Stefaan Verhulst
Book by James M. Tabor: “In 1854, the American entrepreneur Cyrus Field set out to lay a 2,000-mile telegraph cable across the bottom of the Atlantic Ocean. Nothing like it had ever been attempted. Field knew nothing about telegraphy, electricity, ships, or oceans, and science itself still lacked a universal theory of electricity. But he believed that wiring the world for near-instantaneous communication would bring about peace on Earth. In 1866, after enduring over a decade of global scorn, catastrophic failures, staggering losses, and brushes with death, he would finally lay his great cable, ushering in the global information age. From acclaimed author James M. Tabor, Lightning Beneath the Sea is an unforgettable tale of radical vision, unwavering determination, and triumph against overwhelming odds that transformed life on Earth forever.
In a propulsive narrative, Tabor tells how Field swiftly assembled an all-star scientific dream team that included telegraph legend Samuel F. B. Morse; a young Lord Kelvin, called the da Vinci of his day; Michael Faraday, the father of electrical engineering; and legendary philanthropist Peter Cooper. Together they battled epic storms, freak accidents, corporate sabotage, the enmity of Abraham Lincoln, and the hubris of the project’s original chief electrician—an eccentric who insisted on being called Wildman—while racing two rival efforts to establish telegraphic communications between continents. When it was finally done, Field’s cable lay up to 2.5 miles deep under the ocean, and the London Daily News announced: “Time and space seem literally annihilated.” The cable’s legacy can be traced today in the hundreds of descendants that still carry 98 percent of the world’s information through a “world undersea web.”…(More)”
Paper by Marcia Langton, Robert McLellan, Jennifer Fewster, and Kristen Smith: “The Framework for the Governance of Indigenous Data has been developed to guide ethical, inclusive, and culturally grounded data practices across the Humanities, Arts, Social Sciences, and Indigenous Research Data Commons (HASS and Indigenous RDC).
It responds to long-standing calls from Aboriginal and Torres Strait Islander communities for greater control over data that affects their lives, lands, cultures, and futures. The Framework is the result of extensive consultation, co-design, and collaboration with Indigenous data custodians, researchers, institutions, and policymakers. It is intended to be adopted across all HASS and Indigenous RDC activities.
This Framework affirms that Indigenous data governance is essential to Indigenous self-determination. It defines Indigenous data as information generated by, about, or for Aboriginal and Torres Strait Islander peoples, encompassing cultural, ecological, linguistic, genealogical, and community-generated knowledge. The Framework recognises that all data – whether held by governments, institutions, or communities – has implications for Indigenous peoples and must be governed in ways that respect Indigenous rights, laws, and relational worldviews.
The Framework is structured around three interrelated components: a Governance Model, a set of Governance Guidelines, and a Monitoring and Accountability structure. The Governance Model identifies 5 foundational elements:
- Recognition of Indigenous assets
- Partnership
- Building capabilities
- Self-determination
- Inclusive data ecosystem.
These elements underpin the Framework’s guidelines and practices, which provide actionable strategies for institutions and communities to embed Indigenous governance across the data lifecycle.
The Framework is grounded in the CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) and complements the FAIR Principles (Findable, Accessible, Interoperable, and Reusable)…(More)”.
Article by Ashley Farley: “When the Gates Foundation’s Open Access Policy was established, in 2015, the aim was to shift the ecosystem so that open access became the norm. In the years that followed, while open access output did increase, the overall ecosystem shifted in the wrong direction: costs rose sharply and commercial capture intensified.
In 2024, as the foundation reflected on what about the policy was working and what could be improved, we had two choices: to stay the course with a policy that wasn’t improving the broader ecosystem or to cease support for unsustainable practices. We chose to confront the systemic issues in open access publishing head-on, updating our policy to require Gates-funded research be shared as preprints. This change enabled the foundation to divest from restrictive article processing charges (APCs) and redirect resources toward more equitable and sustainable open access models….As APCs became the dominant model for open access publishing, they crowded out other, more equitable approaches and made it difficult for alternative models to scale. The foundation’s decision to stop paying APCs will inevitably put pressure on the broader publishing ecosystem, with both positive and challenging effects.
While the intention is to build a more equitable open access system, we recognize that authors without access to alternative funding for APCs will face tougher publishing choices in the short term. But our data shows that grantees continue to find ways to pay for APCs. And the new policy intentionally preserves author choice. Authors are not required to choose journals that levy APCs. As the various financial streams that flow to publishers start to consolidate, that flow could better reflect the values of funders and institutions, creating pressure that encourages publishers—especially large commercial ones—to reduce their prices. Although publishing carries real costs, charging researchers thousands of dollars to make their work openly available is rooted in inequitable, colonial structures…(More)”.
Article by Daniela Paolotti and Stefaan Verhulst: “The emergence of Hantavirus cases appeared, at first glance, to be a localized public health incident. Yet, when viewed alongside recurring Ebola outbreaks, growing concerns about avian influenza, and other zoonotic disease threats, these events highlight a broader lesson from COVID-19: our vulnerability to infectious disease outbreaks is shaped not only by the pathogens themselves but also by the preparedness of our data ecosystems and our ability to translate information into timely action. In particular, responsible access to non-traditional data sources -including mobility data, online search behavior, social media activity, transaction records, crowdsourced information, and other digital traces- has become critical in complementing traditional surveillance systems. These data sources can provide earlier, more timely, and more granular insights into emerging risks, helping decision-makers detect outbreaks sooner, understand behavioral dynamics, target interventions more effectively, and strengthen overall preparedness and response efforts
As such, these recent outbreaks provide a useful lens through which to revisit some of the insights of our recent research on non-traditional data and pandemic preparedness. In the below, we share key insights from our analysis of the COVID-19 response, reflect on their continued relevance in the context of the current Hantavirus and Ebola outbreaks, and outline three recommendations to help ensure that the barriers, delays, and missed opportunities of past crises are not repeated again.
This article is not intended to be a comprehensive assessment of either (on-going) outbreak -such an undertaking will require more extensive epidemiological, operational, and governance analysis (which we recommend). Rather, our objective is to highlight a number of emerging warning signs and recurring challenges that deserve more serious attention…(More)”.
PressRelease: “The European Commission today presented the European Technological Sovereignty Package, a set of measures to strengthen Europe’s capacity in semiconductors, artificial intelligence (AI), cloud and open source.
Commission President, Ursula von der Leyen said: “We cannot afford to depend on others for the technologies that keep our hospitals running, our energy grids stable and our services secure. This is about protecting our citizens, defending our interests and making our own choices. Europe has the talent, the research excellence, the industrial base and the Single Market. Together, we must turn these strengths into technological sovereignty.”
The package includes two legislative proposals – the Chips Act 2.0 and the Cloud and AI Development Act – as well as the Open Source Strategy and a Strategic Roadmap for Digitalisation and AI in Energy.
Together, these measures support Europe’s ambition to become an AI continent, strengthen its digital autonomy and help build a more sustainable digital future. They will help widen choice in core technologies for EU businesses, citizens and public administrations.
The move comes as Europe remains heavily dependent on suppliers outside the European Union for core digital technologies and as demand for computing capacity rises sharply with the spread of AI. It is designed to reduce structural dependencies and make sure Europe can develop, deploy and secure the technologies Europeans rely on. It signals a major shift in the EU’s approach to technology…(More)”.
Paper by Ankit Bhutani, Guillermo Ordoñez & Laura Veldkamp: “Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and depreciation losses are missed in firms’ balance sheets. Another part, unique to data, is that it serves as a means of payment in the modern economy: consumption bartered for data is also omitted from national accounts. We propose an output-based approach to measure the missing value of data. We treat data as an asset, measure its volume based on the quality of firms’ revenue forecasts, and endogenously determine its depreciation. We then capitalize the data value and explore what the measured GDP would be if the data were treated and transacted similarly to a physical asset. Our findings suggest that the aggregate value of data is about 1.5% of GDP….(More)”.
Article by David Adam: “When psychologist Raluca Rilla asked volunteers to complete a survey last year, she got the following response to one of her questions: “I don’t experience confusion in the same way humans do.”
Rilla, a PhD student at the Max Planck Institute for Human Development in Berlin, suspects that this is the obvious tip of a large and worrying iceberg — one that could scupper academic research on how people think and behave. She and her colleagues estimate that up to 45% of responses they receive to such surveys are now copied and pasted from the output of large language models (LLMs). In some cases, participants might simply be polishing their language. In others, Rilla thinks that the entire operation — signing up, reading the questions and submitting responses — is handled by a machine. Such answers, and the academic studies built on them, are unlikely to reflect the reality of human nature.
Experimental psychology is not alone in wrestling with the impact of LLMs on research. From political science and economics to opinion polling, researchers across the social sciences are sounding the alarm after finding the fingerprints of artificial intelligence and considering the implications. AI chatbots are infiltrating social-science surveys — and getting better at avoiding detection
Even if AI input into polls can be throttled, there’s a concern at the analysis stage, says David Lazer, a political and computer scientist at Northeastern University in Boston, Massachusetts: AI-assisted analyses in social science might flood journals with spurious findings by rapidly whipping up studies. One journal has already chronicled a vast increase in the number of manuscripts it has received that were wholly or mostly prepared using AI tools.
The explosion in the use and power of AI models touches researchers across all academic fields. But the impact on the social sciences is especially acute, says Joshua Tucker, a political scientist at New York University. That’s because, compared with other disciplines, much social-science research is heavily reliant on survey data and analysis. And when researchers aren’t gathering the data themselves, they are often analysing large, general data sets, such as censuses or other huge surveys that were usually collected for a different original purpose. This means that apparent signals in the data can be plucked from noise in a way that isn’t possible with experimental data obtained in narrow tests to check a hypothesis — information that tends to have a single use and a defined shelf life.
“I think we’re approaching a time where the trust in behavioural and social sciences will be undermined by this constant threat of LLM pollution,” says Björn Hommel, a psychologist at Leipzig University, Germany. “And there’s nothing that we are able to do about it right now.”
But it’s not all doom and gloom. An alternative view of the latest AI systems is that they could transform social science by making its findings more robust. The same algorithms that can be used for superficial work such as polishing language can also source and analyse complex data sets quickly and, by toggling through statistical techniques, check how sensitive an individual finding is to various analytical methods. AI-assisted review could help to spot methodological errors, and social-science journals might insist on the use of more-robust methods as AI makes it easier for researchers to attempt them…(More)”.
Article by Eric Niiler: “The Trump administration is dismantling a $368 million deep-ocean observation system that was put in place a decade ago to monitor coastal environments, marine ecosystems and powerful currents that affect the global climate.
The National Science Foundation said it would send ships in June to begin removing more than 900 deep-sea instruments anchored off Oregon, Washington State, Alaska, North Carolina, and an area between Greenland and Iceland known as the Irminger Sea.
Scientists have used data from the system to understand how the ocean is absorbing greenhouse gases from the atmosphere, how changes in ocean temperature such as marine heat waves might affect fisheries or signal bigger shifts in the climate, and coastal flooding along the East Coast…(More)”.
Roadmap by Centre for Social Impact: “How do you measure the real difference your work is making for people and communities – particularly when social issues are interconnected, persistent and solutions are constantly evolving?
For many organisations, social impact measurement can feel challenging. While it’s often easy to track activities or outputs, understanding whether meaningful change is happening over time is far more difficult.
The Roadmap to Social Impact is a practical guide to social impact measurement, designed to help you better plan, measure and communicate the change you’re helping create.
We hope it provides both guidance and inspiration as we work together for a better world…(More)”.
Article by Juan Ortiz-Freuler: “This article reveals significant market consolidation within the digital infrastructure of Latin American media. Extending the political economy of media literature from ownership to control over underlying infrastructure, it uses a newly constructed database of over 400 data points to analyze the online infrastructure of 18 media outlets across 6 Latin American countries, focusing on 11 key elements of the media stack. Findings indicate that dominant players, such as Alphabet and Meta, pose the greatest commercial risks to the analyzed media. Meanwhile, the U.S. government emerges as the greatest geopolitical risk, with 50–100% of providers across the analyzed elements operating under U.S. law, exposing Latin American media with similar infrastructural profiles to U.S. government policy. The article places these challenges in conversation with historical calls for a New World Information and Communication Order, which underlines that a robust understanding of media autonomy requires infrastructural autonomy…(More)”.