CASRAI Dictionary

Author: MCP Service

  • Journal Finder Tools Compared for Plan S Authors

    Springer, Elsevier, Wiley and Taylor & Francis each run a free journal finder that matches a manuscript’s title, abstract or keywords to journals in their own portfolio — but none of them checks Plan S open-access compliance. That verification step belongs to cOAlition S’s separate Journal Checker Tool, which authors should run after shortlisting journals, not instead of it.

    A journal finder is a publisher-run search tool that recommends candidate journals for a manuscript by matching its subject area, title or abstract text against that publisher’s own list of active titles. This distinction matters more than it first appears: a Plan S-funded author who only uses a publisher’s finder can end up with a well-matched journal that is not, in fact, a compliant venue for their grant.

    What Do Publisher Journal Finder Tools Actually Do?

    Every major publisher-run journal finder performs the same core function: it takes a manuscript’s title, abstract or keywords and returns a ranked list of journals from that publisher’s own portfolio likely to fit the manuscript’s scope. None of them search across competing publishers, and none independently verify a journal’s open-access route against a specific funder’s mandate.

    • Input is usually a title, abstract or a short set of keywords, sometimes with a subject-area filter.
    • Output is a ranked shortlist, often annotated with impact metrics, acceptance rate or review speed.
    • Coverage is limited to titles the publisher itself owns or manages — this is the single biggest limitation for cross-publisher comparison shopping.

    How Do Springer, Elsevier, Wiley and Taylor & Francis Compare?

    Elsevier’s Journal Finder lets authors search by journal title, subject area or aims and scope, or run a “match my abstract” search against Elsevier’s own journal list. Springer Nature’s Journal Suggester, reached via the Springer Nature Link journals hub, matches manuscript details against the combined Springer, Nature, BMC and Palgrave Macmillan portfolio and surfaces open-access funding options alongside journal suggestions. Wiley’s Journal Finder states on its own page that it lets authors “search and filter across 1,800+ journals” by keyword, subject or abstract match. Taylor & Francis’s Journal Suggester, hosted on its Author Services site, uses a short five-question, AI-assisted form to recommend titles from the Taylor & Francis and Routledge list.

    Tool Provider Input method Portfolio scope Checks Plan S compliance? Best for
    Journal Finder Elsevier Title/abstract match, subject/scope search Elsevier’s own journals No Fast shortlisting within Elsevier imprints
    Journal Suggester Springer Nature Title, abstract or keyword input Springer, Nature, BMC, Palgrave Macmillan No (shows OA funding options, not funder-mandate checks) Authors targeting Springer Nature imprints
    Journal Finder Wiley Keyword, title or abstract search, with filters 1,800+ Wiley journals Partial — separate Wiley Author Compliance Tool checks funder policy Discipline-specific filtering within Wiley’s list
    Journal Suggester Taylor & Francis Five-question AI-assisted form Taylor & Francis / Routledge portfolio No Quick AI-generated shortlist
    Scopus Source Search Elsevier (Scopus) Lookup by ISSN or title, not manuscript matching Scopus-indexed sources, cross-publisher No Verifying CiteScore or indexing status of a journal already in mind
    Journal Checker Tool cOAlition S Funder, institution and journal input Any journal, cross-publisher Yes — this is its sole purpose Confirming a compliant open-access route before submission

    Does Scopus Have Its Own Journal Finder?

    Scopus, Elsevier’s abstract-and-citation database, does not run a manuscript-matching journal finder in the way Elsevier, Springer Nature, Wiley or Taylor & Francis do. Its Scopus Source Search instead looks up journals you already have in mind, by ISSN or title, to confirm indexing status and metrics such as CiteScore.

    Authors who search “journal finder scopus” are usually trying to do one of two different things, and conflating them causes wasted time. If the goal is to discover new candidate journals for a manuscript, a publisher’s own finder (or a cross-publisher tool such as JournalGuide) is the right starting point. If the goal is to confirm that a journal you have already chosen is Scopus-indexed, Scopus Source Search is the correct tool, not a substitute for journal discovery.

    Do Any of These Tools Check Plan S Compliance?

    Not directly, with one partial exception. Plan S, launched by cOAlition S in 2018 and taking effect for grants awarded from 2021, requires that publications from funded research appear in a fully open-access journal, on a compliant platform, or via a transformative arrangement recognised by the funder. Publisher journal finders match content to scope; they do not check a specific funder’s mandate against a specific journal’s business model.

    Wiley is the partial exception: alongside its Journal Finder, it offers a separate Author Compliance Tool that checks whether a given Wiley journal’s policies align with a named funder’s requirements. For every other publisher listed above, compliance checking sits outside the finder entirely.

    The authoritative cross-publisher tool is cOAlition S’s Journal Checker Tool (JCT). It requires three inputs — the author’s cOAlition S funder, their institution, and the intended journal — and returns whether that journal offers a Plan S-compliant route: full open access, a transformative agreement, or a self-archiving right that satisfies the funder’s policy. Authors should treat this as a mandatory second step after shortlisting journals with a publisher finder, never as an optional extra.

    Self-archiving (green open-access) rights specifically were historically checked via Sherpa/RoMEO. That lookup function has since migrated into Jisc’s Open Policy Finder, which now performs the same self-archiving and copyright policy search that Sherpa/RoMEO ran for over two decades, and remains a useful companion to the JCT when a transformative agreement is not available. Research administration teams tracking institutional compliance across multiple funders often run the JCT and Open Policy Finder together as a two-step check before an author submits.

    Common Questions From Plan S Authors

    Is Wiley JournalFinder free to use?

    Yes. Wiley’s Journal Finder is a free public tool at wiley.com that lets authors search or filter across 1,800+ Wiley journals by keyword, subject area or manuscript abstract. No login or subscription is required to generate a shortlist, though saving results and using the separate Author Compliance Tool may require a free Wiley account.

    What are the alternatives to Wiley Journal Finder?

    Authors publishing outside Wiley can use Elsevier’s Journal Finder, the Springer Nature Journal Suggester, or the Taylor & Francis Journal Suggester, each matching a manuscript to that publisher’s own portfolio. Cross-publisher alternatives include JournalGuide and Scopus Source Search, though neither replaces a funder-specific Plan S compliance check.

    What is Sherpa Romeo mainly used for?

    Sherpa/RoMEO was historically used to check a journal’s self-archiving policy — whether authors could deposit a preprint, accepted manuscript or published version in a repository. Its self-archiving data has since migrated into Jisc’s Open Policy Finder, which now performs the same green open-access policy lookup for Plan S authors.

    Is Wiley better than Elsevier?

    Neither is objectively “better” — each journal finder only searches that publisher’s own portfolio. Wiley’s tool covers 1,800+ titles with subject filters, while Elsevier’s adds an abstract-matching search across its list. The right choice depends on which publisher’s journals suit the manuscript’s discipline and the author’s funder requirements, not on the tool itself.

    For research administrators and institutional open-access teams, the practical takeaway is procedural rather than technical: publisher journal finders solve the discovery problem, but only a funder-aware checker like the JCT solves the compliance problem, and treating the two as interchangeable is the most common cause of post-acceptance compliance disputes. As more funders align with cOAlition S principles, expect publisher finders to integrate compliance flags directly — Wiley’s Author Compliance Tool is an early sign of that direction — but until that integration is universal, running a publisher finder followed by the Journal Checker Tool remains the safest two-step workflow for Plan S authors.

  • Predatory Journal Checker vs Plan S Compliance

    A Plan S compliant journal is not automatically screened by a predatory journal checker: Plan S tests open-access licensing and Directory of Open Access Journals (DOAJ) registration, not editorial integrity or peer-review conduct. The two checks answer different questions, and treating DOAJ/Plan S clearance as proof a journal is legitimate leaves a real compliance gap that research administrators need to close separately.

    A predatory journal checker is a tool, checklist, or reference list — such as the Think. Check. Submit. checklist, Cabells’ Predatory Reports, or the archived Beall’s List — used to test whether a journal’s peer review, editorial board, and fee practices are genuine rather than a vehicle for harvesting article-processing charges.

    What is a predatory journal checker?

    A predatory journal checker evaluates the operational and editorial conduct of a journal rather than its licensing terms. It looks at whether peer review actually happens, whether the editorial board is real and contactable, whether article-processing charges are disclosed upfront, and whether the publisher’s indexing claims can be verified.

    Common red flags that these tools and checklists are built to catch include:

    • Unsolicited, aggressive email invitations promising rapid publication
    • No transparent article-processing-charge (APC) schedule until after acceptance
    • An editorial board listing academics without their knowledge or consent
    • A journal scope so broad it covers unrelated disciplines
    • Fabricated or unverifiable impact-factor and indexing claims

    These are the criteria a checker tests. None of them is what Plan S compliance actually checks — which is the source of the confusion this article addresses.

    Does Plan S compliance screen for predatory journals?

    Not directly. Plan S is a funder mandate — led by cOAlition S — requiring that publicly funded research be published open access under specific licensing terms. Its technical requirements state that a fully open-access journal must be listed in the Directory of Open Access Journals (DOAJ), or apply for DOAJ listing within one year of publishing its first article, to count as a compliant venue.

    DOAJ listing is a proxy signal, not a predatory-publishing audit. cOAlition S guidance separately points authors toward the Think. Check. Submit. checklist for journal-selection due diligence — a clear indication that cOAlition S itself does not treat DOAJ/Plan S clearance as a substitute for a dedicated predatory check. Responsibility for the final journal-selection decision sits with the researcher and their institution, not with the funder’s compliance rule.

    DOAJ listing vs a dedicated predatory journal checker

    DOAJ vetting and a predatory journal checker overlap in intent — both aim to exclude disreputable venues — but they differ in scope, update frequency, and what they miss. DOAJ’s 2014–2016 re-application process is a useful illustration: it removed roughly 3,300 previously listed journals that failed revised inclusion criteria, which shows DOAJ listing is a snapshot assessment, not a continuously monitored guarantee.

    Mechanism What it verifies What it misses Best used for
    Plan S / DOAJ listing Open-access licence terms; baseline transparency criteria at time of listing Ongoing editorial conduct; peer-review quality after listing Confirming funder-mandate eligibility
    Predatory journal checker (Think. Check. Submit., Cabells, Beall’s archive) Editorial board authenticity, peer-review conduct, fee transparency Funder licensing compliance Author-level due diligence before submission
    Scopus / Web of Science journal check Active indexing status, citation metrics, discontinued-title flags Newer or non-English-language legitimate journals not yet indexed Cross-checking indexing claims a journal makes about itself
    Publisher/journal finder tools Journal-manuscript fit by scope and audience Legitimacy screening entirely — these tools assume the candidate pool is already vetted Narrowing a shortlist of already-verified journals

    What does a Scopus journal check add?

    A Scopus journal check confirms whether a title is actively indexed, flags titles that have been discontinued from Scopus for quality reasons, and surfaces citation-based metrics. This is a useful cross-check against a journal’s own indexing claims — predatory titles frequently claim indexing status they do not have — but Scopus coverage is not designed as a predatory-publishing screen and does not evaluate peer-review conduct directly.

    It is also asymmetric: a legitimate new journal may not yet be Scopus-indexed, so absence from Scopus is not itself proof of a predatory operation. Administrators should treat a Scopus check as one data point in a layered process, not a standalone verdict.

    How should administrators layer both checks?

    Institutions handling funder-mandate compliance and research-integrity screening as two separate workstreams should merge them into one journal-selection workflow. A practical sequence:

    1. Confirm funder eligibility first. Check DOAJ listing (or ROAD registration) to establish Plan S / open-access mandate compliance.
    2. Run a dedicated predatory check second. Apply the Think. Check. Submit. checklist, or consult Cabells’ Predatory Reports where the institution has a subscription, against the same candidate journal.
    3. Cross-check indexing claims. Verify any Scopus, Web of Science, or PubMed indexing the journal advertises against the indexing service’s own database.
    4. Escalate ambiguous cases to the institution’s research-integrity office or library scholarly-communications team rather than relying on a single automated pass/fail signal.
    5. Record the outcome in the researcher’s submission file, since funders and REF-style assessment exercises increasingly expect an audit trail of due-diligence steps, not just a final compliance flag.

    This sequencing matters because each mechanism fails differently: DOAJ/Plan S can clear a journal on licensing grounds while missing recent editorial decline; a predatory checker can flag conduct issues DOAJ has not yet caught up with; Scopus can catch a false indexing claim that neither of the other two checks is built to test.

    Common questions about predatory journal screening

    How do you check if a journal is predatory?

    Run the Think. Check. Submit. checklist against the journal, verify the editorial board members individually, confirm the article-processing charge is disclosed before submission, and cross-check any indexing claims (Scopus, DOAJ) directly against the indexing service rather than trusting the journal’s own website.

    What is a red flag for a predatory journal?

    Aggressive, unsolicited invitation emails promising unusually fast peer review are the most cited red flag. Other consistent signals include an editorial board that cannot be independently verified, a scope spanning unrelated disciplines, and article-processing fees disclosed only after acceptance.

    How do you check if a journal is reputable?

    Confirm active listing in DOAJ or an equivalent recognised index, verify the publisher belongs to COPE or a comparable ethics body, check that peer-review policy is published and specific, and confirm the editorial board’s affiliations independently rather than trusting journal-supplied contact details.

    What is considered a predatory journal?

    A predatory journal is one that charges publication fees while failing to provide the genuine editorial and peer-review services legitimate scholarly journals promise, prioritising revenue from article-processing charges over publication quality and research integrity, per definitions developed by COPE and reflected in Frandsen et al.’s peer-reviewed literature review.

    Implications and the path forward

    For institutional research offices, the practical implication is procedural: a single “is this journal Plan S compliant?” check cannot double as a research-integrity sign-off, and treating it that way creates audit risk when a funder or REF-style exercise later asks how a submission venue was verified. Layering a DOAJ/Plan S check with a dedicated predatory journal checker and an indexing cross-check is not duplicative effort — each step tests a distinct failure mode that the others do not cover.

    As open-access mandates expand and predatory operations grow more sophisticated at mimicking legitimate indexing and DOAJ-style transparency signals, the gap between funder-mandate compliance and editorial-integrity verification is likely to widen rather than close. Institutions that formalise the layered workflow now — rather than relying on DOAJ/Plan S status as an implicit predatory-publishing seal of approval — will be better positioned as funders tighten reporting expectations around journal-selection due diligence.

  • AI Act Regulation: Penalties for Research Bodies

    AI Act regulation penalises non-compliance on a three-tier scale: up to €35 million or 7% of global annual turnover for prohibited AI practices, up to €15 million or 3% for high-risk and general-purpose AI failures, and up to €7.5 million or 1% for supplying false information to regulators — whichever figure is higher in each case. For a university, spinout, or research consortium, the exposure is rarely the maximum headline number; it is the cost of misclassifying an admissions algorithm, an exam-proctoring tool, or a recruitment screen as “low risk” when the law says otherwise.

    The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) is the harmonised EU law setting risk-based obligations and penalties for AI systems, and it applies to research institutions as deployers whenever an AI system’s output affects people in the EU.

    What actually counts as an AI Act violation for a research institution?

    Universities and consortia rarely build the AI systems they use — they deploy them. Under the Act, a deployer is any organisation using an AI system in a professional capacity, and deployers carry real obligations even when a vendor built the underlying model. A learning-management platform that scores exam integrity, an HR tool that ranks job applicants, or an admissions filter all fall within scope if they touch people inside the EU, regardless of where the institution is based.

    Non-compliance is not a single offence. It spans failing to conduct a fundamental rights impact assessment, deploying an unregistered high-risk system, ignoring human-oversight requirements, or running a system the Act classifies as prohibited. Each failure mode sits on a different penalty tier.

    How much can an AI Act fine cost, tier by tier?

    Article 99 of Regulation (EU) 2024/1689 sets three fine bands. The final figure is whichever is higher — the flat euro cap or the percentage of worldwide annual turnover — which matters enormously for a university with a large total budget but a tiny AI-specific footprint.

    Violation type Maximum fine Turnover percentage Typical trigger for a research institution
    Prohibited AI practices (Art. 5) €35,000,000 7% Emotion-recognition in exams; covert biometric categorisation of students or staff
    High-risk system / GPAI obligation breaches €15,000,000 3% Recruitment or admissions AI deployed without a rights impact assessment
    Supplying incorrect, incomplete or misleading information €7,500,000 1% Inaccurate disclosures to a market surveillance authority or notified body

    Regulators must apply fines proportionately, weighing the nature, gravity and duration of the breach against the size of the organisation. Article 99(6) directs authorities to consider the interests of small and medium-sized enterprises and start-ups — relevant for university spinouts on constrained budgets — but this softens the number, not the underlying obligation.

    • Fines apply per infringement, so a consortium running several non-compliant systems faces cumulative, not capped, exposure.
    • Turnover is calculated on the whole legal entity’s global turnover, not just the department’s AI-related revenue or grant income.
    • National market surveillance authorities, not the EU AI Office, issue most fines against deployers; the AI Office focuses on general-purpose AI providers.

    Which of your institution’s AI systems could be “prohibited” outright?

    Article 5 bans a specific list of practices regardless of sector, and several map directly onto tools already used in higher education and research settings. A prohibited AI practice cannot be risk-managed into compliance — it must be withdrawn.

    The clearest overlaps for a research institution are:

    • Emotion recognition in educational institutions or workplaces, except for narrow medical or safety purposes — implicating some exam-proctoring and staff-monitoring software.
    • Biometric categorisation systems inferring race, political opinion, trade union membership, religion, or sexual orientation from biometric data.
    • Untargeted scraping of facial images from the internet or CCTV to build a recognition database — relevant to campus security systems built on scraped datasets.
    • Social-scoring-style evaluation of individuals by behaviour or personal traits leading to detrimental treatment unrelated to the original context.

    From 2 December 2026, two further prohibited categories take effect under the Digital Omnibus agreement: AI systems that generate or manipulate non-consensual intimate imagery (“nudifier” applications) and systems used to produce child sexual abuse material. Institutions running student-safeguarding or content-moderation tooling should confirm vendor compliance well ahead of that date.

    Has the Digital Omnibus changed the deadlines that matter?

    Yes, but selectively. The Act’s obligations phase in from its 1 August 2024 entry into force: prohibited practices became enforceable on 2 February 2025 (six months later), and general-purpose AI model obligations followed on 2 August 2025 (twelve months later). Both dates already passed and remain in force.

    In November 2025, the Council and Parliament agreed a “Digital Omnibus” simplification package — analysed by law firms including DLA Piper, Gibson Dunn and White & Case — pushing back the two remaining high-risk deadlines. Stand-alone high-risk systems under Annex III (covering most education, employment and public-service AI) now face obligations from 2 December 2027 rather than August 2026, a sixteen-month reprieve. High-risk AI embedded in regulated products under Annex I moves to 2 August 2028.

    Two dates were not delayed: Article 50 transparency obligations — labelling AI-generated content and disclosing chatbot interactions — still apply from 2 August 2026, the same date the Commission gains full penalty-enforcement powers over general-purpose AI providers. Institutions assuming the whole Act slipped to 2027 risk missing this transparency deadline.

    What should a research institution do now?

    The Digital Omnibus buys time on high-risk classification work, not on everything. A defensible position by August 2026 requires:

    • Inventory every AI system touching students, staff, applicants, or research subjects, tagged against the Article 5 prohibited list and Annex III high-risk categories.
    • Confirm any generative AI or chatbot-facing tool meets the Article 50 transparency requirement before 2 August 2026, independent of the high-risk delay.
    • Assign a named owner — typically in research administration or data governance — to track phased deadlines rather than treat the Act as one compliance date.
    • Apply vendor due diligence to procured AI tools, since deployer obligations do not disappear because a third party built the system.

    Answer-first: common questions on AI Act penalties

    Is the AI Act a regulation?

    Yes. The Artificial Intelligence Act is Regulation (EU) 2024/1689, meaning it applies directly and uniformly across all EU member states without needing national transposing legislation. It entered into force on 1 August 2024, and its obligations phase in over a multi-year timeline extending to 2028.

    What is the EU AI Act in 2026?

    By mid-2026, the prohibited-practice and general-purpose AI rules are already fully enforceable, while most high-risk system obligations have been pushed to December 2027 and August 2028 under the November 2025 Digital Omnibus agreement. Article 50 transparency duties and full GPAI enforcement powers still take effect on 2 August 2026 as originally scheduled.

    Does the UK have to comply with the EU AI Act?

    The UK has no domestic equivalent to the AI Act, but the regulation’s extraterritorial scope reaches UK institutions whenever their AI system’s output is used by, or affects, people in the EU. A UK university running an EU-facing admissions or research-collaboration platform can fall within scope despite being outside the bloc.

    Does the UK have any AI regulation of its own?

    Not a single statute. The UK relies on a sector-by-sector, principles-based approach enforced by existing regulators (ICO, EHRC, Ofcom) rather than one AI Act. This is why UK institutions with EU-facing systems must track both the domestic guidance and the EU regulation’s extraterritorial reach separately.

    What this means for institutional risk management

    The headline €35 million figure will rarely apply to a university outright, but the reputational cost of a prohibited-practice finding is not confined to the fine itself. A finding against emotion-recognition exam software invites scrutiny of every other AI-enabled assessment tool on campus, and funders increasingly expect institutions to demonstrate AI governance maturity, mirroring assurance expectations already familiar from research administration compliance frameworks.

    Treating AI Act regulation as a procurement and governance discipline — inventory, classification, named ownership, phased deadline tracking — converts an open-ended legal risk into a manageable operational programme.

    Where this is heading

    The Digital Omnibus shows the EU will adjust timelines under pressure, but it has not softened the penalty structure, and it has added prohibited categories rather than removed any. Research institutions should expect further phased deadlines and continued extraterritorial reach, and should treat every delay as a planning window, not a reason to deprioritise compliance work.

  • cOAlition S Executive Steering Group Explained

    The cOAlition S Executive Steering Group (ESG) is the body that develops and implements Plan S strategy day to day, taking majority-vote decisions and reporting upward to the funder-led Leaders’ Group. It is chaired by Lidia Borrell-Damián, Secretary General of Science Europe, and is now working alongside Curt Rice, appointed Director in May 2026 after Johan Rooryck’s departure in July 2025.

    The coalition s executive steering group is the operational engine most people mean when they ask “who actually runs Plan S” — as distinct from the Leaders’ Group, which sets overall direction but meets far less frequently. For research administrators trying to work out who to brief, lobby, or route a query to, the distinction matters.

    cOAlition S is an informal alliance of research funders and performers that have publicly committed to implementing the open-access principles of Plan S; it holds no independent legal capacity of its own, according to its published Terms of Reference.

    What is the Executive Steering Group and who sits on it?

    The Executive Steering Group is the standing body responsible for developing cOAlition S’s strategy and overseeing its implementation across member organisations. It sits below the Leaders’ Group in the governance hierarchy but is where most of the substantive, ongoing work happens.

    According to cOAlition S’s published governance roster, current and recent ESG representatives include:

    • Lidia Borrell-Damián (Chair) — Secretary General, Science Europe
    • Zoé Ancion — ANR, French National Research Agency
    • Michael Arentoft — European Commission
    • Rachel Bruce — UKRI, UK Research and Innovation
    • Ian Coltart — World Health Organisation
    • Ashley Farley — Gates Foundation
    • Mongezi Mdhluli — South African Medical Research Council
    • Bodo Stern — HHMI, Howard Hughes Medical Institute

    Two cOAlition S Office roles — a Programme Manager and a Communications Manager — sit in the ESG in a Secretariat capacity, and Marc Schiltz, an architect of the original Plan S principles, continues as a non-voting adviser. Because national funder representatives rotate, administrators should treat any published roster as a snapshot rather than a permanent list.

    How does the ESG fit into cOAlition S’s wider governance?

    cOAlition S runs a four-layer structure: the Leaders’ Group (heads of funding and performing organisations, who approve overall strategy and budget), the Executive Steering Group (which develops and executes that strategy), an Experts Group (technical working groups), and the cOAlition S Secretariat, which is hosted by OPERAS AISBL in Brussels and provides day-to-day administrative and communications support.

    Body Role Chair / lead Decision power
    Leaders’ Group Sets overall Plan S strategy and budget Prof. Mari Sundli Tveit, Chief Executive, Research Council of Norway Highest authority; approves ESG proposals
    Executive Steering Group Develops and implements strategy; runs operations Lidia Borrell-Damián, Science Europe Majority vote; reports to Leaders’ Group
    Secretariat / Office Administrative, financial and communications support Curt Rice, Director Executes decisions; no independent vote
    Experts Group Technical and policy working groups Rotating co-chairs Advisory input to ESG

    The Secretariat’s own budget illustrates how much the operation has contracted: cOAlition S’s published accounts show total Office spending of €545,167 in 2025, down from €1,108,186 in 2024, with staffing falling from roughly 3 full-time-equivalent posts in 2024 to 2 in 2025 — consistent with reporting that cOAlition S is scaling back its ambitions and shifting focus toward funding sustainability.

    Who leads the ESG now Curt Rice has replaced Johan Rooryck?

    Prof. Johan Rooryck, cOAlition S’s Executive Director since 2019, left the role on 3 July 2025 after overseeing the coalition’s expansion to 28 research funding and performing organisations. Following his departure, the Executive Steering Group itself took on interim oversight of operations while cOAlition S undertook a strategic review.

    cOAlition S announced on 13 May 2026 that Curt Rice, former rector of Oslo Metropolitan University with more than three decades in Norwegian and international higher education, would become its new Director. Notably, the title changed from “Executive Director” to “Director” — a small but real signal of a leaner operating model. The appointment coincided with the adoption of cOAlition S’s 2026–2030 Strategy, which administrators should read as the current reference document for near-term priorities.

    Under the Terms of Reference, the Director leads the Secretariat team, reports to the Chair of the Executive Steering Group, and acts as cOAlition S’s main spokesperson — meaning press and policy enquiries typically route through this office rather than directly to individual ESG members.

    What does the ESG actually decide, and how?

    The Executive Steering Group takes decisions by majority vote among its members and reports those decisions to the Leaders’ Group. Its remit covers developing strategic input, overseeing joint programmes and funding streams, and directing cOAlition S’s public communications through the Office.

    In practice this means the ESG — not the Leaders’ Group — is where day-to-day questions about Plan S implementation, transformative journal assessments, and funder-alignment issues get resolved before reaching the funders’ principals. The Leaders’ Group retains final sign-off on strategy and budget, but rarely intervenes at the operational level.

    Answer-first Q&A

    Who are the members of the cOAlition S Executive Steering Group?

    The ESG is chaired by Lidia Borrell-Damián of Science Europe and includes representatives from funders such as UKRI, the European Commission, ANR, WHO, the Gates Foundation, HHMI, and the South African Medical Research Council, plus non-voting cOAlition S Office staff. Membership rotates as national funders change their delegates.

    What is the difference between the Leaders’ Group and the Executive Steering Group?

    The Leaders’ Group comprises heads of member organisations and approves overall Plan S strategy and budget. The Executive Steering Group develops that strategy in detail, runs day-to-day implementation, and takes majority-vote decisions, reporting upward to the Leaders’ Group rather than acting independently.

    Who is the current Director of cOAlition S?

    Curt Rice became Director on 13 May 2026, succeeding Johan Rooryck, who departed on 3 July 2025. Rice previously served as rector of Oslo Metropolitan University and leads the Secretariat team, reporting to the Chair of the Executive Steering Group.

    Does the Executive Steering Group have the final say on Plan S policy?

    No. The ESG develops strategy and takes operational decisions by majority vote, but the Leaders’ Group holds final authority over overall strategy and budget. The ESG’s role is to execute and report, not to set Plan S’s ultimate direction unilaterally.

    What this means for research administrators

    For an institution needing to raise a Plan S implementation question, engagement route should generally go through the Secretariat/Office first — now under Curt Rice’s Directorship — rather than direct outreach to individual Executive Steering Group members, who serve in a part-time, delegated capacity alongside their home-organisation roles.

    Administrators tracking funder-mandate compliance for research administration purposes should also note the contraction in cOAlition S’s own resourcing: a shrinking Secretariat budget and headcount suggests slower turnaround on ad hoc queries and a narrower work programme under the 2026–2030 Strategy than in the coalition’s 2019–2023 growth phase.

    • Route policy and compliance queries to the Secretariat/Office, not individual ESG delegates.
    • Cite the Terms of Reference and governance page directly when briefing institutional leadership — both are publicly hosted by cOAlition S.
    • Expect the 2026–2030 Strategy, not the original 2018 Plan S text, to be the live reference point for near-term commitments.

    cOAlition S’s governance has moved from a growth-and-advocacy phase under Rooryck to a leaner, sustainability-focused phase under Rice and Borrell-Damián’s ESG chairmanship. Administrators who track this shift — rather than relying on the original 2018 Plan S announcement — will have a more accurate picture of who holds real influence over open-access mandates in 2026 and beyond.

  • AI Governance UK: What Universities Hire For

    AI governance UK hiring is real but narrow: employers are advertising standalone “AI governance” titles mainly in consultancies and tech firms, while UK universities and research funders are folding AI oversight into existing research-governance, integrity and data-protection roles rather than minting a new job category. Certifications such as IAPP’s AIGP and the ISO/IEC 42001 Lead Auditor credential map to genuinely different parts of that work — one to policy and compliance, the other to formal audit.

    AI governance is the set of policies, controls and accountability structures an organisation uses to ensure AI systems are developed, procured and used safely, lawfully and transparently across their lifecycle.

    What is driving the AI governance UK hiring wave?

    Search interest in AI governance credentials has accelerated sharply. Keyword-demand data tracked into June 2026 shows “ai governance certification” search volume in the UK up 129% year-on-year, and “ai governance job” postings now surface daily on LinkedIn, Indeed and Totaljobs for roles spanning “Director AI Governance” to “Responsible AI Specialist.”

    The trigger is regulatory, not academic. Under the government’s 2023 White Paper AI regulation: a pro-innovation approach, the UK deliberately chose not to pass a single AI statute. Instead, existing regulators — the Information Commissioner’s Office, Ofcom and the Competition and Markets Authority — enforce five cross-sector principles: safety, security and robustness; appropriate transparency and explainability; fairness; accountability and governance; and contestability and redress.

    That distributed model pushes the compliance burden into individual organisations, which is exactly the vacuum “AI governance” job titles are appearing to fill. Employers with EU exposure are also hiring against the EU AI Act, whose obligations extend to UK organisations that deploy AI systems into EU markets.

    What are UK research institutions actually hiring for?

    A survey of current academic job boards shows standalone “AI Governance Officer” titles remain rare in UK higher education. What is expanding instead is AI content grafted onto established research-governance and research-integrity posts.

    • The University of Oxford has run a postdoctoral researcher post inside its Oxford AI Governance Initiative, focused on AI and risk research rather than institutional compliance.
    • The University of Bristol advertises a Head of Research Governance role — the University’s lead officer for research regulation, ethics and integrity across human participants, tissue and data, a remit now stretching to cover AI-enabled research methods.
    • The Alan Turing Institute’s AI Ethics and Governance in Practice programme, an eight-workbook resource for project teams, functions as the de facto training reference most UK research-intensive institutions point staff toward, in place of a dedicated internal certification.
    • The Russell Group published sector-wide principles on generative AI in education in January 2025, giving member universities a shared policy baseline rather than each hiring separate AI governance specialists.

    The pattern is consistent: research institutions are governing AI through their existing research-integrity, ethics-committee and data-governance infrastructure, supplemented by sector guidance from the Turing Institute and Russell Group, rather than building a parallel AI governance function from scratch.

    Which certifications map to the job?

    Two credentials dominate current job advertisements, and they are not interchangeable. IAPP’s AIGP is a policy and compliance credential; ISO/IEC 42001 Lead Auditor is a formal management-systems audit qualification built on the international AI management system standard published in 2023.

    Certification Body Format Best fit
    IAPP AIGP International Association of Privacy Professionals 100 multiple-choice questions, 180 minutes Privacy, legal and policy staff who need to interpret AI law and risk, not audit systems
    ISO/IEC 42001 Lead Auditor Accredited training bodies (e.g. PECB, BSI) against the ISO/IEC 42001:2023 standard Multi-day course plus exam Auditors and compliance managers validating a formal AI management system (AIMS)
    Vendor foundational courses (e.g. Securiti) Commercial vendors Short on-demand modules, 2–3 hours Awareness-level onboarding, not a substitute for either credential above

    Neither certification is a licence to practise. Both function as evidence that a candidate has studied a defined body of knowledge — AIGP for law and policy, ISO/IEC 42001 Lead Auditor for management-system audit method — which is why job advertisements almost always list them as “desirable,” not mandatory.

    Genuine career pathway or rebadged compliance role?

    The honest answer is both, depending on sector. In consultancies and large tech employers, “AI governance” is emerging as a distinct, senior, well-paid track — UK job boards currently list Director-level AI governance roles paying well above general compliance-officer rates. In research institutions, it is largely a rebadged extension of research integrity, data protection and ethics-committee work that already existed.

    That does not make it hollow. It means the credential value differs by employer type: a corporate AI governance hire benefits most from IAPP’s AIGP or an ISO/IEC 42001 audit qualification, while a university research-governance officer gains more from Turing Institute and Russell Group sector guidance, since their day job already sits inside an ethics and integrity framework those resources were built for.

    Which is the best AI governance certification?

    There is no single “best” credential; fit depends on function. IAPP’s AIGP suits policy, legal and privacy specialists working across jurisdictions and the EU AI Act. ISO/IEC 42001 Lead Auditor suits professionals who must formally audit an organisation’s AI management system rather than advise on policy.

    Is AI governance certification worth it?

    It is worth it for candidates whose work already touches AI policy, compliance, risk management or privacy, where it demonstrates structured knowledge to employers. It adds little on its own without underlying domain experience, since UK job advertisements consistently list these credentials as desirable evidence rather than a mandatory gate.

    How to become an AI governance professional?

    Most current UK postholders arrive via data protection, legal, risk or research-integrity backgrounds, then add AI-specific knowledge through a credential such as AIGP or ISO/IEC 42001. Direct entry-level “AI governance” hiring remains limited; experience in an adjacent regulated function is the more common route in.

    What skills are needed for AI governance?

    Core skills include risk assessment, regulatory interpretation, bias and fairness evaluation, and stakeholder communication across legal, technical and leadership teams. Employers also expect familiarity with the AI lifecycle and enough technical literacy to question a model’s design without needing to build one.

    What this means for research institutions

    For UK research administrators and institutional leaders, the near-term implication is not to create a new “AI Governance Officer” post by default. It is to audit whether existing research-integrity, data-governance and ethics-committee functions already cover AI risk, and where they do not, to close the gap with targeted training — Turing Institute workbooks or an IAPP AIGP course — rather than an immediate new hire.

    Over the next 12–24 months, expect the corporate and research-sector paths to converge somewhat as funders begin asking institutions to document AI oversight within grant compliance and wider research administration processes. Institutions that get ahead of that by mapping certifications to real duties now, rather than hiring a title, will be better placed when funders start asking for evidence.

  • AI Legislation Tracker: Free Tools Compared for Research Offices

    An AI legislation tracker is a curated, continuously updated resource that monitors the progress of artificial intelligence bills, statutes and regulations across jurisdictions. For research offices, three free options cover the ground a paid GRC subscription would otherwise charge for: the IAPP’s US State AI Governance Legislation Tracker for state-level bills, White & Case’s AI Watch for a global regulatory sweep, and the AI Act Explorer for line-by-line navigation of the EU AI Act. Used together, they give research administrators enough coverage to flag compliance and procurement risk without a dedicated legal-intelligence budget.

    An AI legislation tracker is a legal-intelligence tool — usually maintained by a law firm, professional association, or legislature — that indexes AI-related bills and regulations by jurisdiction, status and topic so non-specialists can monitor change without reading primary legislative text. For a research office, that means catching a new state disclosure requirement or an EU AI Act compliance deadline before it lands in an audit finding.

    Table of contents

    What is an AI legislation tracker, and why does a research office need one?

    Research offices sit at the intersection of three regulatory pressures: institutional AI-use policy, funder terms and conditions, and the AI laws of every jurisdiction in which their institution operates, procures software or receives funding. No single regulator publishes a consolidated feed of all three, which is why legal-intelligence trackers — built by law firms and associations to serve their own clients — have become the de facto public monitoring layer for everyone else.

    Three gaps make this monitoring hard for a research office specifically. First, state-level fragmentation in the US: MultiState.ai reported tracking 1,561 AI-related bills across 45 states in early 2026, and a bill’s status can change between a legislative session’s opening and a grant’s renewal date. Second, phased EU obligations: the AI Act (Regulation (EU) 2024/1689) entered into force on 1 August 2024 but applies in stages — prohibited-practice provisions since 2 February 2025, general-purpose AI model obligations since 2 August 2025, and the bulk of high-risk system obligations from 2 August 2026. Third, procurement-clause drift: institutional purchasing teams increasingly need to know whether a vendor’s AI tool falls under a “high-risk” classification before a contract is signed, not after.

    Comparing the free trackers: IAPP, White & Case AI Watch and the AI Act Explorer

    Each of the three core tools covers a different layer of the regulatory stack. None requires a paid subscription for the baseline tracker view, though firms use them as client-development tools, so update cadence and depth of legal commentary vary.

    Tool Publisher Geographic scope Best use for a research office Cost
    US State AI Governance Legislation Tracker IAPP US state legislatures Flagging new state disclosure/consumer-protection bills affecting AI-assisted research tools Free
    AI Watch: Global Regulatory Tracker White & Case US, EU, UK, China and other core markets Cross-jurisdiction horizon-scanning for institutions with international partners Free
    AI Act Explorer Future of Life Institute (artificialintelligenceact.eu) European Union Locating the exact article/annex governing a specific AI use case before procurement sign-off Free
    Artificial Intelligence Legislation Database National Conference of State Legislatures (NCSL) US state legislatures Official-source cross-check against law-firm trackers, filterable by policy topic Free
    OECD.AI Policy Navigator OECD 80+ countries and international bodies Global baseline for institutions with funders or partners outside the US/EU Free

    Two law-firm trackers rarely agree exactly on bill status, since each applies its own inclusion criteria — the IAPP chart, for example, deliberately excludes government-only AI bills to focus on rules affecting private-sector organisations. A research office should treat the NCSL database as the authoritative cross-check whenever a law-firm tracker and an internal compliance log disagree, since NCSL draws directly from legislative records rather than curated commentary.

    How to monitor AI law without a paid GRC subscription

    A practical monitoring routine needs three components: a jurisdiction list, a check cadence, and an escalation trigger. Map the institution’s actual footprint — states where staff or partner sites are located, countries with active funder relationships, and any EU-based collaborators — against the five tools above, rather than trying to watch all 45+ US states with active bills at once.

    • Set a monthly review of the IAPP tracker and NCSL database for the institution’s home state plus any state with a satellite campus or major subcontractor.
    • Set a quarterly review of White & Case AI Watch for jurisdictions tied to international grant or publishing partners.
    • Check the AI Act Explorer whenever procuring or renewing an AI-enabled research tool from an EU-based or EU-selling vendor, since Article 53 transparency obligations for general-purpose AI providers already apply.
    • Escalate to institutional counsel the moment a tracked bill moves from “introduced” to “enacted” in a jurisdiction on the footprint list — status changes, not initial filings, are the actionable signal.

    This cadence substitutes staff time for the subscription cost of a commercial GRC platform. It will not catch everything a paid legal-intelligence service would, but it closes the gap between “no monitoring” and “monitoring proportionate to institutional risk,” which is the realistic target for most research offices.

    Which AI rules actually affect grant compliance and procurement

    Not every tracked bill is relevant to a research office. The ones that matter cluster into two categories: funder-facing disclosure requirements and vendor/procurement obligations. On the funder side, publishers already require disclosure of generative-AI use in manuscript preparation under guidance from bodies such as ICMJE and COPE — a policy layer that sits alongside, not inside, the legislative trackers above, and one research offices should monitor through authorship policy channels rather than a legislation tracker.

    On the procurement side, the EU AI Act’s general-purpose AI model obligations — applicable since 2 August 2025 — require providers to maintain technical documentation and, for systemic-risk models, conduct model evaluations; institutions procuring AI research tools from in-scope vendors should expect updated contract terms reflecting this. Separately, under Article 57 of Regulation (EU) 2024/1689, each EU member state must establish at least one national AI regulatory sandbox operational by 2 August 2026 — a detail the AI Act Explorer surfaces clearly but general news coverage rarely mentions, and one that matters to institutions running EU-based pilot deployments of AI research tools.

    In the US, state consumer-protection style AI bills increasingly impose obligations on “deployers” as well as developers — meaning an institution using a third-party AI tool, not just the vendor that built it, can carry compliance obligations. This is the single most consequential fact a research office should extract from the state trackers: deployer obligations mean procurement due diligence, not just vendor selection, is now a compliance function.

    Common questions research administrators ask

    Are there any regulations on AI?

    Yes. There is no comprehensive federal AI statute in the United States, but individual US states have enacted targeted laws, the European Union’s AI Act (Regulation (EU) 2024/1689) is in force with phased obligations through 2027, and dozens of other jurisdictions maintain sector-specific or principles-based AI policy frameworks tracked by the OECD.

    Does Europe have AI regulations?

    Yes. The EU AI Act is the first comprehensive AI-specific legal framework, entering into force on 1 August 2024. Prohibited-practice rules applied from February 2025, general-purpose AI model obligations from August 2025, and most high-risk system requirements apply from August 2026 onward.

    Where are the AI regulations?

    AI rules are distributed across national statutes, EU regulation, and US state legislatures rather than one source — which is precisely why trackers such as IAPP’s state chart, White & Case’s AI Watch, and the AI Act Explorer exist: each consolidates one layer of a fragmented, multi-jurisdiction landscape into a single reference point.

    The regulatory landscape a research office must monitor will keep expanding rather than consolidating: more US states are expected to move bills from “introduced” to “enacted” through 2026 and 2027, and the EU AI Act’s remaining compliance deadlines run to August 2027. A footprint-mapped, tiered-cadence monitoring routine built on these five free trackers is a realistic, sustainable substitute for a paid GRC subscription — provided it is reviewed and re-scoped as the institution’s own AI use, partnerships and procurement expand.

  • Types of Material Transfer Agreement Guide: One-Way, Two-Way and Commercial-Use MTAs

    Types of material transfer agreement fall into two overlapping categories: direction of transfer (one-way versus two-way) and nature of the parties (academic/non-profit versus commercial-use). Choosing the wrong category adds weeks of unnecessary negotiation to a simple exchange, or leaves an institution exposed on intellectual property and liability in a complex one. A material transfer agreement (MTA) is a contract that governs the transfer of tangible research materials — cell lines, plasmids, reagents, antibodies, animal models, or genetic constructs — between a provider and a recipient, setting terms for permitted use, publication, and downstream rights.

    What is a material transfer agreement, and why does type matter?

    A material transfer agreement is the legal instrument accompanying the physical movement of a research material between organisations. It records who owns the original material, what the recipient may do with it, who owns derivatives the recipient creates, and what happens if the material leads to a publication or invention.

    Institutions do not use one template for every transfer. The right MTA type depends on two independent variables: direction of the exchange, and the nature of the parties — non-profit or commercial. Mismatching the template to either variable is a common cause of avoidable negotiation delay.

    One-way vs two-way MTAs: what is the difference?

    A one-way MTA covers a single direction of transfer: one provider sends material to one recipient, who accepts the provider’s terms. Most institutional MTAs are one-way and are further split into two sub-types depending on which side of the transaction the institution sits on.

    • Incoming MTA — the institution is the recipient. The priority is understanding and accepting the provider’s restrictions: permitted research use, any prohibition on commercial use, and publication or embargo terms.
    • Outgoing MTA — the institution is the provider. The priority shifts to protecting the institution’s own intellectual property, limiting liability for the material’s performance, and controlling further distribution by the recipient.

    A two-way (or reciprocal) MTA is used when both parties send materials to each other, typically in an active collaboration where each lab holds a resource the other needs. Rather than negotiate two separate one-way agreements, the parties combine both transfers into a single reciprocal agreement with symmetric obligations. This is administratively efficient but requires both sides to specify their respective materials and restrictions with equal precision — asymmetric two-way MTAs are a frequent source of later disputes over derivative rights.

    MTA type Direction Typical parties Primary administrative focus
    Incoming (one-way) Provider → institution Academic-to-academic or vendor-to-academic Compliance with provider’s use and publication restrictions
    Outgoing (one-way) Institution → external recipient Academic-to-academic or academic-to-industry IP retention, liability limitation, distribution control
    Two-way / reciprocal Bidirectional Collaborating academic labs Symmetric terms for both transferred materials
    Academic/non-profit Either direction Non-profit-to-non-profit Non-commercial research use only; minimal negotiation
    Commercial-use Either direction At least one for-profit party IP ownership, licensing options, publication delay, indemnification

    Academic/non-profit vs commercial-use MTAs: how do the terms differ?

    The second axis of classification is the nature of the parties, and it changes negotiation complexity more than direction does. A biological material transfer agreement between two non-profit universities is usually a light-touch document; the same transfer involving a commercial partner routinely takes months longer to close.

    Academic and non-profit MTAs exist to facilitate open scientific exchange. The US National Institutes of Health (NIH) has stated that unique research resources arising from NIH-funded work should be shared on terms no more restrictive than its own model agreements, because repeated case-by-case negotiation between non-profits delays the point at which a research tool reaches the laboratory bench. These agreements typically restrict use to non-commercial research, require no royalty, and rarely need individual negotiation once a standard template is adopted.

    Commercial-use MTAs — where a for-profit company is provider, recipient, or both — carry additional, negotiated terms that a non-profit template does not anticipate:

    • Intellectual property rights over inventions made using the transferred material, including whether the provider retains an option to license.
    • Publication rights, including any pre-publication review period the commercial party can invoke to protect confidential information.
    • Scope of permitted use, distinguishing internal research from development toward a commercial product, which may trigger the need for a separate licence agreement.
    • Indemnification and liability allocation, which non-profit-to-non-profit templates typically waive or cap at a nominal level.

    A material transfer agreement policy should specify, in advance, which of these terms are non-negotiable defaults and which require case-by-case legal review — allowing routine academic MTAs to clear in days, reserving negotiation capacity for the commercial-use cases where it is genuinely needed.

    Which standard templates exist, and how do you choose the right one?

    Four template families cover the great majority of transfers, and matching a transfer to the correct family is the fastest route to a signed agreement.

    • NIH Simple Letter Agreement (SLA), published in 1995 by the NIH, is a one-page model for low-risk, non-commercial transfers of routine research materials between non-profit institutions.
    • Uniform Biological Material Transfer Agreement (UBMTA), also introduced in 1995, is the master agreement signed once by an institution; individual transfers under it use a short “Implementing Letter” rather than a fresh negotiation, and it is the standard route for a two-way material transfer agreement between two UBMTA-signatory non-profits.
    • AUTM Model MTAs extend the UBMTA framework to materials — and non-US institutions — outside the UBMTA’s original definition of biological material; a 2011 AUTM member survey found low adoption of standard templates was itself a major cause of unnecessary delay, which the toolkit’s decision tree was built to address.
    • FAO Standard Material Transfer Agreement (SMTA), adopted by the Governing Body of the International Treaty on Plant Genetic Resources for Food and Agriculture in 2006, is a distinct global instrument governing plant genetic resources held in the Treaty’s Multilateral System, with mandatory benefit-sharing terms that have no equivalent in the UBMTA or NIH templates.

    To choose: start by identifying direction (one-way or two-way) and party type (non-profit or commercial). If both parties are non-profit and the material is biological, default to the UBMTA or SLA. If a commercial party is involved, route the transfer to legal review rather than a standard template. If the material is a plant genetic resource in the Multilateral System, the FAO SMTA applies regardless of the parties’ institutional type — a distinction general MTA guidance frequently omits.

    Frequently asked questions

    What is a material transfer agreement?

    A material transfer agreement is a contract governing the transfer of tangible research materials — such as cell lines, plasmids, reagents, or animal models — between a provider and a recipient. It sets terms for permitted use, ownership of derivatives, publication rights, and liability, and is negotiated before the physical material is shipped.

    What is the difference between an MTA and an NDA?

    An NDA (non-disclosure agreement) protects confidential information exchanged between parties, while an MTA governs the physical transfer and permitted use of a tangible material. The two are often signed together — an NDA may protect data about the material, while the MTA governs the material itself — but neither substitutes for the other.

    What is the standard material transfer agreement?

    The Standard Material Transfer Agreement (SMTA) is the FAO instrument used specifically for plant genetic resources held under the International Treaty on Plant Genetic Resources for Food and Agriculture’s Multilateral System. It is distinct from the UBMTA used for general biological materials and includes mandatory benefit-sharing obligations tied to the Treaty.

    Implications and outlook for research administrators

    Institutions that map every incoming request against this taxonomy before drafting — direction first, party type second, then template — process the majority of routine biological material transfer agreement requests without individual legal review. That triage is what standard templates like the UBMTA and NIH SLA were designed to enable, and what a written material transfer agreement policy should formalise as institutional default practice.

    The remaining minority — commercial-use transfers, cross-border plant genetic material, and asymmetric two-way exchanges — is where administrative time should concentrate, since these are the categories where the wrong template creates the greatest downstream IP and compliance risk. As collaborations increasingly span academic, industry, and international-treaty jurisdictions at once, classifying a transfer correctly at intake, rather than after a dispute arises, remains the most effective control a research administration office can apply.

  • Cost Sharing in Grants: Mandatory vs Voluntary

    Cost sharing on a grant is the portion of a project’s true cost that the sponsor does not pay, covered instead by the recipient institution, a third party, or in-kind contributions. It can be mandatory (a condition of the award, set out in the funding announcement) or voluntary (offered by the applicant and not required). A growing number of funders — most notably the US National Science Foundation — have moved away from requiring or even rewarding voluntary cost sharing, on the grounds that it disadvantages under-resourced institutions and adds compliance burden without improving research quality.

    What is cost sharing in a grant budget?

    Cost sharing (also called matching) is the share of a sponsored project’s total cost that is not reimbursed by the funding agency. It is contributed instead by the recipient institution, a subrecipient, or a third-party collaborator, either as cash or as an in-kind resource such as donated staff time, waived facilities-and-administration (F&A) costs, equipment, or space.

    Under the US federal Uniform Guidance (2 CFR Part 200, §200.306), cost sharing and matching are defined as the portion of project costs “not borne by the Federal Government.” Any contribution counted this way must be verifiable from the recipient’s own records, not double-counted against another federally funded project, and necessary and reasonable for the project. This is the baseline definition US sponsored programs offices apply when reviewing a proposal’s grant budget justification.

    Mandatory vs voluntary cost sharing: what’s the difference?

    The distinction between mandatory and voluntary cost sharing determines whether a commitment is legally enforceable. Mandatory cost sharing is imposed by the sponsor and stated explicitly in the funding opportunity; without it, the proposal is ineligible. Voluntary cost sharing is offered by the applicant even though the sponsor did not require it — and once quantified in a funded federal proposal, it becomes just as binding and auditable as a mandatory commitment.

    Type Who requires it Reporting obligation once awarded
    Mandatory cost sharing Sponsor, stated in the solicitation Documented, tracked and reported to the sponsor for the life of the award
    Voluntary committed cost sharing Applicant, quantified in the proposal budget or narrative Treated as binding and auditable once the award is made, on federal awards
    Voluntary uncommitted cost sharing Applicant, contributed after award but never quantified in the proposal Not tracked or reported to the sponsor

    The trap is the second row. A PI who writes “the PI will devote 20% effort at no cost to the sponsor” creates a quantified, reportable commitment — even though the sponsor never asked for one. This is why sponsored programs offices train investigators to use non-quantified language (“will provide expert consultation, as needed”) whenever cost sharing is not actually required.

    Why are funders moving away from mandatory cost sharing?

    The clearest example is the National Science Foundation. Following its own Cost Sharing Task Force review, NSF’s Proposal & Award Policies and Procedures Guide (PAPPG) states that cost sharing is not required except where a specific program solicitation invokes a statutory requirement, and that reviewers may not factor voluntary committed cost sharing into merit review. NSF’s rationale was that cost sharing had become a competitive filter favouring wealthier institutions rather than an indicator of project quality.

    Three arguments recur across funder policy statements and research-administration literature on this reform:

    • Equity between institutions. A fixed percentage match is far harder for a community college or small non-profit to absorb than for a well-endowed research university — skewing award patterns by wealth rather than merit.
    • Administrative burden. Cost sharing must be certified through effort reporting and reconciled at closeout; auditors treat under-delivered cost share as a disallowed cost, risking clawback.
    • Review integrity. A visible voluntary contribution can bias scoring toward applicants who over-promise resources they may struggle to deliver.

    Cost sharing has not disappeared. It remains common — and often mandatory — on infrastructure and construction grants, public-private partnership schemes, and Department of Justice (DOJ) Office of Justice Programs awards, where the required match varies by programme and is set out in each solicitation’s guide sheet.

    How do UK and EU funders structure cost sharing?

    US-centric discussions of cost sharing rarely mention that the UK and EU systems build an equivalent principle directly into their core funding formulas, rather than treating it as a discretionary add-on.

    UK Research and Innovation (UKRI) funds most Research Council grants at up to 80% of a project’s Full Economic Cost (fEC), calculated via the sector’s Transparent Approach to Costing (TRAC) methodology. The host university funds the remaining 20% itself — a structural, near-universal form of mandatory cost sharing built into the grant terms, not a clause institutions can negotiate away project by project.

    Under Horizon Europe, reimbursement rates differ by action type rather than a flat match: Research and Innovation Actions (RIA) are typically funded at 100% of eligible direct costs, while Innovation Actions (IA) are reimbursed at 100% for non-profit entities but only 70% for profit-making organisations — meaning commercial participants effectively cost-share 30% of their own costs as a condition of taking part.

    This is a genuinely different model from the US project-by-project mandatory/voluntary framework. A US-style “voluntary cost sharing is discouraged” mindset does not transfer cleanly to a UKRI fEC or Horizon Europe budget, where the shortfall is baked into the reimbursement rate itself, not offered or declined proposal by proposal.

    Common questions about cost sharing

    What is cost share on a grant?

    Cost share on a grant is the share of a sponsored project’s total cost that the funding agency does not pay, covered instead by the recipient institution, a subrecipient, or a third party. It can be cash (salary, direct funding) or in-kind (donated time, waived facilities-and-administration costs, equipment) and must be verifiable, allowable, and incurred within the project period.

    What are the three types of cost sharing?

    The three recognised categories are mandatory, voluntary committed, and voluntary uncommitted cost sharing. Mandatory is required by the sponsor as a condition of funding; voluntary committed is offered by the applicant and becomes binding once awarded; voluntary uncommitted is contributed after the award but never quantified in the proposal, so it carries no reporting obligation.

    What is a cost sharing requirement?

    A cost sharing requirement is a condition, stated explicitly in a funding announcement, that obliges applicants to contribute a defined percentage or dollar amount of project costs from non-sponsor sources. Requirements vary widely by programme — from a flat percentage match to a formula tied to Modified Total Direct Costs — and must be documented and reported to the sponsor if the proposal is funded.

    How does cost sharing work?

    Cost sharing works by allocating a defined portion of a project’s budget to the recipient rather than the sponsor, expressed either as a percentage of total cost or as a match ratio (for example, 1:1). Once quantified in a funded proposal’s grant budget justification, the commitment must be tracked through effort reporting or financial records and reconciled at the project’s grant closeout report.

    Implications for institutional budget commitments

    For sponsored programs offices, the decline of mandatory cost sharing at agencies like NSF does not reduce the compliance workload — it relocates it. Institutions must train investigators to recognise when descriptive language in a proposal narrative inadvertently creates a quantified, auditable commitment, distinct from genuinely required match on programmes (DOJ, construction grants, many state and foundation awards) where cost sharing is still mandatory and enforced at closeout.

    Under-delivered cost sharing is treated by auditors as a disallowed cost, triggering a proportional reduction in drawable funds regardless of whether the shortfall was mandatory or voluntary. A “decline all voluntary cost share” policy calibrated to NSF norms misfires against a UKRI fEC award, where the 20% institutional contribution is structural, not optional. A no-cost extension can buy time to complete an outstanding commitment, but it does not waive the obligation — the shortfall must still be resolved before the award can close.

    The direction of travel across US federal science funders is towards evaluating proposals on merit rather than an applicant’s ability to co-invest. Institutions that update proposal-review checklists and budget-justification templates accordingly — while keeping separate, funder-specific guidance for programmes where cost sharing remains mandatory or structural — will reduce both audit exposure and the administrative overhead cost sharing has historically imposed.

  • Office of Grants Management vs Program Offices

    The Office of Grants Management is the part of a federal department — at the Department of Health and Human Services (HHS), the Office of Grants (OG), under the Assistant Secretary for Financial Resources (ASFR) — that sets department-wide policy, issues the Notice of Award, and enforces financial and compliance rules across every award. Individual program offices, by contrast, judge scientific and programmatic merit within their own subject area. Grantee institutions deal with both, for different reasons, throughout the life of an award.

    In one sentence: the Office of Grants Management is the administrative and financial authority that governs how federal grant funds are awarded, monitored, and closed out, while program offices decide what gets funded and why. HHS is the largest federal grant-making agency in the United States, and the distinction between its central grants office and its dozens of program offices is one of the most consistently misunderstood parts of the federal award lifecycle for institutional research administrators.

    What Does the Office of Grants Oversee?

    The HHS Office of Grants formulates department-wide grants policy and oversees its implementation across every HHS operating division. It does not decide which research or service proposals get funded; it decides how the resulting awards are administered, financed, and audited.

    According to a December 2023 U.S. Government Accountability Office review (GAO-24-106008), the Office of Grants “provides department-wide leadership on grants” and serves several government-wide roles beyond HHS itself. In January 2021, the Office of Management and Budget designated HHS to house the government-wide Grants Quality Services Management Office (Grants QSMO), which supports other federal agencies in adopting shared, standardised grants-management systems.

    • Developing and issuing department-wide grants policy, including the HHS Grants Policy Statement (GPS), last revised October 2024
    • Applying the Uniform Administrative Requirements, Cost Principles, and Audit Requirements codified at 45 CFR Part 75
    • Issuing the official Notice of Award (NoA) that legally obligates federal funds
    • Overseeing financial reporting, audit resolution, and closeout across all HHS awards
    • Running the Grants QSMO Marketplace, launched September 2022, which offers other agencies shared grants-management and payment platforms

    The scale is substantial: GAO reports the federal government distributed approximately $1.2 trillion in grants in fiscal year 2022 — roughly 19 percent of total federal spending, and over $400 billion more than FY 2019. HHS accounts for the largest share of any single federal grant-making agency.

    How Does the Office of Grants Differ From Program Offices?

    The core distinction is “how” versus “what.” The Office of Grants governs the administrative, financial, and regulatory mechanics of an award — eligibility of costs, reporting deadlines, audit requirements, closeout. Program offices — the National Institutes of Health institutes, the Health Resources and Services Administration bureaus, the Administration for Children and Families divisions, and similar bodies — set programmatic priorities, write the Funding Opportunity Announcement’s scientific or service requirements, and judge whether a grantee is meeting technical objectives.

    Function Office of Grants (Grants Management) Program Office
    Primary question answered Is this cost allowable and compliant? Is this science/service meeting its goals?
    Issues Notice of Award Yes No
    Sets scientific/programmatic scope No Yes
    Reviews financial/progress reports Financial reports, audit findings Technical/programmatic progress reports
    Governs closeout mechanics Yes Provides final technical sign-off
    Typical grantee contact Grants Management Specialist Project Officer / Program Officer

    Grantee institutions need two working relationships per award: a technical relationship with the program office’s project officer, and an administrative relationship with the grants management specialist. Sending a budget modification to a project officer instead of the specialist is a routine, avoidable source of delay.

    Where Does OASH’s Own Grants Function Fit In?

    A frequent source of confusion is the phrase “OASH Office of Grants Management.” The Office of the Assistant Secretary for Health (OASH) operates its own grants and cooperative agreements function, published at health.gov/grants, covering programmes such as Title X family planning and adolescent health initiatives that OASH itself administers.

    This is not a separate, competing authority to the department-wide Office of Grants under ASFR. OASH’s grants activity operates within the HHS-wide policy framework — the same Grants Policy Statement and 45 CFR Part 75 requirements apply — but OASH runs its own competitions, issues its own Funding Opportunity Announcements, and assigns its own grants management staff for the awards it makes. A grantee dealing with OASH therefore interacts with an OASH-specific contact who still answers to department-wide policy. This layered structure — one policy authority, multiple operating-division grants functions beneath it — is largely absent from generic explainer pages, which describe either the federal picture or a single state office, not HHS’s two-tier structure.

    Every accredited research institution maintains an institutional counterpart to the federal grants office: the sponsored programs office (sometimes called Office of Research Administration or Grants and Contracts). Its function mirrors the Office of Grants Management’s role, but from the recipient side.

    The sponsored programs office is the institution’s authorised signatory for award acceptance, its central point for compliance with 45 CFR Part 75 and OMB Uniform Guidance (2 CFR Part 200), and its liaison to the HHS grants management specialist rather than the program office’s project officer. Bodies such as the National Council of University Research Administrators (NCURA) and INORMS document this division of labour consistently: principal investigators own the science; the sponsored programs office owns the compliance interface. For a broader view of this interface within institutional research administration practice, see CASRAI’s research administration resources.

    What Happens at Closeout and With Cost Sharing?

    Two compliance touchpoints sit squarely with the Office of Grants Management rather than the program office: closeout and cost sharing.

    A grant closeout report is the set of final documents — the Federal Financial Report, the final progress report, and any property disposition report — that a recipient must submit once the period of performance ends. Under the Uniform Guidance framework that 45 CFR Part 75 incorporates for HHS awards, these reports are due within a fixed post-performance window, after which unspent funds are deobligated and the award is formally closed by the grants management office, not the program office.

    Cost sharing (sometimes called matching) is the portion of total project cost that the recipient institution — not the federal award — commits to fund, whether required by statute or offered voluntarily in the proposal. The Office of Grants Management verifies documented cost-sharing commitments were actually met before an award can close; a shortfall found at closeout is a grants-management finding, even when the project was scientifically successful.

    Frequently Asked Questions

    What does a grants manager do?

    A grants manager at a federal Office of Grants administers the financial and compliance lifecycle of an award: reviewing budgets, issuing the Notice of Award, monitoring reporting compliance, and processing closeout. This role is distinct from a project officer, who judges technical or scientific performance.

    What is the grant management function?

    The grant management function is the administrative infrastructure — policy, systems, and staff — that a funding agency uses to award, monitor, and close federal financial assistance. At HHS this sits with the Office of Grants under ASFR, applying the Grants Policy Statement and 45 CFR Part 75 across every operating division.

    What are common mistakes in grant management?

    The most common mistakes are routing compliance questions to a project officer instead of the grants management specialist, missing the fixed closeout deadline, and failing to document cost-sharing commitments contemporaneously rather than reconstructing them at award end.

    What are grant management services?

    Grant management services cover pre-award risk assessment, Notice of Award issuance, ongoing compliance monitoring, and closeout processing. HHS centralises much of this through its Recipient Data Insights tool, which automates pre-award risk scoring department-wide.

    Implications and Outlook

    For institutions holding HHS awards, the practical takeaway is structural, not procedural: two distinct offices govern every award, and each has authority the other cannot override. A program office cannot waive a 45 CFR Part 75 cost-allowability rule, and the Office of Grants Management cannot override a program office’s technical judgement on scientific merit.

    HHS’s modernisation record shows this split hardening rather than dissolving. The ReInvent Grants Management initiative (2017–2020) and the September 2022 Grants QSMO Marketplace launch both centralised administrative infrastructure further, while leaving programmatic decisions with the operating divisions. Institutions that route compliance questions to their sponsored programs office, and technical questions to the program office, will keep seeing faster processing than those that conflate the two.

  • Dimensions Altmetrics, Scopus & Web of Science: A DORA-Aligned Comparison

    Dimensions altmetrics, Scopus CiteScore, and Web of Science’s Impact Factor answer different questions about the same paper: how much online attention it attracted, how its journal’s four-year citation average compares, and how its two-year citation count compares against a curated index. No single number from any one database satisfies the San Francisco Declaration on Research Assessment (DORA)’s call for multi-indicator, qualitative-plus-quantitative evaluation — which is why research offices increasingly triangulate across all three.

    A citation database is a structured index of scholarly publications and their citation links, used to measure research coverage, impact, and attention across disciplines. Dimensions, Scopus, and Web of Science each build that index differently, and the differences matter directly for institutions trying to run dimensions altmetrics-aware, DORA-compliant assessment rather than single-metric ranking.

    How does coverage differ across Dimensions, Scopus and Web of Science?

    Coverage breadth is the single biggest structural difference between the three databases, and it is measurable rather than a matter of opinion. A 2021 Scientometrics study by Singh, Singh, Karmakar, Leta and Mayr found that Dimensions indexes 82.22% more journals than Web of Science and 48.17% more journals than Scopus, largely because Dimensions ingests preprints, grants, patents, clinical trials, and policy documents alongside conventional journal articles.

    A separate large-scale comparison published in Quantitative Science Studies (Visser, van Eck and Waltman, 2021, MIT Press) benchmarked Scopus, Web of Science, Dimensions, Crossref and Microsoft Academic together and found that Dimensions and Crossref offer the broadest raw coverage, while Scopus and Web of Science retain more curated, higher-quality affiliation and subject metadata. Web of Science’s Core Collection remains the most selective of the three, with editorial evaluation criteria dating to Eugene Garfield’s 1960 Science Citation Index; Scopus, launched by Elsevier in 2004, applies a comparatively more inclusive Content Selection and Advisory Board process.

    The practical implication: a citation count pulled from only one database will systematically undercount or overcount depending on discipline, document type, and region. A 2020 comparison from the German Kompetenznetzwerk Bibliometrie (Stahlschmidt and Hinze) reached the same conclusion — the three sources are not interchangeable, and cross-checking is a foundational bibliometric hygiene step, not an optional extra.

    What metrics does each database produce?

    Each platform has developed its own headline indicator, and none of the three is a like-for-like substitute for the others.

    Database Owner Headline metric Citation window Altmetrics integration
    Dimensions Digital Science Citation counts + linked Altmetric Attention Score No fixed window; article-level Native — shares parent company with Altmetric
    Scopus Elsevier CiteScore; Field-Weighted Citation Impact (FWCI) via SciVal 4-year rolling window PlumX Metrics
    Web of Science Clarivate Journal Impact Factor (JCR) 2-year window (5-year variant available) Article-level usage counts; expanding via Research Intelligence tools

    CiteScore, introduced by Elsevier in 2016, divides all citations a journal receives in a given year by all documents (not only “citable items”) published in the preceding four years, and is published free of charge — a deliberate contrast with the subscription-gated Journal Impact Factor. Field-Weighted Citation Impact normalises a paper’s citations against the world average for its subject, publication year, and document type, where a score of 1.0 represents parity with the global average; this makes FWCI more field-comparable than a raw citation count. The Altmetric Attention Score, meanwhile, is not a citation metric at all — it is a weighted count of online attention (news coverage, policy documents, X/social posts, Wikipedia references, blogs) that Dimensions surfaces natively because Dimensions and Altmetric are both Digital Science products.

    Which database best supports DORA-compliant, multi-indicator assessment?

    DORA, published in 2012 and now signed by thousands of organisations worldwide, asks institutions to stop using journal-based metrics such as the Impact Factor as a proxy for the quality of an individual researcher’s contributions, and instead to consider the value and impact of all research outputs alongside qualitative peer judgement. The 2015 Leiden Manifesto (Hicks, Wouters, de Rijcke and Rafols, published in Nature) added ten operating principles for responsible metrics use, including that quantitative evaluation should support, not replace, qualitative expert assessment.

    All three database vendors now publicly reference these frameworks, but their practical alignment differs. Digital Science, Dimensions’ parent company, is listed on DORA’s public signatory register, and Dimensions’ native pairing with Altmetric gives assessors an attention-based indicator alongside citations without needing a separate subscription. Elsevier has endorsed the Leiden Manifesto and built CiteScore’s open methodology partly in response to its principles. Clarivate likewise cites the Leiden Manifesto in its own responsible-metrics guidance and has begun layering a “Societal Impact Framework” onto Web of Science Research Intelligence to capture impact beyond citation counts.

    None of the three databases is independently DORA-compliant by design — compliance is a property of how an institution uses the data, not of the database itself. A single Impact Factor, CiteScore, or Altmetric Attention Score used alone to rank individuals contradicts DORA regardless of source. Multi-indicator assessment requires combining citation-based indicators from at least one curated database with attention-based indicators and qualitative peer review — which is precisely why UK funders and the Research Excellence Framework have explicitly excluded journal impact factors from submission guidance since 2014, requiring panel-level qualitative judgement instead.

    Where does OpenAlex fit as an open alternative?

    OpenAlex, launched in 2022 by the non-profit OurResearch as a fully open successor to the discontinued Microsoft Academic Graph, has emerged as the fourth reference point in this comparison. Unlike Dimensions, Scopus, and Web of Science, OpenAlex publishes its entire dataset and API without subscription cost, drawing on Crossref, ORCID, and ROR identifiers for disambiguation rather than proprietary matching.

    OpenAlex does not yet match the curated metadata quality or the established institutional trust of Scopus or Web of Science, and it carries no equivalent to the Altmetric Attention Score. But for institutions constrained by licensing budgets, or for bibliometrics tools built on reproducible, auditable pipelines, OpenAlex is increasingly used as a free cross-check against the commercial databases rather than a replacement for them.

    Answer-first questions

    What is Altmetric a measure of?

    Altmetric measures online attention, not citation impact. It tracks mentions of a research output across news media, policy documents, social platforms, blogs, and Wikipedia, then produces a weighted Attention Score. Because it captures engagement that predates or bypasses formal citation, it is treated as complementary to citation-based indicators, not a replacement for them.

    What counts as a good Altmetric score?

    There is no universal threshold, because Attention Scores vary enormously by field, output type, and publication date. As a rough benchmark, Altmetric itself notes that a score above roughly 20 typically outperforms most tracked outputs, but comparisons are only meaningful against similar papers in the same journal and timeframe, never as an absolute cutoff.

    Is Scopus or Web of Science better for research assessment?

    Neither is unconditionally “better” — Scopus offers broader, more geographically diverse journal coverage with a transparent four-year CiteScore, while Web of Science offers deeper historical coverage back to 1900 and the still-widely-recognised Impact Factor. DORA-aligned assessment favours using both alongside non-citation indicators rather than choosing one as authoritative.

    Implications for research offices

    Research administrators selecting or combining these tools should treat the choice as an assessment-design decision, not a procurement afterthought. Three practical consequences follow directly from the coverage and metric differences above:

    • A researcher’s citation count and h-index will differ meaningfully between Dimensions, Scopus and Web of Science — institutions must specify and disclose which source underlies any reported figure.
    • Attention-based data (Altmetric, PlumX) captures policy and public engagement that citation-only databases miss entirely, which matters for funders assessing societal impact pathways.
    • Free, open sources such as OpenAlex are viable supplementary cross-checks, particularly where licensing cost restricts access to all three commercial platforms.

    Conclusion

    The three databases are converging on responsible-metrics language while remaining structurally distinct in coverage, indicator design, and cost. Institutions that want genuinely DORA-compliant, multi-indicator assessment should treat Dimensions, Scopus and Web of Science as complementary evidence sources — pairing at least one citation database with an attention-based indicator and qualitative peer review — rather than defaulting to whichever single number is easiest to pull from a subscription dashboard.