CASRAI Dictionary

Tag: research impact

  • Beyond the Case Study: Frameworks for Research Impact

    Funders and institutions increasingly ask researchers to demonstrate impact: the contribution of research to society, the economy, policy, health, culture, and the environment, beyond its contribution to scholarship itself. In the United Kingdom this is most visible in the impact case studies of the Research Excellence Framework. But the narrative case study, powerful as it can be, is only one instrument. A set of more structured frameworks, developed over years of methodological work, offers different ways to assess and evidence impact. Three stand out: the Payback Framework, SIAMPI, and contribution mapping.

    Why impact is hard to assess

    Research impact resists simple measurement for several reasons. It often unfolds over long and unpredictable timescales, so that the effects of a study may appear years or decades after publication. It frequently results from many contributions rather than a single project, making attribution difficult. And it travels through complex pathways, from a finding to a policy to a practice to an outcome, with each step shaped by factors outside the research itself. Any credible approach to impact assessment has to grapple with these problems of time lag, attribution, and causal complexity.

    The Payback Framework

    The Payback Framework, developed by Martin Buxton and Stephen Hanney, was among the first systematic attempts to assess the returns from research, originally in health services research. It combines a logic model of how research feeds through to benefits with a set of categories for the different kinds of payback a project can yield. These categories typically span knowledge production, benefits to future research and research use, political and administrative benefits, health and broader sector benefits, and wider economic benefits.

    The framework also describes the stages through which research passes, with interfaces where it interacts with the wider world, and feedback loops that recognise impact is rarely a straight line. Its strength is structure: it gives evaluators a consistent way to ask what kinds of benefit a piece of research produced and where, rather than relying on whatever story happens to be most compelling.

    SIAMPI and productive interactions

    SIAMPI, which stands for Social Impact Assessment Methods through the study of Productive Interactions, takes a different angle. Rather than trying to measure ultimate impacts directly, which is notoriously difficult, it focuses on the productive interactions between researchers and stakeholders that make impact possible. The reasoning is that impact arises through relationships and exchanges, such as collaborations, advisory roles, the use of research in practice, and the contributions can be observed and assessed even when the final outcomes are diffuse or delayed.

    SIAMPI distinguishes between direct or personal interactions, indirect interactions through texts and artefacts, and financial interactions. By looking at the quality and reach of these interactions, evaluators can build evidence of the mechanisms through which research influences the world, sidestepping some of the attribution problems that bedevil outcome-focused approaches.

    Contribution mapping

    Contribution mapping is grounded in the idea of contribution analysis, which asks not whether research caused an outcome on its own, an often impossible claim, but whether and how it plausibly contributed alongside other factors. Applied to research, contribution mapping traces the process from a research project through the people and activities involved to eventual changes, mapping the actors, the linkages, and the alignment of efforts along the way. It treats impact as the result of many converging contributions and seeks to make the research’s part in that convergence visible and defensible.

    Choosing and combining approaches

    These frameworks are not rivals so much as complementary lenses. The Payback Framework offers a comprehensive taxonomy of benefits and a logic model to organise them. SIAMPI shifts attention to the interactions that generate impact, which is especially useful where outcomes are long-term or shared. Contribution mapping provides a principled way to talk about a research project’s contribution without overclaiming sole causation. In practice, a thoughtful evaluation may draw on more than one, using a logic model to frame the analysis, evidence of productive interactions to show mechanisms, and contribution reasoning to make a measured causal claim.

    Evidencing impact responsibly

    The common message across these approaches is that impact should be evidenced rather than merely asserted. A persuasive impact narrative is strongest when it rests on a clear account of how the research connected to the changes claimed, supported by documentation of interactions, intermediate steps, and contributions. Linking that account to the underlying outputs, including well-cited datasets and openly shared FAIR data, and recording who did what through frameworks such as CRediT, strengthens the chain of evidence. The standards and vocabularies catalogued in the CASRAI data dictionary help describe these contributions consistently, so that impact assessment rests on a traceable record rather than a well-told story alone.

  • Altmetrics and research impact: what attention data can and cannot show

    Altmetrics promise something seductive: a near-real-time count of the attention a research output is attracting across news, policy documents, social media, blogs, and reference managers, available within days of publication rather than the years a citation count takes to accumulate. That promise is real, and altmetrics genuinely capture forms of reach that citations miss. But the same speed and breadth that make them useful also make them easy to misread, and the gap between “attention” and “impact” is where most of the trouble lies. This article sets out what altmetrics can and cannot show. It builds on the broader treatment in the engagement, impact and SDG-alignment domain.

    What altmetrics actually measure

    Altmetrics — short for alternative metrics — are indicators of the online attention and engagement a research output receives, drawn from sources outside the traditional citation databases. Typical sources include mentions in news outlets and policy documents, posts and shares on social media, blog coverage, Wikipedia citations, and saves in reference managers such as Mendeley. They are usually aggregated against a specific output — identified by its DOI — and presented as a score or a breakdown by source.

    The honest one-line description is this: altmetrics count attention. They tell you that an output was mentioned, shared, saved, or referenced in non-scholarly venues, and roughly where and how much. That is genuinely valuable information, and it is information that citation counts, by their nature, cannot provide.

    What they are useful for

    • Speed. Attention accrues within days, so altmetrics can surface early engagement long before citations could exist. For recent outputs they may be the only quantitative signal available.
    • Breadth beyond academia. A citation count measures uptake by other researchers. Altmetrics can show reach into policy, news media, and public discussion — audiences a citation count is structurally blind to. For an output whose value is partly its public or policy reach, this is exactly the dimension that matters.
    • Qualitative leads, not just numbers. The most useful part of an altmetric record is often not the score but the underlying mentions: which policy document cited the work, which outlet covered it, what the coverage said. Followed up, these point to specific instances of reach that can seed a genuine impact narrative.
    • A complement to citations. Used alongside citation data and qualitative evidence, altmetrics add a view that the other sources lack. Their role is supplementary, not substitutive.

    What they cannot show

    The central caution is simple and must be stated plainly: attention is not impact, and attention is not quality. A high altmetric score means an output was talked about; it says nothing, by itself, about whether the research is sound, whether the attention was positive, or whether any real-world change followed.

    • Attention can be negative. A paper widely shared because it is being criticised, debunked, or ridiculed can score highly. The count does not distinguish praise from condemnation.
    • Attention is not benefit. Genuine research impact — a changed policy, an improved treatment, an adopted practice — is a downstream outcome that an attention count cannot demonstrate. Altmetrics may flag where to look for impact; they are not evidence of it.
    • The numbers are gameable and biased. Social-media-derived metrics can be inflated by coordinated sharing, and they systematically favour topics, languages, and communities that are active online — which is not the same as the topics that matter most.
    • Scores are not comparable across contexts. A single composite altmetric number compresses very different kinds of attention into one figure, and that figure means different things in different fields and for different output types. Comparing scores across disciplines is largely meaningless.

    The responsible-metrics frame

    This is where the wider movement for responsible research assessment provides the discipline that keeps altmetrics honest. The Leiden Manifesto for research metrics (2015) set out principles for the responsible use of quantitative indicators that apply directly here. Three are especially relevant to altmetrics:

    • Quantitative evaluation should support, not supplant, expert qualitative judgment. Altmetrics are an input to a human assessment, never a replacement for reading the work and weighing its contribution.
    • Account for variation by field. Attention patterns differ enormously between disciplines and output types; a metric must be interpreted in context, not applied as a universal yardstick.
    • Avoid misplaced concreteness and false precision. A single score presented to a decimal point invites a confidence the underlying data do not support. The number is an indicator, not a measurement of worth.

    The same spirit runs through the broader reform agenda — the Declaration on Research Assessment (DORA) and the Coalition for Advancing Research Assessment (CoARA) — which presses evaluators away from reliance on any single quantitative proxy and toward judging the substance of contributions. Altmetrics sit comfortably inside that frame as one more contextual signal, and sit very badly outside it as a standalone score to be maximised.

    Treat an altmetric score the way you would treat a smoke alarm: useful for telling you where to look, useless as a measure of how big the fire is. The value is in the mentions it points you to, not in the number itself.

    Using altmetrics well

    1. Read the mentions, not just the score. The specific policy citation or news item is the evidence; the aggregate number is only a pointer.
    2. Pair them with citations and qualitative evidence. No single indicator carries an assessment; altmetrics are one strand among several.
    3. Interpret in context. Field, output type, and audience all change what a given level of attention means.
    4. Never use a score as a ranking or a target. Optimising for attention corrupts the signal and invites the gaming the metric is most vulnerable to.

    Where shared vocabulary fits

    “Impact”, “attention”, “reach”, “engagement”, and “altmetric” are used loosely and often interchangeably, which is exactly how attention data gets mistaken for evidence of benefit. A shared, federated vocabulary that defines these terms precisely — distinguishing attention from impact and pointing back to the Leiden Manifesto and the responsible-assessment frameworks for the caveats — is what lets engagement data be used honestly in evaluation. Supplying that definitional layer is the role the CASRAI dictionary is designed to play; the relevant terms sit in the engagement, impact and SDG-alignment domain.

    Related reading

  • Capturing research impact: REF case studies, PPI and SDG alignment

    Of all the things the research record tries to capture, impact is the most resistant to counting. A citation is a number; a benefit to a patient, a policy, or a community is a story — and stories travel badly through systems built for tallies. Yet the demand to evidence impact has only grown, from national assessment exercises to funder expectations, and with it the need to represent impact as something more structured than a paragraph in a final report. This article sets out the main frameworks for capturing impact and engagement, drawing on the engagement, impact and SDG domain, and argues that even narrative impact has structure worth preserving.

    The impact case study and the pathway to impact

    The most developed apparatus for evidencing impact is the UK’s Research Excellence Framework (REF), whose impact case study has become a recognised genre in its own right. A REF impact case study is a structured narrative describing a specific instance of REF impact — an effect, change, or benefit beyond academia — underpinned by the institution’s research and evidenced rather than asserted. Its discipline is instructive: it names the non-academic beneficiary, traces the route from the underpinning research to the change, and supplies corroborating evidence. That is a far more rigorous object than a vague claim of influence.

    Upstream of the case study sits the pathway to impact: a description of the planned route from a research output to a non-academic benefit. The pathway is forward-looking where the case study is retrospective, but both share a logic that planning frameworks make explicit. A Theory of Change articulates how a project’s activities are expected to lead to outcomes and impact, treating impact as a chain of reasoning that can be set down and examined, not a happy accident to be claimed at the end.

    Involving the public: PPI, citizen science and co-production

    Impact is rarely something done to a passive public; increasingly it is produced with them, and a precise vocabulary distinguishes the modes of involvement. Patient and public involvement (PPI) — the active involvement of patients and members of the public in the design and conduct of research, prominent in health research — is not the same as recruiting participants. A PPI partner shapes the research; a participant is studied by it. The distinction matters for credit as much as for method, and connects to the recognition of a patient or public partner as a genuine contributor rather than a subject.

    • Citizen science is research conducted in part by members of the public, who contribute observations, classifications, or analysis — a mode that scales participation without conflating it with partnership.
    • Co-production names research conducted jointly by researchers and beneficiaries, where the knowledge produced is genuinely shared rather than extracted, while community-based participatory research (CBPR) treats community members as full partners across the process.

    These are not interchangeable, and a record that flattens them into “public engagement” loses information that matters for both assessment and ethics. The labour involved — knowledge mobilisation, the active translation and movement of research knowledge to its users — is itself a contribution that responsible assessment aims to make visible, the kind of work the authorship conversation has historically rendered invisible.

    SDG alignment: a shared frame for relevance

    Where impact case studies describe particular benefits, the Sustainable Development Goals (SDGs) offer a shared frame for situating research against global priorities. SDG alignment is the mapping of research to one or more of the seventeen United Nations Sustainable Development Goals — from SDG 1 (No Poverty) through SDG 3 (Good Health and Well-being) to SDG 13 (Climate Action) and SDG 17 (Partnerships for the Goals). The appeal of SDG alignment is that it is a vocabulary the whole world already shares: a funder, an institution, and a government can all locate a piece of work against the same seventeen-point frame, which is precisely what a piece of free-text impact narrative cannot offer.

    SDG alignment is powerful exactly because it is a common denominator. The risk is the same as its strength: applied loosely, almost any research can be tagged to almost any goal. Useful SDG metadata records a substantiated relationship, not an aspirational gesture — which is why the alignment, like the impact claim it supports, benefits from being evidenced rather than merely asserted.

    Parallel framings exist in other systems — the EU’s Horizon Europe Mission areas play a comparable role within European funding — and the value in each case is the same: a controlled set of categories that lets relevance be stated comparably across institutions and borders.

    Evidence is the hardest part, and here infrastructure is beginning to help. Policy uptake — documented use of research in policymaking — can be traced through the Overton database, which links scholarly works to the policy documents that cite them. Commercial routes have their own markers: a spin-out company formed to commercialise research, or a technology-transfer licence agreement. Each turns a claim of impact into something an assessor can follow back to its source.

    Why this belongs in a metadata standard

    It is tempting to conclude that impact is simply narrative and therefore beyond the reach of structured metadata. That conclusion is too quick. An impact case study has parts — the beneficiary, the underpinning research, the type of impact, the evidence, the SDG or mission alignment — and those parts can be represented as structured fields linked to the underlying outputs, projects, and people through persistent identifiers. Done that way, an institution can find all its work bearing on SDG 3, a funder can aggregate the beneficiaries of a programme, and a researcher’s engagement and co-production work becomes visible in their record rather than lost in prose. This is the same move that responsible assessment makes when it values contribution and context over a single counted number.

    Where shared vocabulary fits

    The terms here are routinely muddled: involvement is not participation, citizen science is not co-production, SDG alignment is not a free pass to tag anything to anything. A shared, federated vocabulary that defines these precisely — pointing to the REF for the impact case study, to established PPI frameworks for involvement, and to the United Nations for the SDGs — is what lets an impact claim made in one system be understood and compared in another. Supplying that definitional layer is the role the CASRAI dictionary is designed to play.

    What to do now

    For researchers: record impact as it accrues, naming the beneficiary, the mode of involvement, and the SDG alignment, with evidence attached rather than reconstructed at deadline. For institutions and funders: capture impact and engagement as structured, identifier-linked metadata so it can be aggregated and corroborated, not just narrated. For standards work: define the distinct modes of public involvement and the discipline of evidenced alignment, federating to the REF, the SDGs, and established PPI frameworks for the authoritative content.

    Related reading

  • Aligning research outputs with the UN Sustainable Development Goals: metadata that travels

    Open a recent institutional repository, a funder’s portfolio dashboard, or a publisher’s article page, and you will increasingly find research tagged against the United Nations Sustainable Development Goals — the 17 goals, adopted by all UN member states in 2015, that frame global priorities from ending poverty to climate action. SDG tagging has become one of the most common ways to express that a piece of research is relevant to a global challenge. It is also one of the least standardised, and understanding both halves of that statement is essential to using it well. This article examines how SDG alignment metadata works, drawing on the engagement, impact and SDG domain.

    What SDG alignment metadata is

    At its simplest, SDG alignment is a tag: an assertion that a research output contributes to, or is relevant to, one or more of the 17 goals. SDG 3 is Good Health and Well-being; SDG 7 is Affordable and Clean Energy; SDG 13 is Climate Action; and so on through the full set, each with its own targets and indicators beneath it. A paper on malaria vaccines might be tagged SDG 3; a paper on solar-cell efficiency, SDG 7; a paper that crosses domains, several at once.

    The value of expressing this as metadata — rather than as prose buried in an abstract — is that it travels. A structured SDG tag attached to an output flows from the publisher into the aggregators, into institutional CRIS systems, into funder dashboards, and into the analytics products that roll research up by goal. An institution can answer “how much of our output contributes to climate action?” only if the alignment is structured; the same question asked of free text requires a project to answer.

    Where the tags come from, and why it matters

    The central problem with SDG alignment is that there is no single authoritative method for assigning the tags, and the different methods disagree. There are three broad sources.

    • Author-asserted. The researcher selects the SDGs their work addresses, usually at submission. This captures intent and disciplinary nuance but is inconsistent across authors and prone to optimistic over-tagging.
    • Algorithmically classified. Several large bibliometric platforms assign SDG tags by running classifiers — typically keyword queries or machine-learning models — over titles, abstracts, and keywords. These scale to millions of outputs but are only as good as their query definitions, and different vendors’ classifiers produce materially different mappings for the same paper. A well-known consequence is that an output’s SDG profile can change simply by switching data provider.
    • Curated taxonomies. Some publishers and repositories use editorially maintained mappings, which are more consistent but more expensive to maintain.

    The practical implication is that SDG alignment metadata is genuinely useful for discovery and broad portfolio framing and genuinely unreliable for precise measurement or comparison across sources. Treating a vendor’s SDG counts as a precise metric — let alone as a basis for ranking — mistakes a fuzzy, method-dependent tag for a measurement. The honest use is directional: SDG tags tell you roughly where a body of work points, not exactly how much of it counts.

    The connection to impact assessment

    SDG alignment sits within the broader machinery of research impact, and it is most powerful when connected to it rather than treated as a standalone label. The UK’s Research Excellence Framework (REF) assesses impact through narrative impact case studies — structured accounts of how specific research led to specific non-academic benefit. An SDG tag and an impact case study are doing complementary work: the tag is a coarse, machine-readable signal of thematic relevance; the case study is a rich, human-readable account of actual effect. The tag helps you find the relevant work; the case study tells you what difference it made.

    The same complementarity runs through the other impact vocabulary in this domain: a pathway to impact describes the intended route from output to benefit; policy uptake, evidenced through records such as those in policy-citation databases, captures realised influence; patient and public involvement records who outside academia shaped the work. SDG alignment is the layer that lets all of this be aggregated thematically — but only if the alignment metadata is structured and its provenance is recorded.

    Provenance is the missing field

    If there is one recommendation that follows from the method-dependence problem, it is this: SDG alignment metadata should always carry its provenance. A tag that records how it was assigned — author-asserted, classifier (and which one, which version), or curated — is interpretable. A bare tag is not, because the consumer cannot know whether they are looking at an author’s considered judgement or a keyword match. As CASRAI’s broader dictionary work argues, metadata without provenance is metadata you cannot safely act on, and SDG tags are a textbook case. A repository that records “SDG 13, author-asserted” alongside “SDG 7, classifier vX” gives its consumers something they can reason about; one that records only “SDG 13, SDG 7” does not.

    Why a shared vocabulary helps

    The SDGs themselves are a fixed, authoritative vocabulary — the UN defines the 17 goals and their targets, and that is not in dispute. What is not standardised is the metadata layer around the tag: how alignment is expressed in a record, how provenance is captured, how multiple tags are weighted or ordered, how the tag relates to impact and engagement records. That surrounding vocabulary is exactly the kind of integrative, machine-readable definition work the CASRAI dictionary exists to do — not to redefine the SDGs, but to standardise how alignment to them travels through research-information systems.

    What to do now

    For researchers and repositories: tag SDG alignment, but always record the provenance of each tag. For institutions and funders: use SDG metadata for thematic discovery and broad portfolio framing, and resist the temptation to treat vendor SDG counts as precise, comparable metrics. For impact assessment: connect SDG tags to the richer impact record — pathways, policy uptake, case studies — rather than letting the tag stand alone. The goal is metadata that travels and stays interpretable when it arrives.

    Related reading

  • Citizen science: recognising participatory research contributions

    Some of the most ambitious datasets in modern science were not gathered by professional researchers at all. Networks of birdwatchers record millions of sightings; volunteers classify galaxies, transcribe historical records and fold proteins; communities monitor air and water quality in their own neighbourhoods. This is citizen science — the participation of members of the public in research — and at its best it achieves a scale, geographic spread and longevity that no conventional research team could afford. Yet the people who make it possible are often the least visible in the resulting publications, named, if at all, in a collective acknowledgement. This article looks at what citizen science contributes and how that contribution can be recognised properly, drawing on the engagement, impact and SDG domain of the CASRAI Dictionary.

    What citizen science actually contributes

    It is tempting to think of citizen science as merely extra hands for data collection, but the contributions are more varied than that. Volunteers gather observations across vast areas and long timescales — the kind of distributed, sustained data collection that is otherwise impossible. They classify and annotate enormous volumes of images and records, doing pattern-recognition work at a scale that supports later automated approaches. In community-led and participatory projects, members of the public help shape the research questions themselves, bringing local knowledge that researchers lack. And in environmental and health monitoring, affected communities generate evidence about their own circumstances. These are genuine intellectual and labour contributions to research, not a peripheral nicety — and they map onto recognised stages of the research process such as investigation, data collection and curation.

    The recognition gap

    Against the scale of these contributions, the recognition is usually thin. The default mechanism is the acknowledgement: a sentence thanking volunteers or naming a project’s participants collectively. Acknowledgements are valuable and should not be dismissed — sincere thanks matter — but they are limited. They do not say what participants did; they rarely name individuals; they are not structured or machine-readable; and they confer none of the formal standing that authorship or contributorship carries. A volunteer who spent years gathering observations that anchor a study’s entire dataset appears, in the formal record, identically to someone who lent a room for a meeting. The challenge is to recognise participatory contribution in a way that is proportionate, honest and visible.

    Routes to better recognition

    There is no single answer, and the right approach depends on the nature and scale of the contribution. Several routes are available and increasingly used:

    • Structured, specific acknowledgement. At a minimum, an acknowledgement can describe precisely what participants contributed — the observations collected, the classifications made — rather than offering generic thanks, making the nature of the contribution clear.
    • Named contributorship. Where individual contributions are identifiable and substantial, naming people and describing their roles — rather than absorbing them into an anonymous collective — gives concrete recognition to concrete work.
    • Group and consortium recognition. For large networks, recognising the contributing group as a named entity, with the project itself identified, lets a collective effort be cited and credited as a unit.
    • Crediting the data they produced. When citizen-science data are published as a citable dataset with its own identifier, the act of producing the data becomes a recognised, reusable output, and reuse can be traced back to the effort that created it.

    Describing the contributions with a shared vocabulary

    Whatever route is chosen, recognition is clearer when contributions are described in consistent terms rather than ad hoc prose. The CRediT taxonomy offers a controlled vocabulary that maps onto much participatory work: Investigation for conducting observations and collecting data, and Data curation for the classifying, annotating and validating that volunteers so often perform. The complete set of roles is set out in our overview of the CRediT roles. Using a shared vocabulary to describe what participants did — even within an acknowledgement — makes the contribution specific and comparable rather than vague, and it places participatory work within the same framework used to describe professional contribution. That parity of description is itself a form of respect: it says the work is the same kind of work, wherever it came from.

    Why recognition matters beyond fairness

    Recognising citizen-science contribution is partly a matter of simple fairness, but it also serves the research itself. Volunteers who see their work valued are more likely to sustain it, and sustained participation is exactly what gives citizen science its unique reach over time and space. Recognition strengthens the relationship between research and the public, which is the heart of meaningful engagement and impact — and citizen science frequently advances the Sustainable Development Goals directly, through environmental monitoring, biodiversity recording and community health work that connects research to societal benefit. Treating participants as genuine contributors rather than anonymous helpers makes that connection durable.

    A consistent record for participatory research

    For participatory contribution to be recognised consistently, the way it is described must mean the same thing across the systems that record it — publications, datasets, project records and engagement reporting. That consistency is what the CASRAI Dictionary provides: a shared vocabulary so that a contribution made by a volunteer is described, understood and credited the same way wherever it appears. Citizen science widens who gets to participate in research; recognising it properly ensures that widening is reflected honestly in the scholarly record rather than lost in a line of thanks.

  • Knowledge mobilisation: translating research into policy and practice

    There is a comfortable assumption, still widespread, that good research speaks for itself — that if findings are sound and published, the world will notice and act. The reality is otherwise. The distance between a finding sitting in a journal and that finding changing a policy, a clinical practice or a professional routine is often vast, and it is rarely crossed by accident. Bridging it is a discipline in its own right, variously called knowledge mobilisation, knowledge translation or knowledge exchange: the deliberate, skilled work of moving research into the hands of the people who can use it, in a form they can act on. This article examines that work, drawing on the engagement, impact and SDG domain of the CASRAI Dictionary.

    Why dissemination is not enough

    For a long time the implicit model of getting research used was a one-way push: do the research, publish it, perhaps issue a press release, and assume uptake will follow. This model fails repeatedly, and understanding why is the starting point for everything else. Practitioners and policymakers are busy, work under different pressures and timescales than researchers, and rarely read academic journals. Research findings often arrive in a form — long, hedged, technical — that is ill-suited to a decision that must be made next week. And evidence almost never speaks with one voice; using it well requires interpretation, contextualisation and judgement about how it applies in a particular setting. Simply making research available, in short, does very little. Getting it used requires actively engaging with the people who might use it, understanding their needs, and shaping the evidence so it can inform what they actually do.

    The Knowledge-to-Action cycle

    One of the most widely used frameworks for thinking about this is the Knowledge-to-Action cycle, which models how knowledge moves from creation into application. It distinguishes the knowledge creation process — in which raw research is refined and synthesised into more usable forms such as syntheses and tools — from an action cycle of activities involved in applying knowledge: identifying a problem and the relevant knowledge, adapting it to the local context, assessing barriers and facilitators to its use, selecting and tailoring interventions, monitoring use, evaluating outcomes, and sustaining the change. The framework’s great value is that it treats application as an active, iterative process with its own steps, rather than as something that simply happens once research exists. It makes clear that adapting knowledge to context, and attending to the barriers in a particular setting, are not afterthoughts but central to whether evidence ever gets used.

    Tools of the trade

    Knowledge mobilisation has developed a repertoire of practical instruments and tactics. Among the most important:

    • Policy briefs. Short, accessible documents that distil what the evidence says on a question into a form a policymaker can absorb quickly — framed around the decision at hand, clear about implications, honest about uncertainty.
    • Plain-language summaries. Versions of research stripped of jargon and written for a non-specialist audience, so that the substance is reachable by those who need it.
    • Engaging users early. Involving the eventual users of research — practitioners, policymakers, communities — in shaping the questions and the work from the outset, so the research is relevant and the relationships exist when it is time to act.
    • Tailored interaction. Workshops, briefings, secondments and sustained relationships that move evidence through conversation and trust rather than through documents alone.

    What these share is a recognition that mobilisation is relational and active. Evidence travels through people and relationships, not merely through publications.

    Boundary organisations and brokers

    Because the worlds of research and practice differ in language, culture and incentives, a special role has emerged to span them: that of the boundary organisation and the knowledge broker. Boundary organisations sit deliberately between research and policy or practice, translating in both directions, building relationships, and helping each side understand the other. Knowledge brokers are the individuals who do this work — people fluent in both worlds who can interpret research for users and convey users’ needs back to researchers. Their importance reflects a hard-won lesson: the gap between knowledge and action is often best bridged not by asking researchers to become communicators or policymakers to become scholars, but by sustaining intermediaries whose explicit job is to connect the two. Investing in these connective roles is frequently what turns sporadic, accidental uptake into reliable flow.

    Mobilisation as part of impact

    Knowledge mobilisation is closely tied to the wider conversation about research impact — the difference research makes beyond academia — but it is the active practice rather than the retrospective measurement. Where impact assessment asks what difference research made, mobilisation asks how to make that difference happen, and does the work of bringing it about. The two are complementary: mobilisation is the cause, demonstrable impact often the effect. Recognising mobilisation as skilled, valuable work in its own right — rather than as something researchers should do in their spare time — is part of valuing the full range of what research careers involve, a theme explored in our resources on research practice and impact.

    Recording mobilisation consistently

    For mobilisation activity to be recognised, planned and connected to the research and people behind it, it has to be describable in consistent terms across institutions, funders and reporting systems — what was produced, for whom, through what route, with what uptake. That consistency is what the CASRAI Dictionary provides: a shared vocabulary so that engagement and mobilisation activities are understood the same way wherever they are recorded. And because translating research into use is genuine, often substantial contribution, the work can be described within the same framework as every other — the CRediT taxonomy and its full set of contribution roles. Producing knowledge is only half the task; mobilising it — deliberately, skilfully, in partnership with those who can use it — is how research earns its keep in the world.

  • Reporting research outcomes to funders: from outputs to impact

    When a research grant ends, the relationship between the funder and the work it paid for does not. Funders — whether public agencies, charities or research councils — are accountable for the money they distribute, and they increasingly want to understand not merely that a project happened but what it produced and, ultimately, what difference it made. This is the territory of outcome reporting: the structured account a researcher gives, often over several years, of the publications, datasets, software, collaborations, further funding, policy influence and wider effects that flow from a grant. Done badly, outcome reporting is a dreaded administrative chore; done well, it is how the research system demonstrates its value and learns what works. This article examines how outcome reporting is evolving, drawing on the funding and finance domain of the CASRAI Dictionary.

    From outputs to outcomes to impact

    It helps to distinguish three things that reporting tries to capture. Outputs are the direct products of the research — the papers, datasets, software and patents it generates. Outcomes are what those outputs lead to — the further research they enable, the collaborations and follow-on funding they spark, the uptake of findings by others. Impact is the eventual effect on the wider world — on policy, practice, health, the economy and society. The progression matters because it reflects a genuine shift in what funders ask. It is no longer enough to list publications; funders want to trace the path from output to outcome to impact, even though that path is often long, indirect and hard to attribute. Much of the recent innovation in reporting comes from trying to capture outcomes and impact, which unfold years after a grant closes and resist tidy measurement.

    Outcome-reporting systems

    To manage reporting at scale, funders have adopted dedicated systems. In the United Kingdom and elsewhere, Researchfish is a widely used platform through which researchers record the outcomes arising from their funding over time, with funders drawing on the accumulated information to understand and demonstrate the results of their investment. In the United States, federal awards are reported through the Research Performance Progress Report (RPPR), a standardised format for progress reporting across agencies that covers accomplishments, products, participants and impact. These systems share a common purpose: to collect outcome information in a structured, comparable way rather than as scattered free-text, so that it can be aggregated, analysed and reported onward. They also share a common challenge — the burden they place on researchers — which has driven much of the effort to make reporting smarter.

    Reducing burden through persistent identifiers

    The single most important development in lightening the reporting load is the use of persistent identifiers to link information automatically rather than re-entering it by hand. The principle behind this is the well-known maxim “enter once, reuse often”. If a researcher’s outputs carry persistent identifiers — a DOI for a publication or dataset, an ORCID identifier for the researcher, a ROR identifier for their organisation, a grant identifier for the award — then the links between them can be discovered and assembled by systems rather than typed in repeatedly. A publication that records its funding grant can be connected to that grant automatically; outputs registered against a researcher’s ORCID can flow into a report without manual transcription. This turns reporting from an exercise in re-keying information that already exists into one of confirming and contextualising links the infrastructure has already drawn. The persistent-identifier ecosystem — explored across our persistent identifiers domain — is what makes low-burden, accurate outcome reporting possible.

    Narrative and the limits of metrics

    Not everything that matters can be captured as a structured field. The deepest effects of research — how it changed a field’s direction, shaped a policy, improved a practice, built a capability — often need to be explained, not merely counted. This is why narrative has become central to impact reporting. A short, evidenced account of what a piece of research led to can convey forms of value that no list of outputs can. The move towards narrative reflects a broader unease with reducing research to metrics. Several practices help impact reporting work:

    • Evidence-backed narratives. Pairing a clear account of impact with concrete evidence and links to the underlying outputs.
    • Realistic timeframes. Recognising that impact often emerges years after a grant ends, so reporting must continue beyond the grant period.
    • Honest attribution. Acknowledging that impact usually results from many contributions, not a single grant in isolation.
    • Structured links plus narrative. Combining machine-readable links to outputs with human-readable explanation, so reports are both aggregable and meaningful.

    Closing the loop with the grant lifecycle

    Outcome reporting is the final stage of a continuous process that begins when a call is published and an award is made. When information is captured as structured data throughout that lifecycle — the grant, its outputs, the people and organisations involved — reporting at the end becomes a matter of drawing together links already established rather than reconstructing a history from scratch. This is the logic of treating the whole grant lifecycle, from call to closeout, as connected structured information, a theme developed in our resources on research administration. Reporting is not a bolt-on at the end; it is the harvest of good data discipline maintained throughout.

    A consistent vocabulary for reporting

    For outcome information to flow between researchers, institutions and funders — and for the same output reported to two funders to be recognised as one thing — the elements involved must be described consistently, or reports become incomparable and links break. That consistency is what the CASRAI Dictionary provides: a shared vocabulary so that the information flowing into funder reports is understood identically wherever it originates. And because every reported output rests on real contribution, the work behind it can be described in the same shared framework — the CRediT taxonomy. Funders are right to ask what their money achieved; good infrastructure and shared vocabulary are what let researchers answer honestly without drowning in administration.