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Hungary moves to overhaul university models banned from Horizon Europe
Hungary’s new government is aiming to overhaul the governance model of the institutions and universities that lost access to Horizon Europe and Erasmus+ funds “in a short timeframe,” according to Péter Horváth, the new state secretary for science policy and innovation. “As of today, the two non-university foundation-controlled institutions under my remit have been transformed,” he told Science|Business. “We hope the universities will follow and that this will be enough to satisfy the EU’s demands.”Under the previous government, led by Viktor Orbán and his Fidesz party, many academic and scientific institutions were restructured and made into special public trust foundations, with members strongly linked to the government given lifelong mandates and significant decision-making powers with little oversight. The European Commission deemed that this undermined academic freedom and transparency in the country, leading to a ban on 34 institutions in 2022, including 21 universities, which saw academics in Hungary lose access to Horizon Europe grants. The new Hungarian government, elected in May and led by Péter Magyar and his Tisza Party, has promised to dismantle this governance model and restore the institutions to EU programmes. As a result, the European Commission has promised to return access to the Erasmus+ education mobility programme, but so far no concrete agreement has been made on the Horizon Europe programme for research and innovation. Hungary has already made legislative changes in June to allow the institutions to be reformed and set a timeline for the implementation.The non-university foundations, which are mainly cultural centres such as the Foundation for Hungarian Culture or the Makovecz Campus Foundation, will all be dissolved by August 31 this year, a date tied to EU-set milestones that Hungary must meet to unlock frozen funds, with control transferred to relevant ministries. Meanwhile, the universities will have until 31 August 2027. For each university foundation, the government will set the dissolution date and the body to take over operations. There is no final decision on a permanent new model for the universities, according to Hungarian MEP Eszter Lakos, who is shadow rapporteur for the Horizon Europe specific programme, a separate piece of legislation detailing implementation rules for the EU’s next research programme, due to start in 2028. “The aim of the transition year is to settle this with [the universities], not to impose a model, and not to put them under state control,” she told Science|Business. “The goal is to give academic independence back to universities.”There are three models that are most likely to be implemented. One would be a return to a traditional, state-run model. Another could be a newer, more self-standing public institution. A third option would be a reformed version of the foundation models that meet EU standards. According to Lakos, the dismantling of the system put in place by the Orbán government is well underway and if all goes according to plan, Hungary could regain access to EU research money by the start of the next academic year in 2027. “I believe that the Hungarian government is doing the necessary changes so the universities could return as beneficiaries to Horizon from the next school year,” she said. In the meantime, “the universities, institutes and cultural assets carry on under proper public stewardship rather than a handful of unaccountable trustees,” said Lakos. The laws passed in June impose new safeguards: board terms are capped at four years and renewable only once, appointments are made through open competitions and sitting politicians cannot join joining these boards.It remains unclear whether a domestic science funding programme set up as a back-up scheme by the former government following the EU funding ban, called HU-rizon, could be continued. “This programme should never have had to have been established in the first place, but it has proven to be quite popular,” he said. “I am considering continuing this programme with a refined focus on helping international collaborations.”Re-organising the research network One of the first priorities for Horváth and his cabinet is to resolve questions over the future of the Hungarian Research Network, Hun-Ren. The network of academic institutes was taken away from the Hungarian Academy of Sciences in 2019 and turned into a quasi-private organisation, with similarities in its governance to the public trust foundations.Horváth and his team have consulted over 5,000 representatives from the network and the conclusion is broad unhappiness with the directors of all the institutes involved. “We have presented the results to the president and the chief executive of the network and this is currently being discussed,” Horváth said. The plan is to re-integrate the network back into the Academy of Sciences but in an improved way, “learning from mistakes of the past,” he added. The government also wants to reintegrate four humanities and social science research centres into the network that the previous government had transferred to Eötvös Loránd University. The transfer brought sharp criticism from the academic community, with a group of Hungarian research heads, including European Research Council grant recipients, sending an open letter to Orbán warning that the transfer would “cause historically significant harm to the entirety of Hungarian scientific life.” The transfer was “one of the biggest shames in Hungarian research in the past 20 years,” Horváth said. “We want to immediately re-integrate them back into the research network and to regain the trust of these institutions, because the way they were treated was really unfair.”Another pressing issue is the Élvonal Excellence Research and Talent Development Foundation, led by Nobel laureate Ferenc Krausz. The foundation signed a financing agreement with the former government earlier this year for a budget of over €730 million over the next five years. The goal is to create a world-class centre to attract top talent from abroad and to develop local researchers. But there was little transparency about this foundation, and news of its huge budget, which is nearly as much as Hungary’s entire research budget, caused a stir in the media following the recent elections. Another issue is that, as with the foundation boards of the institutes banned from Horizon Europe, there was little-to-no oversight of decisions led by Krausz. “It was an excellent idea but an unacceptable execution,” Horváth said of the foundation. “The fact that this is out of the Hungarian funding scheme, mostly concentrating on one person who decides on the budget is unacceptable for us.”The government plans to merge the centre with the National Research, Development and Innovation Office, the country’s R&D funding agency, with a reduced budget. Horváth is hopeful that Krausz will still be involved. The new government’s R&D goalsA phrase that crops up regularly during the discussion with Horváth is “democratic process.” The new state secretary is determined to involve Hungary’s research community in shaping future policies. This marks a departure from the previous government’s approach, and several people in Hungary’s research community have commented on the openness of the new government. In terms of concrete goals, Horváth and his team already have plans. He wants to create a new science fund in Hungary, based on the best practices of other European countries. The fund would not be tied to annual government budgets and would provide long-term, stable financing. “If we were to succeed in creating this fund, then I think it would be one of the biggest achievements for me in this period of government,” Horváth said. On the innovation side, he is looking into how best to bring in the private sector. “We want to make sure that we are working with the private sector to support innovation,” Horváth said. One of his big aims is to introduce entrepreneurship teaching into high schools. Hungary already has a reputable national academy offering research and innovation training in the field of natural sciences, and Horváth wants to expand this to all disciplines and across the whole country. “One of the main ways to elevate our innovative businesses is to teach our children to be innovative,” he said. “This will probably need ten years to pay off, but this is what we should do. Our aim is to make Hungary strong in our region, and this is what is needed.” Related articlesHungary’s Horizon Europe performance in numbersCommission unlocks Erasmus+ cash for Hungary, Horizon could be next‘Academic Huxit.’ Hungarian researchers speak out on impact of EU ban A more difficult task will be funding. The new government is aiming to increase research spending to 2% of the GDP by 2030, and then increase it to 3% in line with EU objectives. Currently, spending sits around 1.3%. But according to Horváth, the current budget, inherited from the previous government, is “heavily in the negative,” so any plans to increase research spending will have to start next year. Achieving 2% by 2030 already needs a big push, almost doubling the research budget. “This is something we’d definitely like to reach, and something I can see us achieving,” he said. “From the 2030s, I’d like to see an innovative generation growing up that can help us reach the 3% target.” Positivity in the new governmentDespite the many challenges facing Horváth and his team, the mood is positive. “It feels a bit like a start-up right now. Everyone is working 24/7, day and night, trying to do as much as they can. There is a lot of energy,” he said. It is a big change for the state secretary who, like many in his cabinet, is coming from a background in research or business, and not in politics. “Six weeks ago, I was sitting with my microscope and my research PhDs, so it’s been a big change for me,” said the computer scientist, who was previously the director of the Institute of Biochemistry at the Hun-Ren Biological Research Centre in Hungary and worked at the Institute for AI for Health at Helmholtz Munich. But he is relishing the task to transform the country’s R&D landscape. “We feel enormous support from the Hungarian people, but also from Europe,” he said. “We’ve received a lot of letters of support from around Europe. There is big trust there, and huge responsibility on our side. We want to get everything back on track, and there’s a lot to do.”
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Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
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