The consortium in question was assembled to investigate the upstream determinants of non-communicable disease in urban settings, drawing on 57 academic staff across six universities and spanning thirteen disciplinary areas grouped under the UK’s three main research councils: economic and social sciences, engineering and physical sciences, and medical research, plus a professional services team. From 2019 to 2025, a dedicated research-on-research sub-team conducted 63 semi-structured interviews with 39 consortium members in three rounds, timed in Years 2, 3 and 5 of the programme. Those transcripts, normally the raw material for slow, expert-led qualitative analysis, became the test bed for a very different kind of scrutiny.

The researchers adopted what they call a dual approach. The first strand used co-word clustering, an NLP technique that groups words appearing together across texts, to identify emergent themes in the full interview transcripts. The second strand was more targeted: the team built a dictionary of 245 tokens deemed to represent transdisciplinary language, extracted from foundational project documents including the peer-reviewed programme protocol, and then tracked how those words were used over time and across disciplinary boundaries. The dictionary was split into tokens describing the upstream problem under investigation, the mid-downstream built environment and health outcomes, and the research problem itself, that is, the challenge of actually running such a complex collaboration.

The co-word clustering produced eleven initial clusters, refined down to nine coherent themes, ranging from new large-scale research approaches for real-world problems to local government case studies, health and decision-making, mission and integration, shared understandings, project structure, and urban development systems. Yet despite the apparent richness of these themes, the technique yielded no clear findings about how transdisciplinary understanding developed. The authors are candid about this: the themes could be described and tentatively interpreted, but whether those interpretations were accurate or merely what they call wishful thinking remained uncertain, particularly given the low volume of data and the wide variation in the number of tokens within each cluster.

The vocabulary analysis told a far more compelling story. In the first interview round, almost half of the 245 transdisciplinary tokens were not used by any of the disciplinary groups, and most of the rest were used by only one, two or three of the four groups rather than all of them. Over the six years, however, a clear convergence emerged. Overall use of the transdisciplinary vocabulary declined, but the words that remained in circulation became more widely shared. By the end, 135 words were used by at least one discipline, 40 by all four, and the trajectory pointed towards a shared lexicon of roughly 15 to 25 percent of the original dictionary. In other words, the team did not adopt more jargon as it matured; it shed most of it, converging on a small set of useful, jargon-free terms.

The disciplinary breakdown added intriguing nuance. Economic and social scientists used the greatest diversity of transdisciplinary vocabulary in the first round, across by far the widest range of interviews, at 45 percent across 15 interviews, falling to 30 percent and then 23 percent in later rounds. Public health researchers used the second most, followed by engineers, with professional services staff using the least, and none at all in the first round. That ordering is striking because the engineers were arguably the group most familiar with the vocabulary from the outset, having led the systems science and research-on-research work. One explanation offered is that the engineers focused on process-oriented systems analysis rather than the wider research themes; another is that, being most fluent, they simply expressed the same ideas in different words.

Perhaps the most consequential finding is about time. It took the consortium essentially the full six years to develop its shared vocabulary, and the convergence accelerated only towards the end of that timeline. The authors note that the team once spent six months of considerable discussion simply agreeing what the word health meant. For funders and research leaders, the implication is uncomfortable but valuable: the development of shared understanding in large, newly formed transdisciplinary teams may not be easily short-cut. It is a matter of exposure and accumulated interaction, and expectations of rapid conceptual alignment should be managed accordingly. The extensive early management activity in this consortium, including co-developing a glossary, adopting mission-orientation, and building shared conceptual mental models, may have helped, but it may also have attempted to fast-track a process that could not be forced.

The study is equally instructive about the limits of NLP itself. The approach was labour intensive, combining automated analysis with manual, expert-led contextual interpretation, and the authors judge that it could not have provided rapid course corrections during the live programme. Interviewer words were included in the data, interview questions varied between rounds, the consortium restructured between phases, and normalisation across unevenly sized disciplinary groups posed non-trivial challenges. Any confident use of such methods, the authors argue, would require a dedicated validation exercise, ideally involving the interview participants themselves, along with minimum thresholds for token numbers and theme coherence.

Still, the authors see genuine promise. If datasets are larger, better formatted, and analysed with more efficient scaling, NLP could surface patterns that traditional qualitative analysis misses, and its semi-automated nature may reduce researcher bias. The clearest lesson, that shared language in transdisciplinary science takes years rather than months to emerge, and that less jargon ultimately proves more useful, is one that any team hoping to solve complex global challenges would do well to hear before the funding clock starts ticking.

Subject of Research: Using natural language processing to assess the development of shared transdisciplinary understanding in a large research consortium

Article Title: Using Natural Language Processing (NLP) to assess changes in transdisciplinary understandings across a large research consortium

Article References: Black, D., Gopsill, J., Rosenberg, G., Silvonen, T., Kukreja, A., & Hicks, B. (2026). Using Natural Language Processing (NLP) to assess changes in transdisciplinary understandings across a large research consortium. PLOS Sustainability and Transformation, 5(10), e0000222. https://doi.org/10.1371/journal.pstr.0000222

Image Credits: AI Generated

DOI: 10.1371/journal.pstr.0000222

Keywords: natural language processing, transdisciplinary research, interdisciplinary collaboration, research-on-research, co-word clustering, shared vocabulary, research consortium, team science, urban health, research evaluation, qualitative interviews, PLOS Sustainability and Transformation

Meta-research, often described as research on research itself, encompasses the systematic study of research practices, methodologies, and reporting standards. Given the increasing complexity and interdisciplinary nature of scientific investigations, improving peer review mechanisms for meta-research is crucial. By integrating with MetaROR, PLOS Biology pioneers a seamless process where manuscripts undergo peer review on the MetaROR platform before being considered for publication. This approach not only accelerates the trajectory of manuscripts through the editorial pipeline but also significantly enhances the transparency of the peer review process, traditionally cloaked in anonymity and limited accessibility.

The partnership embodies a shared vision among PLOS Biology, RoRI, and AIMOS to drive meaningful reforms in research publishing. Central to this shared mission is the adoption of the publish-review-curate paradigm. This model challenges conventional peer review cycles by allowing authors to utilize already obtained rigorous peer-review reports when submitting their work to partner journals. In practice, this reduces redundant reviewing efforts and eliminates repetitive review rounds, which often delay publication and drain valuable time and resources from both authors and reviewers. For a complex and evolving field like meta-research, such efficiency gains can accelerate dissemination and encourage broader community engagement.

PLOS Biology Editor-in-Chief Nonia Pariente expressed enthusiasm about the collaboration, emphasizing the journal’s dedication to advancing open science principles. “As a strong advocate for meta-research, we are pleased to become a partner journal of MetaROR, which will enable us to support this community while advancing Open Science practice,” Pariente stated. She highlighted PLOS Biology’s commitment to reusing pre-existing reviewer reports as a strategy to streamline the publication process and foster a culture of transparency. This partnership, therefore, aligns perfectly with PLOS Biology’s values of inclusivity, accountability, and innovation in scientific communication.

From MetaROR’s perspective, Ludo Waltman, Co-Editor-in-Chief of MetaROR and Co-Chair of RoRI, acknowledged PLOS Biology as one of the earliest adopters to join this transformative effort. “We are grateful for PLOS’ confidence in MetaROR’s publish-review-curate model,” Waltman remarked. He underscored the collaborative potential this partnership creates for driving innovative publishing practices that prioritize openness and efficiency over traditional, often opaque, procedures.

MetaROR’s architecture reflects a breakthrough in scholarly communication technology. It combines a transparent, community-driven peer review system with a curation mechanism akin to post-publication peer review and editorial commentary. This multifaceted framework not only enhances the visibility of the peer review process but also facilitates critical appraisal and reuse of meta-research outputs. By embracing this approach, the scientific community gains access to a richer, more nuanced understanding of how research itself is conducted and validated, fostering improvements in reproducibility and research integrity.

The founding institutions behind MetaROR bring complementary expertise to this endeavor. Established in 2019, RoRI focuses on accelerating research into the very systems and cultures governing scientific inquiry and decision-making. Meanwhile, AIMOS serves as a hub for interdisciplinary researchers committed to enhancing the quality of scientific practice through meta-research and open science interventions. The synergy of these organizations with PLOS Biology’s high-impact publishing platform foregrounds meta-research as a vital and maturing field, deserving attention and rigorous evaluation akin to traditional scientific disciplines.

This partnership also advances the broader agenda of the Public Library of Science (PLOS), a nonprofit organization dedicated to transforming scientific publishing through open access and systemic reform. PLOS champions a future where scientific knowledge is openly accessible, and where the processes shaping this knowledge are as transparent as the science itself. By partnering with MetaROR, PLOS Biology reinforces its mission to integrate open science principles directly into the editorial and peer review workflows, creating virtuous cycles of innovation and trust between authors, reviewers, and readers.

Technically, the integration with MetaROR allows PLOS Biology to receive manuscripts complete with detailed peer-review dossiers from the platform. Editors can then make more informed decisions based on comprehensive and accessible critiques, shortening the time to publication while maintaining the rigor and quality of the review. This model circumvents traditional barriers commonly faced during manuscript resubmissions to different journals, such as the need to initiate new review cycles from scratch. Consequently, the workflow enhances transparency, reduces reviewer fatigue, and elevates the overall quality and reliability of published meta-research studies.

Moreover, the transparent peer-review philosophy embraced by MetaROR resonates with ongoing global conversations around peer review openness. By making reviews structured, publicly accessible, and linked directly to manuscripts, MetaROR illuminates the scholarly dialogue underpinning research development. This openness fosters accountability and constructive community engagement, which are critical in an era where reproducibility crises and publication biases challenge the integrity of scientific literature.

The partnership’s impact extends beyond meta-research itself to influence the broader academic publishing landscape. Through collaboration, PLOS Biology, RoRI, and AIMOS aim to inspire additional journals to adopt open review models and explore interoperable platforms like MetaROR. This momentum aligns with an emerging ecosystem of open science infrastructure designed to reduce inefficiencies and promote interoperability, data sharing, and collaborative knowledge production across disciplines.

In summary, PLOS Biology’s alliance with RoRI and AIMOS to support the MetaROR platform represents a pivotal moment in the evolution of scientific publishing. By ushering in a new era of transparent, efficient, and community-driven peer review for meta-research, this partnership lays a foundation for improved research integrity, accelerated knowledge dissemination, and enhanced collaboration across the scientific enterprise. As the publishing community grapples with balancing quality, speed, and openness, initiatives like MetaROR, backed by leading journals such as PLOS Biology, exemplify the transformative potential of innovation rooted in open science values.

Subject of Research: Meta-research, peer review transparency, scientific publishing practices

Article Title: PLOS Biology Partners with RoRI and AIMOS to Boost Meta-Research Transparency Through MetaROR

News Publication Date: Not specified

Web References: dknutson@plos.org, waltmanlr@cwts.leidenuniv.nl

Keywords: Scientific publishing, Scientific organizations, Research organizations, Peer review, Open access