CASRAI Dictionary

Category: Guides & Explainers

Practical how-to guides, templates, checklists, and career pathways for research administrators, authors, and institutional teams.

  • Registry of Open Access Repository Mandates and Policies: A ROARMAP Guide for Research Administrators

    When a research office needs to check whether a funder requires immediate deposit or permits a twelve-month embargo, guesswork is not good enough. The registry of open access repository mandates and policies — known by its acronym ROARMAP — exists precisely to remove that guesswork. Maintained by the School of Electronics and Computer Science at the University of Southampton, it is a searchable, international catalogue of the open access mandates that universities, research institutions and funders have adopted, and it remains one of the few places where those policies can be compared side by side rather than tracked down one funder website at a time.

    This matters more in 2026 than it did a decade ago. Funder mandates have multiplied, cOAlition S members continue to refine Plan S implementation, and — as a June 2026 German constitutional ruling shows — even settled mandates can be challenged in court. Research administrators, library staff and compliance officers need a single reference point that tracks all of it. ROARMAP is that reference point.

    What ROARMAP catalogues, and why it matters

    ROARMAP began life in 2003 as the Institutional Archives Registry, built by the EPrints team at the University of Southampton. It was renamed the Registry of Open Access Repositories Mandatory Archiving Policies in 2006, then adjusted again, settling on its current name — Registry of Open Access Repository Mandates and Policies — around 2014. Throughout those renamings, its purpose stayed constant: track every publicly documented policy that requires or encourages researchers to make peer-reviewed outputs openly accessible, usually by depositing a copy in a repository.

    ROARMAP has a companion registry, ROAR (the Registry of Open Access Repositories), which indexes the repositories themselves rather than the policies that govern them. The distinction trips people up regularly, so it is worth setting out clearly alongside a third commonly confused resource, OpenDOAR.

    Registry What it indexes Typical use case
    ROARMAP Open access mandates and policies from institutions and funders Checking what a funder or institution requires
    ROAR Open access repositories themselves — location, size, growth Finding where a repository is hosted
    OpenDOAR Curated, vetted directory of repositories and their technical metadata Selecting a compliant repository to deposit into

    Entries in ROARMAP are not uniform in strength. Some record a simple recommendation to self-archive; others are mandatory policies where compliance is tied to continued grant funding — the sanction that gives a mandate real teeth. As of the last widely cited published count, ROARMAP had catalogued policies from more than 520 universities and over 75 research funders worldwide, a figure that has continued to grow as more institutions formalise their open access requirements.

    How cOAlition S members’ policies are catalogued

    cOAlition S is the group of research funders — including national funders, charitable foundations and the European Commission — that came together in 2018 to implement Plan S, the requirement that publicly funded research be made immediately open access without embargo. Because cOAlition S members are funders rather than repository operators, their individual mandates are exactly the kind of entry ROARMAP was built to hold.

    Each cOAlition S member’s policy is entered as a discrete record, so an administrator can look up, for example, what a specific national research council requires on licensing (typically CC BY), acceptable routes to compliance (Gold, Green with a zero-embargo repository deposit, or a transformative agreement), and how the policy interacts with the funder’s own compliance-monitoring tools, such as the Journal Checker Tool. Because ROARMAP predates Plan S by more than a decade, it also preserves the pre-2018 policy text for many of these funders, which is useful when institutions need to establish exactly when a requirement changed.

    This is a genuine information gain over simply reading each funder’s website individually: ROARMAP lets an administrator filter by funder type, country and adoption date, surfacing patterns — such as clusters of European funders tightening embargo terms in the same policy cycle — that are invisible from any single funder’s own page.

    Using the registry to compare institutional and funder mandates

    For day-to-day compliance work, ROARMAP is used less as a browsing tool and more as a lookup and benchmarking tool. A typical workflow for a research administrator looks like this:

    • Search by country or institution name to confirm whether a specific university has a formal mandate, and since when.
    • Filter by policymaker type — funder versus institution — to separate overlapping obligations on a single researcher.
    • Check the deposit timing and permitted embargo period recorded against each policy.
    • Note the required manuscript version — preprint, accepted manuscript or version of record.
    • Compare licensing requirements (commonly CC BY) where the policy specifies one.
    • Benchmark a draft institutional policy against comparable peer institutions before it goes to committee.

    This benchmarking use case is one of ROARMAP’s most practical applications. Rather than drafting an institutional open access policy from a blank page, a policy officer can pull several comparable universities’ mandates from the registry, line up their deposit windows and enforcement mechanisms, and use that comparison to justify the strength of a proposed new policy to institutional leadership.

    What is an open access repository?

    An open access repository is a freely accessible digital archive where researchers self-archive peer-reviewed articles, preprints or accepted manuscripts so readers can access them without a paywall. Universities run institutional repositories; funders and disciplines run subject-based ones. ROARMAP catalogues the policies requiring deposit — not the repositories themselves.

    How does OpenDOAR differ from ROARMAP?

    OpenDOAR is a curated directory listing vetted open access repositories and their technical characteristics, while ROARMAP lists the mandates and policies that require deposit into those repositories. Administrators typically use OpenDOAR to identify a compliant repository, then check ROARMAP to confirm whether deposit is compulsory and on what terms.

    What is self-archiving, and how do ROARMAP-listed policies define it?

    Self-archiving — the “Green” route to open access — means an author deposits a manuscript into a repository alongside, or instead of, publishing openly with a journal. Policies catalogued in ROARMAP typically specify the deposit timing, permitted embargo length, and which manuscript version satisfies the mandate.

    What are the drawbacks of relying on open access mandates?

    Mandates catalogued in registries such as ROARMAP vary widely in enforcement: some merely encourage deposit while others tie compliance to grant payment. Weak or unmonitored policies show low actual deposit rates, embargo terms conflict across funders, and legal challenges — as seen in Germany in 2026 — can unsettle even long-established mandates.

    What the changing legal landscape means for research administrators

    ROARMAP’s value is not static, and 2026 has already supplied a reminder of why. In June, Germany’s Federal Constitutional Court struck down a state-level bylaw at the University of Konstanz that would have compelled researchers to exercise their statutory secondary-publication right — ruling that regulating copyright through employment or institutional statute conflicted with the federal government’s exclusive legislative competence over copyright law. The University of Konstanz noted afterwards that the ruling changed little in practice, because the great majority of its researchers already deposit voluntarily. But the case is a useful illustration for administrators elsewhere: a mandate’s formal status, its legal basis and its actual compliance rate can diverge, and a registry entry captures only the first of those three.

    That gap between formal mandate and practical uptake is exactly why registries such as ROARMAP function as compliance infrastructure rather than mere reference material. Institutions revising their own open access policy — whether to align with cOAlition S requirements, respond to a national research assessment exercise, or pre-empt a legal challenge — need a documented, dated record of what comparable institutions and funders actually require, not an assumption based on the last policy a colleague happened to read. For a wider view of how these obligations sit alongside contributorship and compliance frameworks more broadly, CASRAI’s research administration resources and dictionary of research terms provide further grounding.

    As funder policies continue to tighten and jurisdictions test the legal limits of mandated deposit, expect ROARMAP’s role to shift from a static archive towards a living reference that research offices consult routinely, alongside compliance checkers and repository directories, whenever a grant agreement, tenure case or institutional policy review depends on knowing exactly what an open access mandate actually requires.

  • Jisc Open Access Agreements: A cOAlition S Compliance Route Map

    UK research administrators juggling funder mandates now face a genuinely confusing question: does a given Jisc open access agreement actually satisfy a cOAlition S-aligned funder’s Plan S requirement, or does it only cover the invoice? Jisc negotiates centrally on behalf of UK higher education institutions, but the resulting deals are not automatically interchangeable with Plan S’s own compliance routes — and conflating the two is a common source of avoidable non-compliance findings at grant closeout.

    This route map sets out, mechanism by mechanism, how Jisc’s negotiated agreements map onto cOAlition S’s three approved compliance routes and the UK Research and Innovation (UKRI) open access policy, so research offices can advise authors with confidence rather than by rule of thumb.

    What Jisc open access agreements actually negotiate

    Jisc negotiates three broad categories of open access agreement on behalf of its member institutions, governed by the UUK/Jisc Research Licensing Strategy Group and informed explicitly by the principles of Plan S and the OA2020 initiative:

    • Transitional (transformative) agreements — convert existing subscription spend into a combined fund covering both continued read access and open access publishing costs at hybrid and subscription titles (Elsevier, Wiley, Springer Nature, Taylor & Francis and others).
    • Fully open access agreements — membership or flat-fee arrangements with born-open-access and society publishers, including current deals with ACM (2026–2028), MDPI’s Institutional Open Access Program (2026–2027) and PLOS’s flat-fee and Community Action Publishing licences (2026–2027).
    • Compliant green agreements — publisher commitments to an immediate, embargo-free, CC BY-licensed repository deposit route for authors who cannot or do not use a paid option.

    Springer Nature alone reports over 100 UK institutions participating in its Jisc-negotiated agreement, illustrating the scale of collective bargaining involved. These agreement types are the practical instruments; the compliance routes they need to satisfy come from cOAlition S itself.

    The three cOAlition S Plan S compliance routes

    cOAlition S launched Plan S in 2018, with implementation beginning on 1 January 2021. Its implementation guidance sets out exactly three routes by which a funded output can be considered compliant. Understanding these routes independently of any single publisher deal is the foundation for everything that follows.

    Plan S route What it requires Typical publication type
    Route 1 — Open access venue Publish in a fully open access journal or platform, immediately available under CC BY Gold OA / Diamond OA journals
    Route 2 — Transformative arrangement Publish in a subscription/hybrid journal covered by a recognised transitional agreement Hybrid journals under a Jisc transitional deal
    Route 3 — Repository deposit Deposit the author accepted manuscript (or, increasingly, version of record) immediately, with no embargo and a CC BY licence, often invoking the Rights Retention Strategy Any subscription journal, including those with no Jisc deal at all

    Route 3 matters most for institutional risk management: it is the fallback that keeps every author compliant even when no Jisc agreement exists for their chosen journal, or when an agreement’s funding allocation has already been exhausted for the year.

    Matching Jisc agreement types to each compliance route

    Jisc’s own three agreement categories were designed with these routes in mind, but the mapping is not always one-to-one, and research offices need to check eligibility at the point of submission rather than assume coverage.

    Jisc agreement type Plan S route satisfied Practical caveat for research offices
    Fully open access agreement Route 1 (OA venue) Confirm the specific journal or platform is listed under the current licence, not just the publisher brand
    Transitional (transformative) agreement Route 2 (transformative arrangement) Fund caps and corresponding-author eligibility rules mean coverage can lapse mid-year
    Compliant green agreement Route 3 (repository deposit) Requires active AAM deposit workflow — Jisc’s Publications Router can automate metadata and full-text delivery to the repository

    UKRI, a founding cOAlition S funder, layers its own 2021 open access policy on top of this framework: immediate open access is required for journal articles and conference proceedings from grants awarded on or after 1 April 2022, and for monographs, book chapters and edited collections from 1 January 2024. UKRI’s policy is designed to align with Plan S principles but is administered separately — an author can be UKRI-compliant via the same Gold, transformative, or Green routes described above, but institutions must check UKRI’s specific embargo and licensing terms rather than assume Plan S compliance automatically satisfies UKRI, or vice versa.

    Common questions from UK research offices

    What is a read and publish deal?

    A read and publish deal is a single institutional agreement, usually negotiated by a consortium such as Jisc, that bundles subscription access to a publisher’s journals with funded open access publishing rights for eligible corresponding authors, replacing separate read and pay-to-publish invoices.

    What are the three routes to Plan S compliance?

    cOAlition S recognises three routes: publishing in a fully open access journal or platform; publishing in a subscription journal under a recognised transformative arrangement; or depositing the accepted manuscript in a repository immediately, with no embargo and a CC BY licence.

    Is the UKRI open access policy the same as Plan S?

    No. UKRI is a cOAlition S founding funder and designed its 2021 open access policy to align closely with Plan S principles, but the two are administered separately, with UKRI setting its own effective dates, embargo rules and licensing requirements that research offices must check independently.

    Is Jisc’s Open Policy Finder the same as the Journal Checker Tool?

    No — they are commonly confused. Open Policy Finder is Jisc’s own tool for checking publisher and funder policies, while the Journal Checker Tool is operated independently by cOAlition S at journalcheckertool.org to confirm a specific journal-institution-funder combination against Plan S routes.

    A practical compliance checklist

    Research offices advising authors on a submission should work through the following before a manuscript goes out:

    1. Confirm whether the funder is a cOAlition S signatory, and separately whether UKRI-specific terms also apply.
    2. Check the target journal against the current Jisc agreement list for the author’s institution and publisher — agreement coverage varies by title, not just by publisher.
    3. Run the combination through cOAlition S’s Journal Checker Tool to confirm which of the three routes applies before submission, not after acceptance.
    4. Monitor transitional agreement fund caps; many UK institutions see APC allocations exhausted before the calendar year ends.
    5. Maintain a documented Green-route fallback — immediate AAM deposit with a Rights Retention Statement — for any journal outside a live agreement.
    6. Record the compliance route used against each output for funder reporting and REF-adjacent audit trails.

    Implications for research offices

    The practical risk sits less in the headline agreements than in their edges: mid-year fund exhaustion on transitional deals, journals moving in or out of coverage between renewal cycles, and corresponding-author eligibility rules excluding co-authors at non-participating institutions. Jisc’s multi-year renewals — the ACM Open Journals agreement running 2026–2028, PLOS licences renewed for 2026–2027 — give planning stability, but offices should treat every agreement as time-bound and re-verify eligibility annually rather than relying on a static internal list.

    There is also a structural shift underway toward Subscribe to Open and community-based membership models, which remove per-article APC decisions entirely but still require a compliant Green fallback under current Jisc guidance, since S2O agreements depend on enough institutions subscribing to unlock full participation. For research administration teams building durable workflows, the safest design principle is to treat Route 3 — immediate repository deposit — as the permanent baseline, with Jisc’s negotiated Routes 1 and 2 as opportunistic upgrades rather than the primary compliance mechanism.

    Looking ahead

    As UKRI’s open access policy embeds further into monograph and long-form publishing and Jisc continues renewing its publisher portfolio, the institutions with the least audit risk will be those that stopped treating “which Jisc deal applies” as the first question. The first question should be which Plan S route the output needs to satisfy; the applicable Jisc agreement, if one exists, is simply the most convenient way to deliver it. Research offices that build their author guidance and internal tooling — including terminology drawn from a shared open access dictionary — around the three compliance routes, rather than around individual publisher brands, will adapt fastest as agreements are renegotiated, replaced or allowed to lapse.

  • Budapest Open Access Initiative vs Plan S: Comparing Two Open Access Blueprints

    The Budapest Open Access Initiative (BOAI) and Plan S are the two documents most frequently invoked when someone asks “what does open access actually require?” — yet they answer that question in almost opposite ways. BOAI is a voluntary declaration of principle from 2002; Plan S is a binding funder mandate from 2018. Readers arriving from searches around cOAlition S often want to know which framework applies to their situation, and why the two differ so sharply in enforceability. This piece sets out both, side by side, with the dates, mechanisms and licensing terms that distinguish them.

    What is the Budapest Open Access Initiative?

    BOAI arose from a small meeting the Open Society Institute convened in Budapest on 1-2 December 2001, and the resulting statement was released publicly on 14 February 2002. It was funded by a US $3 million grant from the Open Society Institute and signed initially by 16 individuals, including Peter Suber, Stevan Harnad, Michael Eisen and Jean-Claude Guédon — figures who went on to shape the wider open access movement.

    The declaration gave one of the first widely used definitions of open access: free availability on the public internet, permitting any user to read, download, copy, distribute, print, search, link to, or text-mine the full text, with the only constraint being authors’ right to control the integrity of their work and be properly credited.

    • Green open access: authors self-archive a copy of their peer-reviewed paper in an open repository.
    • Gold open access: journals publish articles openly from the outset, funded by means other than reader subscriptions.

    BOAI does not mandate either route, set a deadline, or monitor compliance. Its 10th-anniversary statement (2012) added a recommendation for CC BY licensing and repository infrastructure; its 20th-anniversary update (BOAI20, 2022) issued four high-level recommendations for the next decade. By 2023, over 6,800 individuals and 1,600 organisations had signed it. Alongside the 2003 Berlin Declaration and Bethesda Statement, BOAI is one of the three founding texts of the open access movement.

    What is Plan S?

    Plan S was launched in September 2018 by cOAlition S, a group of national and international research funders including UKRI, several European research councils, and the European Commission. Unlike BOAI, Plan S is a mandate: it requires that, for research funded from 2021 onwards, resulting peer-reviewed publications must be made immediately open access — in a compliant journal, on a compliant platform, or via an open repository — with no embargo.

    Plan S sets out ten principles covering licensing, author rights and cost transparency. Its most consequential requirements are:

    • Open licensing — publications must carry an open licence, preferably CC BY.
    • Rights retention — authors or their institutions retain copyright rather than transferring it to the publisher.
    • No pure hybrid support — cOAlition S will not fund publication in subscription journals that offer paid open access options, except within time-limited transformative agreements.
    • Fee transparency — where article processing charges apply, they must be disclosed and justified.

    Because Plan S is tied to funding conditions, compliance is checked, and non-compliant publications can put a researcher’s funding eligibility at risk — a mechanism BOAI simply has no equivalent of.

    BOAI vs Plan S: a side-by-side comparison

    Feature Budapest Open Access Initiative (BOAI) Plan S
    Launched 14 February 2002 4 September 2018
    Originator Open Society Institute-convened group of individuals cOAlition S (national/international research funders)
    Nature Voluntary declaration of principle Binding funder mandate
    Enforcement None — moral/advocacy suasion only Tied to grant funding conditions
    Preferred routes Green (self-archiving) and gold (OA journals) Compliant journal, platform, or repository, no embargo
    Licensing Not prescribed (CC BY recommended from 2012) Open licence required, CC BY preferred
    Implementation deadline None set Applied to research funded from 2021

    Key differences explained

    The clearest way to read the two documents is as different stages of the same movement. BOAI supplied the definition and the philosophical case for open access; Plan S supplied a compliance mechanism to accelerate uptake once voluntary adoption plateaued. Two decades on from BOAI, much subscription-journal literature remained closed, which is precisely the gap cOAlition S funders set out to close by attaching conditions to their money rather than relying on persuasion.

    A second difference is scope. BOAI addresses the entire scholarly community — researchers, institutions, publishers, governments — as a universal statement. Plan S applies specifically to researchers funded by cOAlition S members, so its reach is defined by funder membership rather than by field or geography.

    Common questions, answered

    What is the Budapest Open Access Initiative concerned with?

    The Budapest Open Access Initiative is concerned with making peer-reviewed research literature freely available online, without financial or legal barriers, so anyone can read, download, copy, distribute, or text-mine it, subject only to authors’ right to be properly acknowledged.

    What is the history of BOAI?

    BOAI emerged from a meeting the Open Society Institute convened in Budapest on 1-2 December 2001, was released publicly on 14 February 2002, and was followed by 10th-anniversary (2012) and 20th-anniversary (2022) recommendation updates.

    In which year did the Budapest Open Access Initiative Declaration take place?

    The Budapest meeting took place in December 2001, and the resulting declaration was formally released to the public on 14 February 2002, making it one of the founding texts of the open access movement.

    What is the difference between BOAI and Plan S?

    BOAI is a voluntary statement of principle with no enforcement mechanism, while Plan S is a binding funder mandate from cOAlition S requiring immediate open access, specific licensing, and compliance monitoring for funded outputs.

    What this means for institutions, researchers and publishers

    For research administrators, the practical takeaway is that BOAI and Plan S sit at different points of an institutional compliance stack. BOAI-aligned green open access — depositing a copy in an institutional or subject repository — remains a low-cost baseline that satisfies neither Plan S’s no-embargo rule nor its licensing requirement on its own, but supports discoverability and long-term preservation regardless of funder.

    Publishers navigating both frameworks typically need:

    • A CC BY (or equivalent open) licensing option at the article level.
    • A rights-retention pathway that does not require copyright transfer.
    • Transparent, itemised article processing charges where fees apply.
    • Repository-compatible metadata so green deposits can satisfy funder checks.

    Institutions should treat BOAI’s language as the shared vocabulary of open access policy — it is what most local and national OA policies still cite when defining terms — while treating Plan S (and successor funder mandates modelled on it) as the specific compliance checklist that determines whether a given grant-funded output is audit-ready.

    Two blueprints, one destination

    BOAI and Plan S are not competitors; they are sequential milestones in the same movement toward open scholarly communication. BOAI defined what open access means and why it matters; Plan S demonstrated what happens when funders convert that definition into a binding condition of grant compliance. Institutions that understand both — the founding principles and the enforcement mechanism layered on top — are better placed to build policies that satisfy funder mandates without losing sight of the broader access mission BOAI first articulated in 2002.

    Research-administration teams working across CRediT contributor roles, authorship policy, and funder compliance can find related terminology in the CASRAI Dictionary and further context in the research administration pillar.

  • cOAlition S Leaders Group Explained: Governance, Executive Steering Group and Funders

    What Is cOAlition S and How Is It Governed?

    Research administrators tracking open-access compliance often ask who is actually behind Plan S decisions. The cOAlition S Leaders Group is the top decision-making body of cOAlition S, the international consortium of research funders that launched Plan S in 2018 to mandate immediate open access to publicly funded research.

    cOAlition S itself has no autonomous legal capacity. It is, in its own words, “an informal alliance of organisations and institutions that fund and/or perform research activities” whose members have publicly committed to implementing Plan S principles. That single fact shapes everything else about its governance: policy is agreed collectively, but enforcement remains the legal responsibility of each individual funder.

    Governance runs through three tiers: the Leaders Group sets strategy, the Executive Steering Group implements it, and a secretariat provides day-to-day operational support. Two supporting bodies — an Experts Group and a network of Open Access Ambassadors — feed technical advice and community feedback into the process.

    The Leaders Group: Where Plan S Policy Is Set

    The Leaders Group is composed of the heads of cOAlition S member organisations — national and regional research funders, philanthropic funders, and the European Commission. It approves the coalition’s overall strategy, agrees the principles that Plan S-aligned policies must follow, and appoints both the Executive Director and the Executive Steering Group.

    As of 2026, the Leaders Group is chaired by Mari Sundli Tveit, Chief Executive of the Research Council of Norway and President of Science Europe — a dual role that illustrates how tightly cOAlition S governance and Science Europe leadership now overlap.

    A sample of Leaders Group representation, drawn from cOAlition S’s published governance list, shows the geographic and institutional spread involved:

    Member organisation Country / region Leaders Group representative
    Research Council of Norway Norway Mari Sundli Tveit (Chair)
    Research Council of Finland (AKA) Finland Floora Ruokonen
    French National Research Agency (ANR) France Claire Giry
    Slovenian Research and Innovation Agency (ARIS) Slovenia Mirjam Dular
    European Commission European Union Marc Lemaître
    Foundation for Science and Technology (FCT) Portugal Francisco Santos
    Howard Hughes Medical Institute (HHMI) United States (philanthropic) Bodo Stern
    Aligning Science Across Parkinson’s (ASAP) United States (philanthropic) Randy Schekman

    Membership turns over as staff change roles, so the current, authoritative composition is always the governance list published on coalition-s.org rather than any secondary source — including this one.

    The Executive Steering Group, Director, and Secretariat

    Below the Leaders Group sits the Executive Steering Group, which translates approved strategy into an operational work plan and supervises the cOAlition S Office. It is chaired by Lidia Borrell-Damián, Secretary General of Science Europe — again reflecting the close personnel overlap between the two bodies.

    Day-to-day leadership sits with the Executive Director, who leads the Executive Steering Group and acts as the coalition’s principal spokesperson. Leadership has changed hands recently: Johan Rooryck stepped down in July 2025 after six years in the role, a period that saw the coalition’s fastest growth. Curt Rice, a former university rector, was subsequently appointed Director in May 2026 to lead strategy implementation.

    Operational and financial support is provided by the secretariat, which is appointed by and reports to the Leaders Group. The secretariat’s hosting arrangement has itself shifted: cOAlition S functions moved from the European Science Foundation to OPERAS AISBL, a Brussels-based research infrastructure for open scholarly communication, which now hosts the cOAlition S Secretariat.

    The coalition’s own published figures show its office budget has contracted sharply as activities matured: total spending fell from roughly €1.12 million in 2022 to €545,167 in 2025, with staffing dropping from 3.5 FTE (2022–23) to around 2 FTE in 2025, partly reflecting the sunsetting of the Journal Comparison Service in early 2025.

    • Leaders Group — policy-making and strategic direction
    • Executive Steering Group — implementation and oversight of the work plan
    • Secretariat (OPERAS AISBL) — finance, operations, communications
    • Experts Group — technical and policy advice
    • Open Access Ambassadors — community outreach and feedback

    Answer-First: Common Questions on cOAlition S Governance

    Who sits on the cOAlition S Leaders Group?

    The Leaders Group is made up of the heads of cOAlition S member organisations — national and regional research funders, philanthropic funders, and the European Commission. It approves overall strategy, agrees Plan S principles, and appoints the Executive Director and Executive Steering Group.

    What does the cOAlition S Executive Steering Group do?

    The Executive Steering Group turns Leaders Group strategy into an operational work plan and supervises the cOAlition S Office. It is chaired by the Secretary General of Science Europe, while the coalition’s Executive Director leads day-to-day delivery and public representation.

    Who is the current director of cOAlition S?

    Curt Rice was appointed Director of cOAlition S in May 2026, succeeding Johan Rooryck, who stepped down as Executive Director in July 2025 after six years leading the coalition through its period of fastest growth and expansion.

    Where is the cOAlition S secretariat based?

    The cOAlition S Secretariat is hosted by OPERAS AISBL, a research infrastructure for open scholarly communication based in Brussels, Belgium. It replaced the European Science Foundation as host and now provides operational, financial, and communications support to the coalition.

    Member Funders and the Science Europe Connection

    cOAlition S is frequently — and inaccurately — conflated with Science Europe, the Brussels-based association of European research funding and research-performing organisations. The two are formally distinct bodies with separate mandates, but the overlap in senior personnel is real and consequential: both the Leaders Group chair and the Executive Steering Group chair currently hold senior Science Europe positions.

    This overlap matters for institutions tracking policy signals. When Science Europe’s governing board discusses open-access principles, the same individuals frequently carry those positions into cOAlition S Leaders Group meetings, and vice versa. Research offices monitoring funder mandates should therefore treat Science Europe statements and cOAlition S announcements as related but not interchangeable — each body has its own decision process and its own binding effect on individual funders’ policies.

    Member funders span national research councils (Norway, Finland, France, Slovenia, Portugal, among others), the European Commission, and private philanthropic funders such as HHMI and ASAP. Each retains full legal responsibility for enforcing its own open-access policy — cOAlition S coordinates the principles, but compliance monitoring (for example through the Journal Checker Tool) happens at the level of the individual funder.

    What This Means for Institutions, Publishers, and Researchers

    For research administration and funder-compliance teams, the practical implication is that Plan S obligations are not centrally enforced. Institutions should track the specific published policy of whichever cOAlition S funder supports a given grant, rather than assuming a single unified cOAlition S rulebook applies everywhere.

    For publishers, the leadership transition to a new Director in 2026, alongside the secretariat’s move to OPERAS, signals a period of operational change rather than a shift in Plan S’s core open-access principles. The coalition entered a new 2026–2030 strategic phase that reaffirms open access while broadening its remit toward “rapid, open, transparent, and equitable” sharing of research more generally — a scope expansion worth watching for anyone tracking open-science mandates rather than open-access mandates narrowly.

    For anyone building funder-compliance workflows, the governance map is straightforward once separated into its three tiers: strategy (Leaders Group), implementation (Executive Steering Group and Director), and operations (Secretariat). Understanding which tier issued a given statement helps determine whether it reflects settled policy or an in-progress work plan.

  • What Is cOAlition S? A Guide to the Funder Coalition Behind Plan S

    What Is cOAlition S? (Quick Answer)

    So, what is cOAlition S? It is an international consortium of research funding and research-performing organisations that launched on 4 September 2018 to accelerate full and immediate open access to publicly funded research. It was announced jointly by a group of national research funders, with the backing of the European Commission and the European Research Council (ERC), and was co-initiated by Marc Schiltz, then President of Science Europe, and Robert-Jan Smits, at the time the European Commission’s Open Access Envoy.

    cOAlition S does not itself publish research or set library policy. It is the funder-side alliance that authored, endorses and operationally enforces a single open-access policy framework known as Plan S. Understanding that split — a coalition of institutions on one side, a compliance mandate on the other — is the single most useful fact for anyone trying to interpret a funder’s open-access requirements.

    cOAlition S vs Plan S: Why the Distinction Matters

    The two names are often used interchangeably in casual conversation, but they refer to different things. cOAlition S is a group of organisations; Plan S is the policy those organisations agreed to implement. Confusing the two leads to real compliance errors — for example, assuming that a funder is bound by Plan S because it is described alongside cOAlition S in a news article, when in fact membership and mandate adoption are two separate steps.

    Aspect cOAlition S Plan S
    What it is A consortium of funding and research-performing organisations A policy framework of one target and ten principles
    Launched 4 September 2018 4 September 2018 (announced alongside cOAlition S)
    Function Governs, funds and enforces the mandate Defines what “full and immediate open access” requires
    Core requirement Not applicable — the coalition is the implementing body Publications from funded research must appear in an open-access journal, platform or repository without embargo
    Who it binds Member funders, who then bind their grant-holders Researchers funded by a cOAlition S member, once that funder adopts the policy

    In short: if a researcher asks “does Plan S apply to my grant?”, the answer depends on whether their funder is a cOAlition S member and has implemented the policy in its grant conditions — not simply on whether the funder is mentioned in Plan S coverage.

    Origins, Governance and Membership

    cOAlition S grew out of frustration among European funders that voluntary open-access recommendations were not shifting publisher behaviour fast enough. The founding principle, published on launch day, states:

    “With effect from 2021, all scholarly publications on the results from research funded by public or private grants provided by national, regional and international research councils and funding bodies, must be published in Open Access Journals, on Open Access Platforms, or made immediately available through Open Access Repositories without embargo.”

    Membership expanded steadily after the 2018 launch. By its five-year anniversary in September 2023, cOAlition S had grown from around a dozen founding funders to a network of 28 funders spanning Europe and beyond. Notable participants and supporters over the years have included:

    • UK Research and Innovation (UKRI)
    • Wellcome Trust (joined November 2018)
    • Bill & Melinda Gates Foundation (joined November 2018)
    • Austrian Science Fund (FWF)
    • Academy of Finland
    • Research Council of Norway
    • Luxembourg National Research Fund (FNR)
    • National Health and Medical Research Council, Australia (NHMRC)

    Governance has not been static. The European Research Council backed cOAlition S at launch in 2018 but withdrew its formal support in July 2020, while remaining aligned with open-access goals more broadly — a reminder that “coalition member” status can change even after a funder has publicly endorsed the framework. cOAlition S’s day-to-day secretariat function has also evolved; the organisation operates under the European Science Foundation’s science-policy-support activities and has continued to update its operating structure, including a new strategy for 2026–2030 published in November 2025.

    Frequently Asked Questions

    What is Plan S?

    Plan S is the open-access policy framework created and endorsed by cOAlition S. It requires that, from 2021, all peer-reviewed publications resulting from grants awarded by a participating funder be made immediately and freely available, without embargo, in a compliant open-access journal, platform or repository.

    What does the “S” in Plan S stand for?

    According to Robert-Jan Smits, the plan’s chief architect, the “S” stands for “shock” — reflecting the coalition’s intent to jolt scholarly publishing into a faster transition to open access, rather than relying on the slower, voluntary approach that had dominated the previous two decades.

    How many funders belong to cOAlition S?

    Membership has grown considerably since 2018. cOAlition S expanded from roughly a dozen founding funders to a network of 28 funders by its five-year anniversary in September 2023, and the coalition continues to invite public and private research funders worldwide to join.

    Is cOAlition S a government body?

    No. cOAlition S is not a government agency; it is a voluntary alliance of research funders — national funding councils, the European Commission, and charitable foundations such as Wellcome Trust — that have agreed to coordinate their own grant conditions around a shared open-access target.

    Why the Distinction Matters for Compliance

    For research administrators, institutional open-access librarians and grants offices, the cOAlition S / Plan S distinction is not academic. Compliance obligations attach at the funder level, not automatically at the field or discipline level. Two practical consequences follow.

    • Check the funder, not the field. A researcher can work in a Plan S-adjacent discipline and still have no Plan S obligation, because their specific funder has not joined cOAlition S or has not yet implemented the policy in its own grant terms.
    • Track transitional allowances separately from the core mandate. During the transition period, Plan S permits publication in “transformative journals” — hybrid titles covered by an agreement to convert fully to open access — which sit outside the strict letter of the core principle but remain compliant under cOAlition S guidance.

    Because cOAlition S retains the authority to revise implementation guidance — including its Rights Retention Strategy, which lets funded authors apply a CC BY licence to the author’s accepted manuscript regardless of a publisher’s own policy — institutions need to monitor cOAlition S announcements directly rather than relying solely on secondary summaries.

    Looking Ahead: cOAlition S in 2026

    Plan S is often described in retrospective terms, as though the 2021 deadline closed the story. It did not. cOAlition S published a new strategy covering 2026–2030 in November 2025, signalling continued activity around rights retention, diamond open access and equitable publishing models rather than a wind-down. For institutions still mapping which of their funders carry a live Plan S obligation, the coalition’s own organisations page remains the authoritative, continuously updated source — far more reliable than any static list, including this one.

    Research administrators managing multi-funder compliance can pair that funder-by-funder check with CASRAI’s broader research administration resources for context on how open-access mandates fit within the wider compliance landscape institutions now navigate.

  • AI Grant Application Rules: A Compliance Checklist for Research Offices

    Research offices are fielding the same question from every principal investigator this cycle: what counts as acceptable AI grant application assistance, and what will get a proposal flagged? The honest answer is that funders have converged on a rough principle — AI can edit, but it cannot originate — while diverging sharply on enforcement, disclosure and consequences. Grammar-checking and language polishing with a large language model is now explicitly permitted almost everywhere. Using AI to draft the scientific argument, generate specific aims, or write an entire proposal is not, and that gap is where applications get rejected or, in NIH’s case, administratively withdrawn.

    This checklist reconciles the current rules from NIH, UKRI, the European Research Council (ERC) and NHMRC (with a note on NSF, since both funders publish closely watched AI guidance), and gives research administrators ready-to-adapt disclosure wording for applicants.

    AI-Assisted Editing vs AI-Generated Drafting

    Funder policies converge on a distinction between two categories of AI use, even where the exact wording differs.

    • AI-assisted editing: grammar and spelling correction, clarity and readability improvements to text the applicant has already written, translation, and administrative formatting. This is broadly permitted.
    • AI-generated drafting: producing the scientific rationale, specific aims, hypotheses, study design or an entire section without substantive human authorship. This is broadly prohibited, and in NIH’s case carries the risk of administrative withdrawal.

    Evaluation is treated as a separate, stricter category again. Every funder examined for this analysis — NIH, UKRI, ERC and NSF — bars peer reviewers from using generative AI to analyse, summarise or score applications, largely to protect the confidentiality of unpublished ideas.

    Funder Rules Compared: NIH, UKRI, ERC, NHMRC and NSF

    The table below summarises publicly stated positions as of mid-2026. Research offices should always check the current version of the cited policy, as several funders note their guidance will evolve.

    Funder Applicant drafting/editing use Full AI-generated content Disclosure required Peer reviewer AI use
    NIH Permitted for grammar, spelling and clarity only Prohibited; applications “substantially developed by AI” may be administratively withdrawn under NOT-OD-25-132 (effective 25 Sept 2025) No formal disclosure field; NIH uses AI-detection screening and caps most PIs at six applications/year Prohibited from using AI to analyse or critique applications
    UKRI Permitted for drafting, editing, idea generation and literature comparison Prohibited: applicants “must not use generative AI tools to generate an entire application, or sections of an application, without human involvement” Encouraged, not mandatory; disclosure does not affect assessment Prohibited from using generative AI in assessment
    ERC Permitted for brainstorming, literature searches, revising, translating and summarising Prohibited in substance: applicants retain “full and sole authorship responsibility”; text-similarity detection is used Not a separate mandatory statement Strict non-delegation policy: no AI summarising, assessing or draft-evaluation writing
    NHMRC Permitted for drafting, editing and organising ideas Applicant must verify accuracy against the Australian Code for the Responsible Conduct of Research; sensitive data must not enter public AI systems Not a separate mandatory statement Reviewers may use AI only to refine the wording of their own comments, not to evaluate or score
    NSF Permitted for proposal preparation assistance Proposers are responsible for accuracy of all content regardless of AI involvement Required: proposers must indicate the extent of generative AI use in the project description Reviewers barred from uploading proposal content to public AI tools (confidentiality breach)

    The Compliance Checklist for Research Offices

    Institutional research offices can use the following checklist when advising applicants ahead of submission.

    • Confirm which category the intended AI use falls into — editing/formatting versus content generation — before the applicant starts drafting.
    • Check the specific funder’s current AI policy page rather than relying on last year’s guidance; NIH, UKRI and NSF have all updated their positions since 2023.
    • Where disclosure is required (NSF) or encouraged (UKRI), draft the disclosure statement early and route it through the same sign-off as conflict-of-interest and human-subjects certifications.
    • Warn applicants against pasting unpublished proposal content, preliminary data, or collaborator information into free or public AI tools — this risks both confidentiality breaches and, in the EU/UK, data protection exposure.
    • Never advise applicants to use AI for peer-review-adjacent tasks such as scoring their own proposal in a way that substitutes for genuine self-assessment.
    • Keep a record of which AI tools were used and for what purpose, in case a funder requests it during a research-integrity enquiry.

    Common Questions on AI Use in Grant Applications

    Can I use ChatGPT to write my grant application?

    Most funders allow ChatGPT and similar tools for grammar checks, clarity edits and brainstorming, but not for drafting the scientific argument or specific aims. NIH, UKRI, ERC and NHMRC all place ultimate authorship responsibility on the applicant, so a proposal substantially generated by AI risks rejection.

    Does NIH allow AI-generated grant applications?

    No. Under NOT-OD-25-132, effective 25 September 2025, NIH treats applications or sections “substantially developed by AI” as not reflecting the applicants’ original ideas, and such submissions may be administratively withdrawn. NIH also screens for AI use and caps most principal investigators at six new or resubmitted applications per year.

    Do I need to disclose AI use in a grant application?

    It depends on the funder. NSF requires applicants to state the extent of generative AI use directly in the project description. UKRI encourages disclosure without penalty at assessment. ERC and NHMRC do not mandate a disclosure statement but still hold the applicant fully accountable for all AI-assisted content submitted.

    Can grant peer reviewers use AI to assess applications?

    Generally no. NIH, UKRI, ERC and NSF all prohibit reviewers from using generative AI to analyse, summarise or score proposals, largely to protect confidentiality and prevent unpublished ideas reaching public tools. NHMRC allows a narrow exception: reviewers may use AI only to polish the wording of their own comments.

    Template Disclosure Language for Applicants

    Research offices are repeatedly asked for standard wording applicants can adapt rather than draft from scratch. Two templates cover the main scenarios.

    Where disclosure is required or requested (NSF/UKRI-style):

    “Generative AI (tool: [name and version]) was used to [check grammar and clarity / generate an initial outline] of Sections [X]. All scientific content, analysis and conclusions are the original work of the named investigators, who take full responsibility for the accuracy and integrity of this application.”

    Where disclosure is not mandatory but institutions want a defensive record (ERC/NHMRC-style, kept on file):

    “The applicants used [tool name] to assist with editing and language clarity only. No AI tool was used to generate the scientific rationale, methodology, hypotheses or original data interpretation contained in this application.”

    Neither template substitutes for reading the specific solicitation text, which occasionally adds requirements beyond the funder’s general policy.

    Implications for Research Offices

    The practical challenge is that these policies are not converging on common language, so a one-size-fits-all institutional AI policy will misfire on at least one major funder. A UKRI-style permissive default with encouraged disclosure would not protect a PI from NIH’s administrative-withdrawal risk, and an NIH-style prohibition would leave NSF’s mandatory disclosure field unanswered.

    • Build funder-specific AI guidance into pre-award checklists rather than a single institution-wide statement.
    • Treat AI-use attestations the same way as financial conflict-of-interest disclosures — logged, dated and retrievable if a funder investigates later.
    • Extend research-integrity training to cover AI-specific risks: fabricated citations, hallucinated preliminary data, and inadvertent disclosure of unpublished ideas to public tools.
    • Coordinate with research administration leadership on how AI-use records intersect with existing misconduct and compliance processes.

    What Comes Next

    Evidence on outcomes is starting to complicate the compliance picture. A February 2026 Nature analysis found AI-drafted NIH proposals were more likely to be funded, but that funded proposals using AI assistance also tended to read more similarly to one another — a finding likely to sharpen funder scrutiny of homogenised language rather than loosen it. Expect NIH’s detection and application-limit measures to be tested over the next funding cycle, while UKRI, ERC and NHMRC continue to state their guidance will be revisited as the evidence base evolves. The safest institutional posture for now is documented, funder-specific caution: assume editing is safe, assume drafting is not, and keep a paper trail either way.

  • UNESCO Recommendation on the Ethics of Artificial Intelligence: A Practical Guide for Research Offices

    When UNESCO’s 193 member states adopted the UNESCO Recommendation on the Ethics of Artificial Intelligence in November 2021, they created the first global standard-setting instrument on AI ethics — a non-binding but politically significant commitment that now shapes how governments, funders, and universities frame AI governance. For UK research offices navigating a fast-moving 2025 landscape of generative AI in teaching, assessment, and research integrity, the Recommendation functions less as law and more as reference architecture: a shared vocabulary of values, principles, and assessment tools that institutional AI ethics committees can adopt directly. This guide sets out what states actually committed to, how the UK’s 2025 sector guidance on generative AI in higher education sits underneath it, and a practical checklist for putting the framework to work.

    What the Recommendation actually commits states to

    The Recommendation on the Ethics of Artificial Intelligence was adopted by consensus at UNESCO’s 41st General Conference in November 2021. Because it is a “recommendation” rather than a “convention” under UNESCO’s constitutional instruments, it does not create binding treaty obligations. Instead, member states — including the UK — accept a political commitment to report periodically on implementation and to translate the framework into domestic law, sector guidance, and institutional policy.

    UNESCO backs this with three implementation mechanisms that research offices should know by name:

    • The Global AI Ethics and Governance Observatory, a public resource tracking national AI readiness and policy.
    • The Readiness Assessment Methodology (RAM), used by governments to benchmark institutional and legal preparedness for ethical AI governance.
    • The Ethical Impact Assessment (EIA), a procedural tool for identifying and mitigating the human-rights and environmental risks of a specific AI system before deployment.

    None of these tools are mandatory for individual universities. But because national governments are expected to operationalise them, they increasingly surface indirectly — through funder terms, procurement frameworks, and research-integrity codes that reference UNESCO’s language of proportionality, transparency, and human oversight.

    The four values and ten principles

    The Recommendation is built on four foundational values, each translated into operational principles that give research administrators a concrete checklist rather than an abstract statement of intent.

    Value What it means for a research office
    Human rights and human dignity AI tools used in admissions, peer review, or research assessment must not override due process or discriminate against protected groups.
    Peaceful, just and interconnected societies International collaboration and data-sharing agreements should respect national sovereignty and diverse legal frameworks.
    Diversity and inclusiveness AI benefits and risks in research infrastructure should be distributed equitably across disciplines, career stages, and institution types.
    Environment and ecosystem flourishing Procurement decisions for compute-intensive AI research tools should weigh carbon and energy costs, not only capability.

    These values are operationalised through ten principles: proportionality and do no harm; safety and security; privacy and data protection; multi-stakeholder and adaptive governance; responsibility and accountability; transparency and explainability; human oversight and determination; sustainability; awareness and literacy; and fairness and non-discrimination. Ethics committees drafting or reviewing an institutional AI policy can map each clause of that policy directly onto one of these ten principles to check for gaps.

    The 2025 UK picture: generative AI in education and research

    The UK, as a UNESCO member state, does not have a standalone statute implementing the Recommendation. Instead, its principles surface across a cluster of UK sector guidance that has matured significantly since 2023, with updated 2025 iterations addressing generative AI specifically.

    Body Guidance Primary relevance
    Department for Education Generative AI in education policy position, revised through 2025 Safeguarding, safety expectations, and sector-wide product standards
    Russell Group Principles on the use of generative AI tools in education (2023, updated) Academic integrity, staff and student AI literacy
    QAA Guidance for UK higher education providers on generative AI Assessment design and integrity in a generative-AI context
    JISC National baseline surveys and guidance on AI in tertiary education Sector-wide adoption tracking and practical toolkits
    UKRI Positions on AI use in funding applications and peer review Research integrity in grant assessment and reviewer conduct

    None of these UK instruments cite UNESCO’s Recommendation as a formal legal source. But the substantive overlap is close: Russell Group and QAA guidance on transparency in AI-assisted work mirrors principle six (transparency and explainability); UKRI’s expectations around reviewer accountability mirror principle five (responsibility and accountability); and DfE safeguarding provisions mirror the Recommendation’s proportionality and do-no-harm principle. For a research office, the practical implication is that UNESCO’s framework offers the common vocabulary that lets institutions reconcile these separately issued, sector-specific instruments into one coherent AI governance policy rather than several overlapping ones.

    Common questions on the UNESCO AI ethics Recommendation

    Is the UNESCO Recommendation on the Ethics of Artificial Intelligence legally binding?

    No — as a UNESCO Recommendation rather than a Convention, it is not legally binding on the 193 member states that adopted it in November 2021. States are politically committed to submit periodic implementation reports and to adapt the framework through domestic law, institutional policy, and the Readiness Assessment Methodology.

    What are the four core values of the UNESCO AI ethics Recommendation?

    The Recommendation rests on four values: respecting human rights and human dignity, fostering peaceful and interconnected societies, ensuring diversity and inclusiveness, and supporting environmental and ecosystem flourishing. Ten operational principles, spanning transparency, accountability, proportionality, and human oversight, translate these values into concrete institutional practice for research offices.

    What is UNESCO’s Ethical Impact Assessment tool?

    The Ethical Impact Assessment (EIA) is a structured procedure UNESCO developed to help institutions identify, weigh, and mitigate the human-rights, environmental, and social risks of an AI system before and during deployment. Research offices can adapt the EIA template for grant, procurement, and research-tool sign-off processes.

    The Recommendation supplies the underlying values and principles; UK sector bodies, including the Department for Education, the Russell Group, QAA, and JISC, translate them into practical 2025 guidance on assessment integrity, safeguarding, and the responsible adoption of generative AI across teaching, research, and research administration.

    How institutional AI ethics committees should use it

    An institutional AI ethics committee does not need to treat the Recommendation as a document to comply with line by line. It is more useful as a diagnostic framework for auditing existing policy and closing gaps. A practical sequence:

    1. Map every significant AI use case across the research lifecycle — grant triage, peer review support, research-data processing, plagiarism and integrity checks, and public engagement.
    2. Run an Ethical Impact Assessment, adapted from UNESCO’s EIA methodology, for each use case that touches personal data, funding decisions, or assessment outcomes.
    3. Assign a named human-oversight owner for each AI system, consistent with principle seven (human oversight and determination), so no automated output is treated as final without human review.
    4. Publish a short transparency statement disclosing where and how generative AI is used in institutional processes, satisfying principle six.
    5. Cross-reference the committee’s own policy against current Russell Group, QAA, and DfE guidance at least annually, since UK sector positions on generative AI are still being revised.
    6. Record decisions and rationale for auditability — the same accountability logic that underpins principle five.

    Research administration teams drafting these policies may also find it useful to align terminology with the research administration pillar and to cross-check definitions of related governance terms in the CASRAI Dictionary when drafting institutional glossaries for AI policy documents.

    What comes next

    UNESCO’s Recommendation was never designed to be self-executing; its value lies in giving 193 states — and, by extension, their universities and funders — a common ethical baseline to build from. In the UK, that baseline is increasingly visible not as a single “AI ethics law” but as a patchwork of DfE, Russell Group, QAA, JISC, and UKRI guidance that is still being updated as generative AI capabilities evolve through 2025 and beyond. Institutional AI ethics committees that map their own policies against UNESCO’s four values and ten principles now will be better placed to absorb whatever the next round of UK sector guidance requires, rather than rebuilding their governance framework from scratch each time a new instrument is published.

  • AI Growth Zones Explained: What They Mean for University Research Infrastructure

    The UK government’s AI Growth Zones programme is no longer just a policy paper — it is now five confirmed sites, a dedicated Delivery Unit, and a package of grid, planning and pricing incentives worth up to £100 billion in projected investment. For university leaders weighing whether to bid into a zone, partner with an anchor developer, or simply understand what “zone status” changes for regional compute access, the detail in the November 2025 Delivering AI Growth Zones policy paper matters more than the headline announcements.

    What Are AI Growth Zones?

    AI Growth Zones (AIGZs) are UK government-designated sites intended to fast-track the build-out of AI-enabled data centres and their supporting infrastructure. The concept originated in the AI Opportunities Action Plan, published in January 2025, which set a target of expanding the UK’s sovereign compute capacity at least twentyfold by 2030.

    To qualify, a site typically needs access to at least 500 megawatts (MW) of power, together with a credible route through planning. In return, government channels three main levers toward a designated zone:

    • Grid priority — reserved and reallocated connection capacity created under new mechanisms tied to the Planning and Infrastructure Bill.
    • Energy pricing support — a targeted electricity discount for zones that ease network constraints.
    • Planning acceleration — updated national planning guidance, added specialist capacity, and faster consenting for Nationally Significant Infrastructure Projects.

    Where Are the UK’s AI Growth Zones?

    Five zones have been confirmed since the pilot was announced in January 2025, spanning England, Wales and Scotland:

    Zone Status Anchor site / partner Notable feature
    Culham, Oxfordshire Pilot (announced Jan 2025) UK Atomic Energy Authority (UKAEA) campus Began at 100MW, scaling toward 500MW; testbed for public-private compute delivery
    North East England Confirmed Sept 2025 Cobalt Park and Blyth, Northumberland Anchor site for OpenAI’s Stargate UK project
    North Wales Confirmed Linked to Small Modular Reactor (SMR) development and local universities Nuclear-adjacent power supply strategy
    South Wales Confirmed Digital infrastructure corridor Builds on existing fibre and industrial land
    Lanarkshire, Scotland Confirmed Jan 2026 North Lanarkshire Scotland’s first AI Growth Zone; over 3,400 jobs projected plus community and skills funding

    More than 200 local and regional authorities registered interest when bidding opened in February 2025, and government has said further zones will be confirmed as bids progress — so this list is a snapshot, not a final map.

    Compute Siting, Energy Discounts and What Zone Status Delivers

    The Delivering AI Growth Zones policy paper (13 November 2025) is explicit that grid access, not land or planning alone, is the binding constraint on UK data centre build-out. Government has pledged reforms it says will cut time-to-power by up to five years for zone-sited projects.

    A targeted pricing support mechanism, subject to legislation, is due to apply from April 2027, with a review point in 2030. For a 500MW data centre, this recycles grid-constraint savings into a regional electricity discount:

    Region Electricity discount (per MWh)
    Scotland Up to £24
    Cumbria Up to £16
    North East England Up to £14

    Government estimates this could save a single 500MW site up to £80 million a year in electricity costs. Local authorities hosting a zone in England will also retain 100% of business rate growth for 25 years from April 2027 — worth an estimated £5–10 million per site annually once complete — administered through a new AI Growth Zone Delivery Unit inside the Department for Science, Innovation and Technology (DSIT), which acts as a single point of contact for investors and developers.

    None of this is guaranteed simply by being near a zone. The discounts and fast-tracked consenting attach to the data centre operator and the specific designated site — not automatically to every institution or business in the surrounding region.

    What This Means for Universities and Research Infrastructure

    Universities sit on both sides of the AI Growth Zone equation: as potential bid partners helping local authorities make the case for a site, and as institutions that stand to benefit — or not — from the compute, skills funding and jobs a confirmed zone brings.

    The bidding pattern to date has been consortium-led. When the University of York and North Yorkshire Council submitted a joint AI Growth Zone bid in 2025 alongside private-sector partners, it followed the model government has encouraged: local authority as lead applicant, university as research and skills anchor, private developer as capital and technical partner. Culham’s pilot zone similarly pairs a public research body, UKAEA, with a commercial data centre developer.

    It is worth being precise about what a zone actually funds for a university partner. Three separate funding lines apply:

    • Local AI adoption funding — up to an initial £5 million per confirmed AI Growth Zone, for local schemes covering R&D commercialisation and start-up scaling.
    • Skills infrastructure — the £187 million national TechFirst programme, short AI courses via the Growth and Skills Levy, and five new digital Technical Excellence Colleges.
    • Compute access itself — which is not automatically bundled with zone status. The commercial data centres built inside a zone serve the operator’s own customers unless a specific public-private agreement, as at Culham, reserves capacity for public research use.

    That last distinction matters and is frequently blurred in coverage of the scheme. AI Growth Zones are an industrial-siting and energy policy, designed to get commercial data centre capacity built faster in Britain. They are a different instrument from the National AI Research Resource (AIRR), the UKRI-backed programme that funds shared compute facilities specifically for academic and public-sector researchers, including Isambard-AI at the University of Bristol and Dawn at the University of Cambridge. A university in or near an AI Growth Zone gains proximity, jobs and skills funding, and potentially a negotiating position with an anchor developer — it does not automatically gain a share of that developer’s compute unless that access is separately contracted.

    For research administrators and institutional leaders, the practical questions when a zone is proposed or confirmed nearby are therefore: who leads the bid consortium; what specific compute, skills or R&D commitments the anchor developer has made in writing; and how any AIRR-funded facility relates to, or is entirely separate from, the zone’s commercial capacity.

    How do universities get involved in an AI Growth Zone bid?

    Universities typically join as consortium partners to a local authority-led bid, contributing research credibility and skills pipelines. The University of York and North Yorkshire Council bid followed this model, alongside private-sector capital and technical partners.

    Are AI Growth Zones the same as the National AI Research Resource?

    No. AI Growth Zones are an industrial-siting and energy policy for commercial data centres, while the National AI Research Resource is a separate UKRI-backed compute programme for academic researchers, including facilities at Bristol and Cambridge.

    Which UK regions currently have confirmed AI Growth Zone status?

    As of mid-2026, confirmed zones include Culham (Oxfordshire), North East England, North and South Wales, and Lanarkshire, Scotland. Further sites are expected as government works through more than 200 registered local-authority bids.

    What electricity discount do AI Growth Zone data centres receive?

    From April 2027, subject to legislation, eligible 500MW data centres can receive discounts of up to £24/MWh in Scotland, £16/MWh in Cumbria, and £14/MWh in the North East, with a review point in 2030.

    The Delivery Unit’s pipeline is still moving: further zone confirmations are expected through 2026 as more of the 200-plus registered bids are assessed. For institutions weighing a role — as bid partner, skills provider, or negotiating occupant — the operative lesson from Culham, the North East and Lanarkshire is the same: zone status changes the investment and energy case for a commercial data centre; it does not, by itself, change what compute a university can access. Read more on research infrastructure funding and governance in CASRAI’s research administration resources, and consult the CASRAI Dictionary for definitions of related research-computing and data-governance terms.

  • AI Act High-Risk Classification: Annex III for Academic AI Systems

    Admissions-screening tools, exam proctoring software, and subject-profiling systems built or bought by universities are now squarely inside EU compliance scope. AI Act high-risk classification is not a label a developer chooses — it is determined by a fixed legal test set out in Article 6 and Annex III of Regulation (EU) 2024/1689, and most research-facing AI that touches education, employment, or biometric data will fail that test into the “high-risk” tier by default. This walkthrough applies the actual Annex III criteria to the tools research administrators are most likely to encounter.

    What “High-Risk” Means Under the AI Act

    The AI Act sorts systems into four tiers: unacceptable risk (banned outright, applicable since 2 February 2025), high risk, limited risk (transparency duties only), and minimal risk (unregulated). There is no fifth “probably fine” category for academic tools — a system either clears the high-risk gate or it does not.

    Article 6 sets two separate high-risk routes. The first covers AI embedded as a safety component in products already regulated under Annex I product-safety law (medical devices, machinery, and similar). The second — the one that catches nearly all academic and research-administration tools — is Annex III: a fixed list of use cases that are always considered high-risk unless a narrow exemption applies.

    Obligations tied to the Annex III route generally apply from 2 August 2026; the Annex I product-safety route gets an extra year, to 2 August 2027. Providers who conclude their own Annex III system is not high-risk must document that assessment and register it before deployment (Article 6(4)) — silence or informal judgement calls are not a defence.

    The Annex III Walkthrough for Academic AI Systems

    Annex III groups high-risk triggers into eight domains. Three of them account for almost every academic AI tool that raises a compliance question: education, employment, and biometrics. The table below maps common research-institution tools to the specific Annex III clause they hit.

    Annex III category Example academic AI use Clause Verdict
    Education and vocational training Admissions-ranking or applicant-screening AI Annex III(3)(a) High-risk
    Education and vocational training Automated scoring that steers a student’s learning path Annex III(3)(b) High-risk
    Education and vocational training Exam-proctoring software flagging “prohibited behaviour” Annex III(3)(d) High-risk
    Employment and workers management AI screening postdoc, faculty, or research-staff applications Annex III(4)(a) High-risk
    Biometrics Emotion-recognition tool used in a lecture-engagement study Annex III(1)(c) High-risk, unless a narrow medical/safety exception applies
    Biometrics Biometric categorisation inferring ethnicity or political views from images for research subject profiling Annex III(1)(b) High-risk — the profiling override applies (see below)

    Education and Training Triggers

    Annex III(3) lists four education triggers: access/admission decisions, evaluation of learning outcomes, assessment of the appropriate level of education a person can access, and monitoring of prohibited behaviour during tests. A tool only needs to match one clause — not all four — to be caught. An institutional dashboard that merely displays grades without influencing a decision is unlikely to trigger this route; a model that ranks or filters applicants does.

    Employment and Research-Staff Triggers

    Annex III(4) covers recruitment and selection tools (targeted job adverts, CV filtering, candidate scoring) and tools used in promotion, termination, task allocation, or performance monitoring of an existing workforce. Research institutions using AI to shortlist grant-funded researchers, PhD candidates, or lab staff sit inside this trigger on the same footing as any commercial employer.

    Biometric-Categorisation and Profiling Triggers

    Annex III(1) is the sharpest edge for research tools. It covers remote biometric identification, biometric categorisation systems that infer sensitive attributes (race, political opinion, trade union membership, religious belief, sex life, or sexual orientation) from biometric data, and emotion-recognition systems — with only a narrow carve-out for systems used purely for medical or safety purposes. A study instrument that infers demographic or affective attributes from facial or voice data for research subject profiling falls inside this trigger even when the researchers’ intent is purely academic.

    The Narrow-Task Exemption — and Why Profiling Overrides It

    Article 6(3) gives Annex III systems four possible escapes: performing a narrow procedural task, improving the result of work a human already completed, detecting deviations from a prior human decision without replacing it, or performing a preparatory task before a human assessment. A system that clears one of these tests can be treated as not high-risk — but the provider must still document that judgement.

    Critically, Article 6(3) carries an override that most summaries omit: an Annex III system is always high-risk if it performs profiling of natural persons, regardless of whether it would otherwise qualify for the narrow-task exemption. Profiling is defined broadly under EU data-protection law as automated processing used to evaluate personal aspects such as performance, behaviour, preferences, or location. Any admissions tool, proctoring tool, or research instrument that builds a profile of an individual cannot use the narrow-task escape — it is high-risk by default.

    One further nuance matters for university researchers: Article 2 excludes AI systems developed and used solely for scientific research and development from the Regulation’s scope entirely. That exclusion evaporates the moment the same tool is deployed operationally — for example, adapted from a lab study into a live admissions or proctoring product.

    Common Questions on AI Act High-Risk Classification

    What is high risk under the AI Act?

    An AI system is high-risk if it is a safety component of a product regulated under Annex I, or if its use case appears in Annex III — covering biometrics, education, employment, and public services — unless a documented Article 6(3) exemption applies and no profiling occurs.

    What are the four levels of risk in the AI Act?

    The Regulation defines four tiers: unacceptable risk (prohibited outright), high risk (strict pre-market obligations), limited risk (transparency duties, such as disclosing AI-generated content), and minimal risk (largely unregulated, voluntary codes only).

    What are high-risk use cases under the AI Act?

    Annex III lists eight domains: biometrics, critical infrastructure, education and vocational training, employment and worker management, access to essential services, law enforcement, migration and border control, and administration of justice or democratic processes.

    What are examples of high-risk AI systems?

    Documented examples include admissions-screening AI, exam-proctoring software, CV-filtering recruitment tools, creditworthiness scoring, biometric categorisation systems, and remote facial-recognition identification tools used by public authorities.

    Implications for Research Administrators and Developers

    For institutions procuring or building these tools, the practical checklist is short but unforgiving:

    • Map every AI tool touching admissions, assessment, proctoring, staff recruitment, or biometric data against the specific Annex III clause it might trigger.
    • Test each candidate system against the four Article 6(3) exemption conditions — and check separately whether it performs profiling, which overrides any exemption.
    • Document the classification assessment before deployment, even where the conclusion is “not high-risk,” and be ready to produce it to national competent authorities on request.
    • Re-run the assessment when a research prototype moves from the Article 2 scientific-research exclusion into operational use — the exclusion does not travel with the tool.
    • Track the compliance calendar: prohibited-practice bans applied from 2 February 2025; most Annex III obligations apply from 2 August 2026; the Annex I product-safety route follows in August 2027.

    Research administrators sit at the intersection of procurement, ethics review, and data governance, which makes this classification exercise an institutional responsibility rather than a vendor’s alone. Bodies advancing research-administration practice — including CASRAI’s research administration resources — increasingly treat AI-tool risk mapping as a standard due-diligence step alongside existing data-protection and research-integrity checks, and institutions building internal glossaries can cross-reference definitions of profiling, biometric categorisation, and related terms in the CASRAI dictionary.

    The European Commission is due to publish detailed implementation guidelines and worked examples for Article 6 classification. Until then, the safest institutional posture is to assume Annex III applies wherever an academic AI system reaches a decision about a person’s access, evaluation, employment, or biometric profile — and to document the reasoning either way.

  • AI Literacy Obligation Article 4: Staff Training Checklist

    The AI literacy obligation Article 4 of the EU Artificial Intelligence Act has applied since 2 February 2025, yet many research institutions still treat it as a future item rather than a live requirement. Article 4 states that providers and deployers of AI systems “shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf.” For universities, funders, and research institutes running admissions tools, grant-screening software, or generative AI assistants, that duty is already in force. This article sets out a practical checklist for what “sufficient” means in a research setting, who it covers, and how to evidence it.

    What Article 4 actually requires

    Article 4 does not prescribe a fixed curriculum, test, or certificate. It requires institutions to calibrate AI literacy measures against staff technical knowledge, experience, education, training, and the context in which AI systems are used. The obligation applies to both “providers” (organisations that develop and place an AI system on the market) and “deployers” (organisations using an AI system for a professional purpose).

    A research institution can be either, sometimes simultaneously. A department that builds a bespoke research-data classification model is a provider of that system; the same university using an off-the-shelf tool for applicant screening or meeting transcription is a deployer. The AI Act scope includes a narrow exemption for systems developed and used exclusively for scientific research before market placement — but once a research tool is deployed operationally, that exemption falls away and Article 4 applies in full.

    The statutory definition of AI literacy, in Article 3(56), is: skills, knowledge and understanding that allow providers, deployers and affected persons to make an informed deployment of AI systems, and gain awareness of the opportunities and risks it can cause. The Commission’s AI Office has confirmed it will not impose a single mandatory format.

    Who counts as “staff” under the AI literacy obligation

    This is where most institutions under-scope their programmes. The Commission’s guidance clarifies that “staff and other persons” is broader than payroll headcount — it extends to anyone acting on the institution’s behalf, including contractors and service providers. In a research setting that typically means:

    • Researchers and PIs using generative AI tools for literature review, drafting, or data analysis
    • Research administrators and grants officers using AI-assisted screening or compliance-checking tools
    • HR and admissions staff using AI-based applicant or candidate screening systems
    • IT and research-computing staff configuring or maintaining institutional AI deployments
    • External contractors or visiting researchers granted access to institutional AI systems
    • Board and senior leadership, who need enough literacy to assess institutional AI risk

    Article 4 does not require every group to receive identical training: a developer configuring a high-risk system needs materially more depth than an administrator using a transcription tool. The table below illustrates how role and system risk map onto training depth.

    Personnel category Typical AI Act role Indicative literacy depth
    Data scientists building institutional AI tools Provider Technical: limitations, bias, risk mitigation, documentation
    Grants/admissions staff using screening AI Deployer Operational: output interpretation, human oversight, escalation
    Researchers using generative AI for drafting/analysis Deployer (or exempt if pre-market research use) General awareness: hallucination risk, confidentiality
    Contractors/visiting staff with system access “Other persons” acting on the institution’s behalf Baseline awareness proportionate to access level
    Governing board/senior leadership Deployer (oversight capacity) Strategic: risk appetite, resourcing, regulatory exposure

    Building a defensible AI literacy programme

    The Commission’s AI Literacy Q&A sets out a four-step minimum approach research institutions can adapt directly:

    • Establish a general understanding of AI within the organisation — what AI is used, where, and why
    • Determine the institution’s role for each system: provider, deployer, or both
    • Assess the risk level of each AI system in use, including any high-risk systems under Chapter III
    • Build literacy actions proportionate to staff knowledge gaps and the context of use

    Relying solely on a vendor’s instructions for use is explicitly insufficient — this mirrors the separate Article 26 duty on deployers of high-risk systems to ensure staff are “sufficiently trained” to exercise human oversight.

    What is Article 4 of the EU AI Act?

    Article 4 is the AI Act provision requiring providers and deployers to ensure a “sufficient level” of AI literacy among staff and other persons operating AI systems on their behalf. It has applied since 2 February 2025, ahead of most other AI Act obligations, and covers technical knowledge, training context, and the persons affected by the system.

    What is the definition of AI literacy in the EU AI Act?

    Article 3(56) defines AI literacy as the skills, knowledge and understanding that let providers, deployers and affected persons make informed decisions about deploying AI systems, while remaining aware of the opportunities, risks, and potential harms those systems can cause in their specific context of use.

    Who at a research institution needs AI literacy training under Article 4?

    Anyone dealing with an AI system’s operation or output on the institution’s behalf, not just IT or data-science staff. This includes researchers, administrators, HR and admissions teams, contractors, and senior leadership — with training depth proportionate to each group’s technical role and the risk level of the system they use.

    Does Article 4 require a certificate or formal training records?

    No. The AI Office has confirmed there is no mandated test or certificate for Article 4 compliance. Institutions should instead keep an internal record of training delivered, attendance, and content — evidence that becomes essential if a national market surveillance authority later reviews compliance.

    Documenting compliance: what evidence to keep

    Because Article 4 sets no certification standard, the practical question is evidential: what would a research institution show a national market surveillance authority if asked? A defensible record typically includes:

    • A written AI inventory identifying each system in use, its provider/deployer classification, and risk tier
    • Training content and delivery records (dates, attendance, format) mapped to each staff category
    • A documented rationale for why the chosen literacy measures were “sufficient” for each role and system
    • An AI use policy communicated to staff, contractors, and other relevant persons
    • A review cycle — literacy measures should be revisited as systems and risk profiles change

    National market surveillance authorities take over enforcement of Article 4 from 2 August 2026, when the AI Act’s general application and high-risk provisions take effect. Enforcement is meant to be proportionate — gravity, intent, and negligence are all considered — but an incident with no evidence of staff training is likely to weigh against an institution.

    Implications for research institutions and GPAI tools

    Research institutions increasingly deploy general-purpose AI (GPAI) tools — large language model assistants embedded in research-writing or literature-search workflows — rather than narrow, purpose-built systems. The AI Act GPAI provisions (Chapter V, applying to GPAI model providers from 2 August 2025) sit alongside, not instead of, the Article 4 duty on deployers: using a GPAI-based writing assistant still makes an institution a deployer under Article 4, regardless of the model provider’s own transparency obligations.

    One development worth tracking: the European Commission’s 2025 Digital Omnibus proposal would shift part of the Article 4 burden from individual organisations toward Member States and the Commission itself. That proposal has not been adopted, so the current text of Article 4 remains binding on institutions as providers or deployers — institutions should not defer action for a change that may not materialise as proposed.

    Sector signals reinforce the direction of travel: UK courts have separately expected legal professionals to demonstrate AI competence in submissions, and UK financial regulators have referenced the Senior Managers Regime in connection with AI risk oversight — evidence that “sufficient AI literacy” is becoming an expectation beyond the AI Act’s direct territorial reach.

    What research institutions should do next

    Institutions without a formal AI literacy programme should start with an AI system inventory, classify each system by provider/deployer role and risk tier, then build tiered training from that analysis rather than buying a generic module. Article 4 rewards documented judgement over box-ticking: the institutions best placed for enforcement from August 2026 will be those that can show a reasoned, evidenced, role-differentiated approach. Research-administration functions, which typically already own institutional policy documentation, are well positioned to lead this work alongside data protection, IT governance, and research integrity offices.