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Springer Nature announces new transformative agreement with University of Texas System. New York, NY - 17 September 2026. Springer Nature has signed a transformative agreement with the University of Texas System, expanding opportunities for researchers to publish their work open access while ensuring access to Springer Nature content. The agreement qualifies University of Texas System researchers to publish open access, articles that are accepted in the Springer Nature hybrid portfolio, helping their findings be discovered and built upon by the global research community. The deal deepens a 20-year relationship with the University of Texas System. The four-year agreement supports both read access to subscription content and open access publishing, and includes titles across Springer, Palgrave, and Adis brands. The agreement is expected to enable the open publication of nearly 900 articles per year. By removing the barrier of cost to eligible researchers, University of Texas-affiliated researchers will be able to increase the reach and vsibility of their work[1], advancing scientific discovery. Maria Lopes, Vice President, Institutional Sales for Springer Nature, said: "This agreement reflects the strong partnership between Springer Nature and the University of Texas, one founded on a commitment to publishing high-quality research. By making open access publishing easier and more accessible, researchers at the University of Texas will be able to share their findings and contribute to the scholarly discourse of their disciplines." Alexia Thompson-Young, Assistant Director of Scholarly Resources, University of Texas, stated: "We are pleased to partner with Springer Nature to give University of Texas researchers a greater opportunity to publish their work openly and increase the visibility of the research produced at our UT institutions. This agreement helps to ensure that the discoveries made by our researchers have a global impact." [1] When publishing gold OA, authors across all disciplines see almost seven times more downloads per research article than non-OA articles and 50% more citations on average than non-OA articles. Springer Nature. (2026). From access to impact: How open access shapes research reach, citations, and global visibility. About Springer Nature. Springer Nature is one of the leading publishers of research in the world. Library Technology publish the largest number of journals and books and are a pioneer in open research. Through its leading brands, trusted for more than 180 years, Library Technology provide technology-enabled products, platforms and services that help researchers to uncover new ideas and share their discoveries, health professionals to stay at the forefront of medical science, and educators to advance learning. Library Technology is proud to be a progress, working together with the communities Library Technology serve to share knowledge and bring greater understanding to the world. For more information, please visit about.springernature.com and follow Springer Nature. | Summary: | Springer Nature has signed a transformative agreement with the University of Texas System, expanding opportunities for researchers to publish their work open access while ensuring access to Springer Nature content. The agreement qualifies University of Texas System researchers to publish open access, articles that are accepted in the Springer Nature hybrid portfolio, helping their findings be discovered and built upon by the global research community. | | Publication Year: | 2026 | | Type of Material: | Press Release | | Language | English | | Date Issued: | September 22, 2026 | | Publisher: | Springer Nature | | Company: | Springer Nature | | Permalink: | https://librarytechnology.org/pr/32946/springer-nature-announces-new-transformative-agreement-with-university-of-texas-system | DocumentID: 32946 views: 154 Created: 2026-09-21 10:30:41 Last Modified: 2026-09-21 21:41:46.

India's AI boom confronts its missing institutions. * PublishedSeptember 20, 2026 India's artificial intelligence ambitions have run into a hard question, not whether the country can build the technology, but who is answerable when it fails. At the inaugural Science on the Raisina - The Sakshya Dialogue 2026, convened by Springer Nature under the theme "From Guidelines to Institutions: Building India's Trusted AI Ecosystem," policymakers, scientists, technologists and industry leaders arrived at a blunt consensus: India does not have a shortage of AI guidelines. It has a shortage of institutions to enforce them. "Technology cannot be held accountable. People and institutions can." This formed the foundation of the discussion and captured its central argument - a sharp distinction between building AI responsibly and being responsible for AI. The two are not the same, participants said, and India's policy conversation has so far focused overwhelmingly on the former. Participants pushed back on the notion that AI's trust problem is a matter of public perception. It is structural, they argued. Trust is a downstream effect of accountability, and accountability requires knowing precisely who trained a model, on what data, and who is on the hook when it fails. The example offered was a familiar one: a doctor using an AI diagnostic tool has no reliable way of knowing whether it was trained on peer-reviewed medical literature or scraped social media commentary. In most cases today, that information simply isn't available. A recurring theme was the absence of independent evaluation. Academics who build AI models should not be the ones benchmarking them, participants said, and companies claiming state-of-the-art performance across dozens of Indian languages should not have the final word on whether those claims hold up. What's missing, the Dialogue concluded, isn't another guideline, it's an independent, conflict-free benchmarking body, potentially anchored in India's own AI Safety Institute. This was described not as a gap in policy documents but as a genuinely missing institution. Participants were clear they did not want a single, omnibus AI law racing to keep pace with a fast-moving technology. Legislation, they argued, inevitably moves slower than the technology it seeks to govern, and heavy-handed overreach risks stalling the innovation India is trying to build. The consensus instead favoured a federated, sectoral, risk-graded approach, AI used in healthcare warrants a fundamentally different level of scrutiny than AI recommending a film. This was framed explicitly as calibration, not deregulation. The more immediate priority, participants said, is not fresh guidance telling developers what to aspire for, but testing frameworks robust enough for government to deploy AI models at scale with actual confidence. A distinctly India-specific problem also emerged: the country doesn't lack good AI pilots, it lacks the institutions to scale them responsibly. Successful, localised projects, including an agricultural advisory tool for farmers and a maternal health risk predictor, have proven themselves in one state and then stalled for want of a sectoral data layer, a model registry, or a trusted mechanism to certify that a model safe in one state is safe to deploy in another. Bhashini, India's multilingual AI translation platform, was repeatedly cited as the model of what the country already has and can build on. Spanning India's 22 scheduled languages and their many dialects, it now handles 22 million inferences a day, supporting everything from digitising panchayat meeting minutes to delivering real-time agricultural advice to farmers in their own language. Its real lesson, participants said, is institutional rather than purely technological. Bhashini democratised data collection by partnering with more than 70 research institutions to gather and vet language data that no single Delhi-based team could have collected alone. It also embraced iteration over perfection - building, learning, failing fast and rebuilding - rather than waiting for a flawless first version. That combination of broad institutional participation and tolerance for early error was held up as a potential foundation for a trusted, federated AI ecosystem more broadly. Beneath the institutional debate sat an uncomfortable fact: India continues to underfund foundational AI research, and industry too often treats R&D spending as a cost centre rather than a long-term investment. Participants noted candidly that no Indian company has yet made significant money from AI, a fact that only strengthens the case for evaluation frameworks that account for the true cost of building and deploying a model, rather than relying solely on headline accuracy. Proposed fixes were practical rather than sweeping: CSR-funded research grants, secondment programmes rotating industry talent through public institutions, and stronger academic publishing standards to close the funding and talent gap. Geopolitics loomed large over the Dialogue. With AI increasingly framed as a contest between the United States and China, several participants argued India has a genuine opening to offer a third way, one rooted in its credibility on digital public infrastructure (DPI) and its standing with the Global South. But the room was candid about the gap between admiration and adoption: goodwill toward India's DPI experience has not yet translated into Global South countries choosing Indian AI models over Chinese ones. There was also no single agreed definition of "sovereign AI", with its importance potentially varying between a compute provider and an ordinary enterprise. What was broadly agreed, however, was the direction of travel: manage dependencies and choose partners deliberately in the near term, while keeping sovereign capability the long-term goal. Nobody at the Dialogue called for India to ease off its AI ambitions. The call, instead, was to build what's missing, registries, independent benchmarking bodies, sectoral regulators and funding incentives, the institutional scaffolding needed to convert ambition into infrastructure the country, and the world, can actually trust. India has the talent. It has the data scale, and through Bhashini, it already has a working proof of concept for responsible, democratised AI infrastructure. What it does not yet have is the institutional architecture to match, and turning principles into practice, the Dialogue made clear, is the work that starts now. Disclaimer: The views expressed in this article are those of the author/authors and do not necessarily reflect the views of ET Edge Insights, its management, or its members.