Research strategy deserves better than a chatbot: Why Dimensions Research Strategy is different from other AI tools in research

There is no shortage of AI tools promising to transform research. Barely a week goes by without another launch – another platform powered by a large language model, another interface built on public data, another chatbot that will supposedly revolutionize how we work. And there is no doubt that they can be very useful tools; this blog, for example, is written in partnership between a human author and AI.

We want to explain why we think Dimensions Research Strategy is different to other AI tools in research.

The problem with most AI tools for research

The challenge with general-purpose AI in research strategy isn’t capability: it’s the lack of provenance and domain-level depth. Ask a large language model a question about your institution’s competitive position in oncology research, and it will give you a confident, plausible answer. But, it won’t give you an answer that shows an understanding of how actors in the research environment think about these problems; it won’t be able to tell you the assumptions that are implicit in how it arrived at its analysis and, most importantly, it won’t give you provenance or reproducibility.

It can’t tell you which data it used, whether that data are current, how it weighted different signals, or whether its methodology reflects how a skilled research analyst would actually approach the question. The answer sounds right at first sight, and it’s confidently given. You just can’t defend it when someone asks you to. 

This is a problem we’ve all encountered with chatbots.

The minor errors that slip into analyses – unchecked or unfounded assumptions – perhaps don’t matter too much when you’re just asking for a recipe or collecting actions from a daily meeting you missed, but they are foundational when determining a research strategy, portfolio analysis or when considering a tenure-track proposal. In these situations, AI must be used carefully, in a limited way. AI has tremendous promise to help in situations of cognitive overload, and so the foundations on which it is built matter enormously when the question has deep consequences for a person, a department, a strategy, a research program or even a research field.

Why Dimensions Research Strategy is different: every finding traceable to source

This is the thing we think will matter most to the people who will actually use this, and the thing we’ve seen really resonating in our discussions with the community: the provenance of every piece of data.

When a Vice-Chancellor receives a Dimensions Research Strategy analysis during a meeting with senior colleagues to discuss their research strategy, they need to know that every number in it can be challenged and defended. So we’ve made sure that every finding in Dimensions Research Strategy links back to its primary data source. Every analysis is auditable and repeatable, giving you an evidence chain you can check – rather than a “thin” report built on potential hallucinations without a route to go deeper to understand the underlying facts.

That standard of analytical rigour used to be available only to institutions with the budget for dedicated research analysts. We think every research organisation should be able to access the best strategic intelligence. 

What a decade of real analytical work looks like, encoded

Digital Science has spent over a decade doing this work for the research community by hand.

Our research analysts have delivered bespoke strategic intelligence for some of the world’s leading organisations: impact evaluations, research landscape analyses, strategic benchmarking, faculty recruitment intelligence. We have experience working with clients around the world to solve some of the hardest research analytics problems in the sector.

What Dimensions Research Strategy encodes is the thinking behind that work: not the outputs themselves, but the patterns and commonalities which surfaced over time, aligned with the accumulated judgment of domain experts who understand not just how to query data, but know the personality, caveats and – yes – even the failure-points of that data intimately. 

The result is not a large language model trained on public text and pointed, unseeing, at a research question. Nor has any of the analysis our clients have received been used in training an LLM. It is AI built from the inside out, from a decade of real analytical practice in the research intelligence domain, by the people who have been doing this work for the world’s leading research organizations. 

Research strategy doesn’t fit in easy categories

We also designed Dimensions Research Strategy to solve a frustration that comes up in every conversation we have with the research community. Existing tools impose their own topic structures: fixed classification systems that don’t map to how institutions actually think about their research. 

Rather than asking institutions to work within our categories, Dimensions Research Strategy lets you define the research areas that matter to you, and reasons from there. The analytical framework bends to the question, not the other way around.

The underlying data matter too – and it’s these data that are Digital Science’s heritage. Dimensions Research Strategy is built on Dimensions and Altmetric data combined: publications linked to grants, patents, clinical trials, policy documents, and real-world attention signals. 

That means: 

• 165M+ publications
• 41M+ researcher profiles
• 8M+ grants
• 256m+ mentions of over 24 million research outputs
• 180M+ patents
• 5bn+ connections between research objects 

Of course, data means nothing without the connections between it. When you are asking complex questions that will drive critical decisions across research strategy, those connections are where the insight lives. Dimensions and Altmetric bring together more connections in a single, verified database than any other provider – so whether you’re asking about the impact of your research (connections from publications to patents, clinical trials, and policy) or the efficacy of your funded research (connections from grants, through publications to impact), the full picture is there. No other system brings those connections together at this scale and with this level of verification, in one centralized platform. 

So when you ask Dimensions Research Strategy how your institution’s translational medicine capability compares to your peer group, it’s drawing on a picture that includes not just what researchers have published, but what they’ve been funded to do, what’s made it into clinical trials, what’s been cited in government policy, and what’s generating public attention. The whole research landscape, navigated with context. 

Until now, assembling that picture – across publications, grants, clinical trials, patents, and policy documents and all the links between them – for a single strategic question can take a skilled analyst weeks, months or not even be possible. Dimensions Research Strategy aims to change that so that anyone can create reproducible, interrogable analyses which an organization can leverage with confidence – because they can see every step. 

What it will do

At launch, Dimensions Research Strategy will deliver two workflows designed for senior research leaders.

1. The first is Benchmarking.
   What we’ve heard from early development partners is that the most important feature isn’t the comparison itself – it’s being able to define the research areas that matter to your institution, rather than working within the fixed topic structures that existing tools impose. So rather than measuring your institution against someone else’s metrics, Research Strategy lets senior leaders define their own peer group – the institutions they actually compete with – and run a multi-dimensional comparison across research output, citation impact, collaboration reach, funding competitiveness, and societal engagement. The analysis is interrogable and iterative: ask a follow-up question, adjust the comparison, drill into a specific discipline. The value isn’t in knowing where you rank in absolute terms; it’s in understanding your relative position against the institutions that actually matter to your strategy.

2. The second is Expertise.
   Understanding who is working in a specific research area – globally, at peer institutions, or within your own institution – is a question that comes up constantly in strategic planning, and one that currently takes days of manual work to answer with any confidence. Dimensions Research Strategy surfaces that picture in a single session. Organizations can map researcher expertise across output profile, collaboration network, field standing, and real-world impact, to understand the full landscape of who is working in areas that matter to their strategy, and identify expertise that complements their existing capabilities. The insights inform the process, while the decisions remain with the humans who hold the true strategic judgment. 

These are the first two workflows on our roadmap – but they are far from the last. Research impact, funding intelligence, grants preparation, research integrity and security: each of these is a challenge we hear raised by the research community, and each is a space we can see tremendous opportunities in. The sequence and the timing of these will be shaped by ongoing conversations with our Development Partners and Early Adopters. But the direction is clear: a platform that gives every research institution the strategic intelligence it needs, across every dimension that matters. 

If you’re interested in becoming an Early Adopter, sign up to our launch newsletter here. 

Do we see this replacing analysts?

In a word: No.

Every organization we’ve spoken with so far has a highly capable analyst working at the centre of this – and in every case, the bottleneck isn’t skill. It’s capacity. 

In the same way that this blog was written in collaboration between human and AI, we see the role of the human analyst as evolving from data wrangler to strategic partner. The human co-author of this article has spent the time allocated to this task editing, shaping, sense-checking, cross-interrogating. Her role has changed, but remains essential to the success of the task.

Whether you’re a head of department, research manager, or librarian taking your first steps in research analytics, or a team of experienced analysts looking to go further and faster – Dimensions Research Strategy is built for both. In the first case, it puts rigorous, evidence-backed analysis directly in your hands, built on a decade of expertise you wouldn’t otherwise have access to. In the second, it removes the weeks of manual work that stand between a question and an answer, freeing your team to focus on what only they can do. 

With Dimensions Research Strategy, the analysis that used to take a week can now take minutes. What you do with the time that frees up becomes the next interesting question.

Strategic expertise across every dimension that matters. On demand, for every institution.

Stay informed – sign up to our launch newsletter

If you’d like to stay informed about availability and early access opportunities, sign up here. 

The post Research strategy deserves better than a chatbot: Why Dimensions Research Strategy is different from other AI tools in research appeared first on Digital Science.

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REF readiness: evidencing Strategy, People & Research Environment

In the first two posts in this series, we explored the evidence challenges around Engagement & Impact and Contribution to Knowledge & Understanding. Here, we turn to the third element of REF 2029 assessment: Strategy, People & Research Environment (SPRE) and what it takes to approach it with both credibility and confidence.

SPRE is often treated as the administrative pillar of REF, the section concerned with governance structures, research culture, and support systems. REF 2029 reflects a broader shift in research evaluation, placing emphasis on the conditions that enable excellent research, engagement and impact. Shaped by extensive sector consultation and piloting, the assessment now places greater value on institutional context, strategic intent, and evidence of effectiveness, recognising that positive research culture takes different forms across institutions and disciplines. 

In practice, SPRE is considerably more demanding than the traditional framing suggests. Panels assessing SPRE look for institutions to articulate a coherent and credible account of how their research environment has developed, how it supports researchers at all career stages, and how institutional strategy is shaping the conditions for future research excellence.

That requires not just a well-written narrative, but one that can be traced back to evidence: investment patterns, collaboration networks, career development activity, researcher demographics, and infrastructure choices. Without that evidence base, even a well-constructed SPRE narrative risks appearing aspirational rather than grounded.

“The SPRE element is where institutions have to show that their environment is genuinely enabling their research, not just describe it. Panels can distinguish between a narrative that reflects a well-evidenced reality and one that is largely rhetorical,” explains Ann Campbell, Director Research Impact & Comparative Analytics at Digital Science.

Evidencing strategy and environment credibly

SPRE narratives are expected to show how strategy has shaped research activity over the assessment period and how it will continue to do so. That means institutions must position their strategic claims within a broader context: how their research portfolio benchmarks against institutions of similar profile, where they have invested relative to emerging fields, and where their environment has demonstrably strengthened research quality.

Dimensions data supports this kind of strategic evidence building by enabling institutions to monitor research activity, collaboration patterns and emerging strengths over time. Rather than focusing solely on comparison with peers, these data can help institutions assess progress intrinsically, providing evidence of how strategic priorities, investments and interventions have influenced research behaviours and contributed to the development of the research environment. For example, an institution seeking to create a more collaborative and externally connected research environment might examine changes in international and academic–industry collaboration over time. Dimensions enables institutions to track indicators such as the proportion of publications involving external partners, industry co-authors, or interdisciplinary teams, helping to build evidence of how strategic priorities have influenced collaboration behaviours and the wider research environment. 

While no single metric can demonstrate the effectiveness of a strategy in isolation, trends in collaboration activity can provide valuable supporting evidence when considered alongside institutional priorities and reported activity.

Ulster University: research collaboration trends, 2014-2025. Source: Dimensions.

A practical example can be seen at Ulster University. The proportion of publications involving international collaborators increased from 49.8% in 2014 to 63.7% in 2025, while the share involving industry collaborators rose from 2.4% to 4.6% over the same period. Looking more closely at Information and Computing Sciences and Engineering – two areas aligned with strategic investments in Digital Healthcare, Artificial Intelligence, Advanced Manufacturing and the Belfast Region City Deal – publication output and engagement with both international and industry partners increased substantially over time. While these indicators do not demonstrate causation, they are consistent with a research environment that has become increasingly connected and externally engaged. 

This is not about selecting evidence to fit a narrative. It is about building a sufficiently complete picture that the narrative reflects what the evidence actually shows – and that the gaps, where they exist, are understood and addressed before submission.

Mapping collaboration and demonstrating connection

Collaboration indicators can help evidence the effectiveness of institutional strategy, but collaboration is also a significant area of assessment in its own right within SPRE. Institutions must demonstrate not only that collaborations exist, but that they have created conditions enabling meaningful engagement across disciplines, sectors and international boundaries.

REF panels look for evidence of meaningful engagement with the wider research community, nationally, internationally, and across disciplinary boundaries. The challenge is that collaboration evidence is frequently scattered. Co-authorship data may be held in multiple systems. International partnerships may be documented in grant records but not linked to publications. Interdisciplinary work may not be captured consistently within any single Unit of Assessment.

“The institutions that can tell the clearest story about their collaboration environment are the ones that have been able to assemble a connected picture – not just outputs and grants in isolation, but how those things link to partners, to networks, to the world beyond the institution. That connected view is rarely available without deliberate investment in data infrastructure,” says Campbell.

Dimensions enables institutions to map collaboration networks at unit, discipline, or institutional level, surfacing international partnerships, cross-sector engagement, and interdisciplinary links in a way that supports both narrative construction and internal verification. Symplectic Elements complements this by structuring the local evidence layer: linking researchers to their outputs, grants and activity records in a way that is auditable, consistent, and provides the data governance that REF submissions require.

Career development, researcher wellbeing and institutional assurance

A significant strand of SPRE assessment focuses on how institutions support researchers across the career lifecycle: early-career researchers, those on fixed-term contracts, researchers returning from career breaks, and those working in interdisciplinary or applied fields that may not map neatly onto traditional academic career structures.

Panels expect evidence that institutions have thought carefully about equity and inclusion in their research environment, not as a compliance exercise, but as something visible in both data and narrative. That means demonstrating that early-career researchers are represented in outputs, that career development provision reaches those who need it, and that the research environment does not systematically disadvantage particular groups.

Institutional self-awareness matters as much as institutional performance. The ability to identify where evidence falls short, where coverage is uneven, or where certain groups are underrepresented is itself a sign of a credible, well-governed research environment.

SPRE submissions span multiple Units of Assessment, and each must cohere with the institution-level narrative. The risk of inconsistency, between institutional claims and unit-level evidence, or between different units’ accounts of shared infrastructure, is a practical one that affects many institutions.

As Campbell notes, “One of the things that makes SPRE preparation genuinely hard is that it requires consistency across the whole submission, not just excellence in individual parts. An institutional narrative that isn’t grounded in unit-level evidence, or vice versa, creates credibility gaps that panels notice.”

Good institutional assurance also requires a clear view of metadata quality and completeness. Where records are incomplete, inconsistently attributed, or missing key linkages, between researchers and outputs, grants and the work they funded, international partners and the collaborations they enabled, submission quality risks become difficult to manage without coordinated data governance.

Symplectic Elements supports institutional assurance by providing structured, auditable records of researcher activity, linking people, outputs and grants in a way that enables governance teams to identify gaps and inconsistencies before submission. Combined with the external validation layer that Dimensions provides, this gives institutions a more complete and more defensible evidence base for the decisions they need to make.

Open research as part of the environment narrative

REF 2029 places increased emphasis on open research practices as a feature of a healthy research environment. Institutions that can demonstrate strong open access compliance, a culture of data sharing, and investment in open research infrastructure are better placed to present a credible SPRE narrative.

Open research infrastructure, including institutional repositories such as Figshare, plays a practical role here. Persistent identifiers, FAIR-compliant data repositories, and transparent access to research outputs and datasets are not simply technical features. They are evidence of the kind of open, well-governed research environment that panels expect institutions to be building and sustaining.

Scholarly visibility tools such as Altmetric can provide supplementary evidence of how research outputs are engaging public, policy and professional communities – a useful addition to SPRE narratives that address engagement beyond the institution.

Institutions that prepare most effectively:

• benchmark research performance and strategic positioning using interconnected data solutions such as Dimensions, tracking trends over time and in disciplinary context
• map collaboration networks, nationally, internationally and across disciplines, to evidence the reach and depth of their research environment
• structure researcher activity records and governance through systems such as Symplectic Elements, linking people, outputs and grants with auditable consistency
• demonstrate open research practices through compliant, FAIR-ready infrastructure such as Figshare, making the breadth of research outputs visible and accessible
• use supplementary visibility data from tools such as Altmetric to situate research engagement within public, policy and professional discourse

REF readiness is about leading, not lagging. For SPRE, that means investing in the data infrastructure and governance practices that make the environment narrative credible, not constructing it retrospectively from incomplete records.

Digital Science supports this readiness through interconnected solutions that strengthen the evidence base for decision-making, while leaving judgement firmly with institutions and REF panels.

Digital Science can help your institution build a credible, evidence-grounded SPRE narrative for REF 2029.

Book a call to explore how.

The post REF readiness: evidencing Strategy, People & Research Environment appeared first on Digital Science.

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Asha G Receives Best Researcher Award

 

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Semra TEBRİZCİK Receives Innovative Research Award

 

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Jonathan Dior Nima Ngapey receives Innovative Research Award - Global Scholar Awards

 

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♻️ Synergistic Co-Upcycling of Polycarbonate and Organophosphate Ester Wastes via Chemically Complementary Reactivity

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Xiaoguang Huang | Pharmacology | Innovative Research Award

Xiaoguang Huang is affiliated with Guangzhou Baiyunshan Tianxin Pharmaceutical Co., Ltd in China and contributes to research activities within Pharmacology. His academic profile demonstrates participation in scientific publications, citation visibility, and pharmaceutical research engagement. These scholarly activities support recognition within the Global Scholar Awards and reflect continued involvement in pharmacological and healthcare-related scientific advancement.
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