CASRAI Dictionary

Tag: citizen science

  • 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

  • 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.