise a policy on it,’” said Dr. Russell, who is now at the University of Southern California. “‘But this? Nah, this might make for a book in the airport.’”

The SCORE team inspected hundreds of studies, running many of them again, to better understand what makes research hold up. Now it is publishing a raft of papers on those efforts.

For now, a scientific credit score remains a dream, the researchers say. Artificial intelligence cannot make reliable predictions…

For more than 15 years, some scientists have been trying to change the culture. They started by documenting the extent of the problem. In the early 2010s, Dr. Nosek and colleagues replicated 100 psychology papers — and matched the original results only 39 percent of the time.

In another project, Dr. Nosek teamed up with cancer biologists to replicate 50 experiments on animals and human cells. Fewer than half of the results withstood their scrutiny…(More)”.

Research Agenda & Bibliography of Proposals by Anna Lenhart: “In recent years, academics, advocates, and policymakers have proposed or discussed the need for a new digital regulator (NDR) – a new agency of the federal government that regulates the AI and technology industry, with a particular focus on market competition, data privacy, and transparency & safety. We have documented over 20 academic papers and studies, think tank reports, books and parts of books, essays and op-eds, and pieces of legislation that propose such agencies or analyze such proposals. 

On February 25, 2026, the Institute for Data, Democracy and Politics at George Washington University and the Vanderbilt Policy Accelerator hosted many of the experts who authored those proposals for a day-long summit to discuss the need for an NDR and open questions related to the design of the agency. Informed by those discussions, this research agenda outlines questions we believe still deserve additional research attention, across disciplines. We are publishing this agenda in hopes to inspire scholarly work on these issues. Some areas may already have work that we have inadvertently missed from our literature review, and we welcome input from those interested in these issues…(More)“.

Paper by Anton Korinek & Joseph E. Stiglitz: “Rapid progress in new technologies such as AI has led to widespread anxiety about adverse labor market impacts. This paper asks how to guide innovative efforts so as to increase labor demand and create better-paying jobs while also evaluating the limitations of such an approach. We develop a theoretical framework to identify the properties that make an innovation desirable from the perspective of workers, including its technological complementarity to labor, the relative income of the affected workers, and the factor share of labor in producing the goods involved. Applications include robot taxation, factor-augmenting progress, and task automation. In our framework, the welfare benefits of steering technology are greater the less efficient social safety nets are. As technological progress devalues labor, the welfare benefits of steering are at first increased but, but beyond a critical threshold, decline and optimal policy shifts toward greater redistribution. Moreover, as labor’s economic value diminishes, steering progress focuses increasingly on enhancing human well-being rather than labor productivity…(More)”.

Editorial by Nature: “The durability of research findings can be cast in terms of three Rs. Findings should be reproducible (the same type of analysis using the same data should produce the same result); replicable (redoing an experiment to collect fresh data should produce the same result); and robust (alternative analyses using the same data should draw the same conclusion).

Over the past two decades, studies in fields from psychology to medicine have highlighted that these criteria are often not met, leading to talk of a crisis in replication and reproducibility. Four papers published this week in Nature look at the reproducibility, replicability and robustness of research in the social and behavioural sciences. They provide a snapshot of the analysed fields, and suggest factors that could make research findings more likely to endure. Researchers, funders, journals and institutions should take note — for the betterment of all science.

Three of the papers are an outcome of nearly US$8 million in funding provided in 2019 by the US Defense Advanced Research Projects Agency to the Systematizing Confidence in Open Research and Evidence (SCORE) programme. The project is run by the Center for Open Science, a non-profit organization in Washington DC. More than 850 researchers contributed to hundreds of duplication efforts, establishing a database of reliability markers for 3,900 papers published between 2009 and 2018 (see go.nature.com/4campyc). The fourth paper is the result of a series of one-day ‘replication games’ workshops organized around the world since 2022 by the Institute for Replication, a virtual, non-profit network.

Some of the results are sobering. For example, Tyner et al.find that statistically significant effects could be replicated for only about half of the 164 papers they studied. Moreover, the replicated effect sizes were on average less than half of what was originally reported. This ‘decline effect’ has been reported before, but it is unclear how much is due to authors’ cognitive biases, questionable research practices, the preference of journals for eye-catching results, flukes or true effects that are specific to a particular population and time…(More)”.

Paper by Erzhuo Shao, Yifang Wang, Yifan Qian, Zhenyu Pan, Han Liu & Dashun Wang: “We introduce SciSciGPT, an open-source, prototype artificial intelligence (AI) collaborator that uses the domain of science of science as a testbed to explore the potential of large language model-powered research tools. SciSciGPT automates complex workflows, supports diverse analytical approaches, accelerates research prototyping and iteration and facilitates reproducibility. Through case studies, we demonstrate its ability to streamline a wide range of empirical and analytical research tasks while highlighting its broader potential to advance research. We further propose a large language model agent capability maturity model for human–AI collaboration, envisioning a roadmap to further improve and expand upon frameworks such as SciSciGPT. As AI capabilities continue to evolve, frameworks such as SciSciGPT may play increasingly pivotal roles in scientific research and discovery. At the same time, these new advances also raise critical challenges, from ensuring transparency and ethical use to balancing human and AI contributions. Addressing these issues may shape the future of scientific inquiry and inform how we train the next generation of scientists to thrive in an increasingly AI-integrated research ecosystem…(More)”.

Article by Nadiya Safonova, Hannah Chafetz, and Stefaan G. Verhulst: “In 2025, the Armed Conflict Location & Event Data organization recorded more than 185,000 violent events. This is nearly double that of 2021. Not only is the level of conflict growing, the nature of warfare is changing. Technologies such as drones, artificial intelligence (AI), and smartphones are increasingly being used for military combat and defense purposes.  

The use of these technologies for warfare is pulling more civilians into conflict. Anyone in possession of a smartphone can easily transmit vital information in real time, including positions of troops, military equipment and persons of interest. Civilian participation in warfare comes with a higher risk of death and injury. 

Conflict can also unfold online through communication technologies. This includes disinformation campaigns aimed at eroding public trust, and hate speech aimed at exacerbating tensions between groups. This can spark, prolong and escalate physical conflicts.

However, these technologies can also serve another purpose—one that promotes, builds, and maintains peace. 

Over the last several years, peace technologies (or PeaceTech) have emerged around the world and have provided new pathways to promote human dignity and rights. Yet, what are the latest developments in the field of PeaceTech and how are technologies shaping peacebuilding efforts?

In what follows we highlight several examples of PeaceTech launched or updated in 2025 and explain how they are being applied before, during, and after a conflict. We conclude with cross-cutting takeaways across these examples. 

The examples were collected through desk research in February 2026 and do not aim to be fully comprehensive. It is rather a selection of technologies used in different conflicts across the globe. As technologies rapidly evolve, this blog provides a snapshot in time and does not assess the ethical implications of these technologies…(More)”.

Paper by Melissa Ross, Hazel Jovita, and Lucas Veloso: “Growing research on citizens’ assemblies has focused primarily on ‘frontstage’ standardization and deliberative quality, centering the experience of assembly members. Only very recently have studies and guidelines turned to the ‘backstage,’ considering who makes decisions and how when it comes to organizing and running citizens’ assemblies. This article provides four original contributions to this emerging line of inquiry. First, we identify three constitutive elements of governance: who governs (stakeholders), what (decision-making), and how (integrity). Second, we provide a working definition of governance as the negotiation of tensions between deliberative values and practical constraints in commissioning, designing, and delivering citizens’ assemblies. Third, we illustrate these findings with original focus group and interview data from the 2021 Global Assembly on Climate and Ecological Crisis, centering the experience of a global community of practice. Fourth, we reveal three key tensions at the core of governing citizens’ assemblies: (1) collaboration across diverse stakeholders, (2) grounding decision-making, and (3) balancing horizontal and vertical logics. These elements and tensions offer insights for both future research and practice…(More)”.

Paper by Stefaan Verhulst: “As data sharing and reuse become central to scientific discovery, artificial intelligence, and public decision-making, the challenge of data governance has shifted from a primarily technical problem to one of institutional design. Over the past decade, a wide range of governance models for data collaboration have emerged-including data trusts, data commons, data cooperatives, data intermediaries, data unions, data sandboxes, and data spaces. These models are often presented as competing institutional solutions to the problem of responsible data sharing. This paper argues instead that such models represent distinct governance responses to different structural challenges within data ecosystems. 

Building on the concept of data collaboratives-which I introduced in 2017 as cross-sectoral arrangements for responsible data reuse in the public interest-I propose a purpose-driven typology that identifies seven governance archetypes and the specific coordination problems they address, including transaction costs, power asymmetries, legitimacy deficits, collective governance needs, ownership inequality, systemic uncertainty, and scaling complexity. 

I argue that the question is not which governance model is normatively superior, but rather which model is fit for purpose within particular institutional contexts. The paper concludes by introducing a functional theory of data collaboration centered on institutional orchestration, whereby multiple governance arrangements coexist and evolve within polycentric data ecosystems. In this framework, strategic data stewardship becomes essential for diagnosing governance needs and sequencing institutional responses that enable responsible and sustainable data reuse…(More)”.

Report by Janna Anderson and Lee Rainie: “Hundreds of global technology experts share insights, urging an all-encompassing systems response by leaders to serve humanity’s best interests in light of rapid technological change…These globally-located experts from all walks of life noted that AI is quickly becoming the invisible operating system of society, shaping how opportunity is distributed, services are delivered, risks are managed and human rights are experienced. Most said the traditional resilience strategies humans have employed for millennia – focused on individual “grit,” and after-the-fact personal adaptation – are not enough to help humanity flourish as we adjust to an AI-infused future.

These experts predicted:

• AI’s larger role: 82% said AI will have a significantly larger role in shaping our daily lives and key societal systems in the next 10 years or less; 13% said that level of change is 20-30 years away.
• AI guiding decisions: 56% said that at the time they expect AI will be significantly more advanced it will influence, guide or control “nearly all” or “most” human activities and decisions (another 24% said AI will influence, guide or control nearly half of activities and decisions).
• Resilience worries: 45% said humans will be only “a little” or “not at all” resilient in the face of that level of change. About half said people will be somewhat to very resilient.
  Of note: Many experts wrote in their essay responses that many to most humans will passively accept the influence of AI systems. Thus, these people will not feel any need to be resilient.
• Satisfaction concerns: only 33% said people will be more satisfied than dissatisfied with AI systems at that time; 31% said people will be more dissatisfied than satisfied; 33% said people will have an equal amount of satisfaction and dissatisfaction with AI systems.

(See downloadable PDF, a four-page news release and 15-page executive summary)…(More)”