CASRAI Dictionary

Tag: clinical data management plan

  • Horizon Europe Calls 2026: Deadlines & OA Rules

    Horizon Europe calls 2026 run across the EU’s €14 billion 2026-2027 Work Programme, published by the European Commission on 11 December 2025 and searchable on the Funding & Tenders Portal. Pre-award teams should track submission status and cluster deadlines on the Portal, then verify each call’s specific open access and data-sharing conditions before drafting a proposal.

    The Horizon Europe Funding & Tenders Portal is the European Commission’s single official system for publishing, filtering and submitting proposals to every Horizon Europe call for proposals.

    What Is the Horizon Europe 2026-2027 Work Programme?

    The European Commission adopted the Horizon Europe 2026-2027 Work Programme on 11 December 2025, releasing over €14 billion in funding opportunities across the programme’s final two-year cycle, according to UK Research Office (UKRO) and Innovate UK Business Connect reporting on the publication. This is the last work programme under the current 2021-2027 Multiannual Financial Framework.

    The European Research Council (ERC) and European Innovation Council (EIC) published their 2026 work programmes separately and earlier in the cycle. Pre-award offices tracking “all open calls” therefore need to check the main Work Programme package and these two standalone documents together, not the main package alone.

    • Horizontal activities, including a new €540 million call supporting the Clean Industrial Deal and AI in Science
    • Marie Skłodowska-Curie Actions (MSCA) postdoctoral, doctoral network and staff exchange strands
    • Six thematic Clusters: Health; Culture, Creativity and Inclusive Society; Civil Security for Society; Digital, Industry and Space; Climate, Energy and Mobility; and Food, Bioeconomy, Natural Resources, Agriculture and Environment
    • Five EU Missions: Adaptation to Climate Change, Cancer, Restore our Ocean and Waters, Climate-Neutral and Smart Cities, and Soils

    How Do You Track Open Horizon Europe Calls in 2026?

    The EU Funding & Tenders Portal is the single official source for every Horizon Europe call; institutions should treat it, not third-party digests or mailing lists, as the system of record for deadlines and eligibility rules. Portal searches can be filtered and saved so that new topics matching an institution’s research areas trigger an automatic alert.

    An effective pre-award tracking routine has four steps:

    1. Filter the Portal’s “Search Funding & Tenders” screen by programme (Horizon Europe), submission status (“Forthcoming” or “Open for submission”), and programme part or cluster.
    2. Save the search and register for email notifications so new topics appear automatically rather than being missed between manual checks.
    3. Cross-check each topic’s call identifier (for example HORIZON-MSCA-2026-PF-01) against the relevant cluster or actions Work Programme PDF for full scope and evaluation criteria.
    4. Log the topic’s submission deadline, type of action (RIA, IA, CSA or COFUND), and open science conditions in the institution’s internal pipeline before allocating proposal-writing resources.

    National Contact Points add a second layer of verification: UK applicants, for example, can confirm topic scope and competitiveness with UKRI’s National Contact Point team before committing resources to a full proposal.

    What Are the Key Horizon Europe 2026 Call Deadlines?

    Most single-stage calls that opened in early 2026 close in September or October 2026, though Clusters 1, 4, 5 and 6 include topics with earlier or later cut-offs, according to Innovate UK Business Connect’s analysis of the published Work Programme. Pre-award teams should check each cluster individually rather than assume a single portfolio-wide deadline.

    Call Reference Deadline
    MSCA Postdoctoral Fellowships 2026 HORIZON-MSCA-2026-PF-01 9 September 2026
    MSCA Doctoral Networks 2026 HORIZON-MSCA-2026-DN-01 24 November 2026
    ERC Proof of Concept 2026 ERC-2026-POC 17 September 2026
    Restore our Ocean and Waters Mission calls Mission-specific topics 23 September 2026
    EU Space Research (Cluster 4, HaDEA-managed) Cluster 4 topics 2026 call, €90.97 million budget

    These dates illustrate the spread across strands rather than an exhaustive list. Every topic carries its own deadline on the Portal, and multi-stage calls add an earlier outline-proposal cut-off before the full submission date.

    What Open Access and Data-Sharing Obligations Apply Before You Submit?

    Every Horizon Europe grant agreement carries mandatory open science obligations that sit alongside the topic-specific scientific requirements, and reviewers assess a proposal’s data management approach as part of the excellence criterion. Confirming these terms before submission avoids a compliance gap that would otherwise surface only at the grant agreement stage.

    Three obligations apply to essentially every Horizon Europe-funded output:

    • Immediate open access to peer-reviewed publications, with no embargo period, deposited in a trusted repository and licensed under Creative Commons Attribution (CC BY) or an equivalent licence.
    • A Data Management Plan (DMP) as a mandatory deliverable, with a first version due within six months of the project start and updated as data-generation plans evolve.
    • FAIR data handling — Findable, Accessible, Interoperable and Reusable — applied under the principle of “as open as possible, as closed as necessary,” with closure permitted only for justified reasons such as intellectual property, personal data or security.

    Individual calls layer additional conditions on top of these baseline rules. A Cluster 1 (Health) topic handling clinical data, for example, carries stricter personal-data provisions than a Cluster 4 digital-infrastructure topic. Call-specific conditions are published in the topic’s own annex, not just the general Work Programme introduction, so pre-award teams must read both documents before finalising the proposal’s data management section.

    For terminology used across these obligations — contributor roles, persistent identifiers, licensing terms — the CASRAI Dictionary provides standards-aligned definitions that research administration teams can cite directly in DMPs and internal guidance.

    Common Questions About Horizon Europe Calls 2026

    Where can I find the official list of open Horizon Europe calls for 2026?

    The EU Funding & Tenders Portal is the European Commission’s official system listing every open, forthcoming and closed Horizon Europe call. Filter by programme, submission status and cluster, then save the search to receive automatic email alerts when new matching topics are published.

    How much funding is available in the Horizon Europe 2026-2027 work programme?

    The Commission made over €14 billion available across the 2026-2027 Work Programme, published 11 December 2025, covering MSCA, Research Infrastructures, the six thematic Clusters, the five Missions, and horizontal strands such as the Clean Industrial Deal call.

    Do Horizon Europe grants require open access to publications?

    Yes. Horizon Europe requires immediate open access with no embargo for all peer-reviewed publications, deposited in a trusted repository under a CC BY licence or equivalent, with open metadata describing the funding and licensing terms.

    What is a Data Management Plan and when is it due?

    A Data Management Plan (DMP) sets out how a project will generate, document, share and preserve research data under FAIR principles. It is a mandatory Horizon Europe deliverable, with a first version due within six months of the project start date.

    Implications for Pre-Award Teams

    Treating call-tracking and open science compliance as two separate workflows creates risk: a proposal can clear the Portal’s deadline filter yet still fail a topic’s data-sharing conditions during grant preparation. Pre-award offices get better outcomes by building a single checklist that logs the deadline, the type of action, and the open access and DMP conditions from the same read-through of the topic text.

    The 2026-2027 Work Programme is the final cycle before the next Multiannual Financial Framework, so institutions should expect the Commission to keep tightening open science verification at the reporting stage rather than relax it. Early, consistent DMP practice now reduces rework at grant signature. Research administration teams building this capability can align proposal, compliance and reporting language using the CASRAI research administration resources.

  • Horizon Europe Work Programme 2026-2027 Guide: Open Access and FAIR Data Changes

    Horizon Europe Work Programme 2026-2027 keeps the core open science mandate intact — immediate open access, FAIR data and a Data Management Plan for every project that produces data — while cutting call topics by 35%, expanding lump-sum funding to roughly half of all calls, and introducing new cross-cluster “horizontal calls”. For grant offices, the compliance clauses have not moved; the surrounding administrative machinery has.

    The Horizon Europe Work Programme 2026-2027 is the European Commission’s final two-year implementation plan for the 2021-2027 Horizon Europe framework, published in December 2025 and covering all funding calls, budgets and eligibility rules through the end of the programme.

    What changed in the Horizon Europe Work Programme 2026-2027?

    The European Commission adopted the Horizon Europe Work Programme 2026-2027 on 12 December 2025, according to the European Health and Digital Executive Agency (HaDEA). The Commission committed over €14 billion across the 2026 and 2027 calls, spanning all three Pillars, the Missions, Widening Participation and Strengthening the European Research Area (WIDERA), and the New European Bauhaus Facility, as confirmed by Innovate UK Business Connect’s summary of the published documents.

    The headline structural change is scale: the Commission’s General Introduction to the 2026-2027 Work Programme states that the number of topics across Pillar 2’s collaborative research Clusters was cut by 35% compared with the 2023-2024 Work Programme, a reduction also reported independently by Science|Business and EMDESK. Fewer, broader topics replace the previous highly prescriptive call texts.

    Dimension Work Programme 2023-2025 Work Programme 2026-2027
    Pillar 2 call topics Baseline count 35% fewer topics
    Lump-sum funding share Partial, growing Approx. 50% of all calls
    Open access mandate Immediate OA, CC BY, no embargo Unchanged
    FAIR data / DMP requirement Mandatory; “as open as possible, as closed as necessary” Unchanged; EOSC integration reinforced
    Cross-cluster “horizontal calls” Not used Introduced (e.g. Clean Industrial Deal, AI in science)
    Committed budget signalled — Over €14 billion

    Open access to publications: what’s the same, what’s different

    Nothing has changed in the core publication mandate. Under the Horizon Europe Model Grant Agreement, beneficiaries must ensure immediate open access to peer-reviewed publications reporting funded results, with no embargo period, deposit in a trusted repository, and a licence — typically Creative Commons Attribution (CC BY) or equivalent — that permits reuse, redistribution and text and data mining.

    What grant offices should actually re-check is the supporting metadata clause, not the licence clause. The 2026-2027 General Annexes continue to require full bibliographic metadata and persistent identifiers (DOI, ORCID iD, ROR) on every deposited publication. Institutions that let repository metadata quality slip during the 2023-2025 cycle should treat the new Work Programme as a trigger to re-audit templates, not assume automatic carry-over.

    • Confirm the trusted-repository and CC BY licence clause wording in your institutional agreement template matches the 2026-2027 General Annexes text
    • Update publication-metadata forms to capture DOI, ORCID iD and ROR identifiers consistently
    • Re-brief researchers that “no embargo” still means no embargo, even for monographs and long-form outputs
    • Flag any project bidding into a new horizontal call for additional cross-cluster reporting fields

    FAIR data, Data Management Plans and the EOSC push

    The FAIR data obligation is also unchanged in substance: research data generated or collected under a funded grant must be Findable, Accessible, Interoperable and Reusable, and every applicable project must maintain a Data Management Plan (DMP) that is created early and updated across the project lifecycle. The principle “as open as possible, as closed as necessary” continues to govern the balance between openness and legitimate restriction — intellectual property, personal data and security exceptions still apply, but even restricted datasets must carry FAIR, openly accessible metadata.

    What is new is emphasis, not obligation. Work Programme documentation for the Missions strand explicitly references infrastructures “federated under the European Open Science Cloud (EOSC)”, and the 2026-2027 cycle leans further into EOSC as the delivery mechanism for FAIR compliance — pushing project consortia towards EOSC-compatible repositories and machine-actionable metadata rather than institution-specific solutions. Grant offices whose DMP templates still point researchers to generic “any FAIR repository” language should update guidance to name EOSC-aligned options explicitly.

    Structured contributor metadata is part of the same compliance chain: publications reporting Horizon Europe-funded work increasingly carry standardised role disclosures. CASRAI originated the CRediT contributor role taxonomy in 2014, and the standard is now stewarded by NISO as ANSI/NISO Z39.104-2022 — grant offices building publication-metadata checklists can treat CRediT-style role tagging as a practical way to strengthen the bibliographic metadata clause without waiting for a funder mandate to force it.

    Structural and procedural changes that affect compliance workflows

    Three procedural shifts in the 2026-2027 Work Programme indirectly affect how open science obligations get delivered, even though the obligations themselves are stable.

    • Lump-sum funding expansion. EMDESK’s analysis, citing Science|Business reporting on the final Work Programme text, puts lump-sum funding at roughly 50% of all 2026-2027 calls — up sharply from the partial rollout in 2023-2025. Lump-sum grants change how compliance is verified, since cost reporting is replaced by milestone and deliverable verification, which shifts open-access and DMP checks toward deliverable sign-off rather than cost-claim audit.
    • Horizontal calls. New cross-cluster calls address themes such as the Clean Industrial Deal and AI in science, spanning multiple Clusters within Pillar 2. These calls typically generate larger, more heterogeneous datasets, making FAIR data planning and interoperable metadata schemas more operationally important than under single-Cluster calls.
    • Broader, less prescriptive topics. With 35% fewer topics, each call description covers more ground, meaning the same open-access and data clauses now apply across a wider range of project types per topic — grant offices should not assume a topic’s compliance profile is self-evident from a shorter call text.

    Grant office FAQs and what happens next

    When did the Horizon Europe Work Programme 2026-2027 take effect?

    The European Commission adopted the Horizon Europe Work Programme 2026-2027 on 12 December 2025, per HaDEA’s official announcement, opening the programme’s final two-year cycle. Most single-stage call deadlines fall in September or October 2026, though some Clusters open earlier, with deadlines in March or April 2026.

    Is open access still mandatory under Horizon Europe 2026-2027?

    Yes. The 2026-2027 Work Programme retains the immediate open access mandate for peer-reviewed publications: no embargo, deposit in a trusted repository, a CC BY (or equivalent) licence, and complete bibliographic metadata with persistent identifiers. Grant offices should verify these clauses remain unchanged in institutional agreement templates.

    What is the FAIR data requirement in Horizon Europe 2026-2027?

    FAIR data means research data must be Findable, Accessible, Interoperable and Reusable, with a Data Management Plan required for projects that generate or collect data. “As open as possible, as closed as necessary” continues to apply, and metadata must remain FAIR even when underlying data is restricted.

    How many fewer call topics are there in the 2026-2027 Work Programme?

    According to the Commission’s General Introduction, Pillar 2’s collaborative research Clusters saw a 35% reduction in the number of topics compared with the 2023-2024 Work Programme, consolidating funding into broader, less prescriptive topic descriptions.

    None of this changes the substance of what a research office signs up to when it accepts Horizon Europe funding: immediate open access, FAIR-managed data, and a live Data Management Plan remain non-negotiable. What has changed is the operating environment around those obligations — fewer but broader topics, half of all calls running on lump sums, and new cross-cluster calls that will generate messier, larger datasets than before. Institutions that treat the 2026-2027 Work Programme as a compliance-template refresh, not just a new set of calls to bid into, will spend less time firefighting metadata and DMP queries once projects are underway.

  • MRC Data Management Plan vs Wellcome Rules for Bioscience Grantees

    An MRC data management plan (DMP) sets out how researchers will collect, document, store, secure and share data on an MRC-funded project, using UK Research and Innovation’s (UKRI) official template. Wellcome instead requires a broader “outputs management plan” covering data, software and physical materials, with no fixed template. Both are due at application stage, but their scope, sharing timelines and repository rules differ in ways that matter for bioscience grantees.

    A data management plan is a funder-mandated document that specifies how research data will be handled, from creation through to long-term preservation and reuse.

    What must an MRC data management plan include?

    The Medical Research Council (MRC), a UKRI council, requires all funding applicants to submit a DMP as part of their research proposal. Applicants must use UKRI’s official MRC data management plan template, an ODT document last revised on 1 April 2024 to align with the MRC’s revised data sharing policy.

    The template asks researchers to address:

    • Data types and volumes — what will be generated or reused, and in what formats.
    • Documentation and metadata — how the data will be made interpretable to other researchers.
    • Ethics and legal compliance — data protection, consent and confidentiality arrangements.
    • Storage, backup and security — arrangements during the life of the project.
    • Sharing and preservation — the named repository and any restrictions on access.
    • Trusted research and innovation (TRI) considerations — a requirement added in the April 2024 revision, reflecting UKRI-wide guidance on research security.

    The underlying MRC data sharing policy was itself revised on 29 November 2023 to reflect the commitments in the MRC’s Strategic Delivery Plan 2022 to 2025, incorporating a wider definition of “research data” and updated open access and data protection law. Reviewers assess DMPs against a published rubric, and MRC guidance states it expects valuable data to be shared with as few restrictions as possible.

    How does Wellcome’s outputs management plan differ?

    Wellcome does not ask for a “data management plan” in the MRC sense. Its Policy on Data, Software and Materials Management and Sharing — released on 10 July 2017, replacing an earlier Policy on Data Management and Sharing — requires an outputs management plan wherever a project will generate data, software or materials of clear value to others.

    Three features distinguish the Wellcome approach from MRC’s:

    • Broader scope — the plan must cover physical materials such as antibodies and cell lines, not only digital data and software.
    • No fixed template — applicants draft a plan “proportionate” to the scale and likely value of the outputs, rather than completing a standard form.
    • Living document — the plan is expected to be maintained and reviewed throughout the research lifecycle, not filed once at application stage.

    Wellcome frames its position as “as open as possible, as closed as necessary” — language that mirrors the European Commission’s Horizon Europe open-data principle — allowing restrictions to protect participant confidentiality or to enable intellectual property to be developed under its IP and patenting policy.

    MRC vs Wellcome: data-sharing requirements compared

    The table below summarises the structural differences a bioscience grantee applying to both funders needs to reconcile.

    Feature MRC Wellcome
    Plan name Data management plan (DMP) Outputs management plan
    Template Fixed UKRI ODT template (rev. April 2024) No template; proportionate free-text plan
    Scope Research data Data, software and physical materials
    Governing policy MRC data sharing policy (rev. Nov 2023) Policy on Data, Software and Materials Management and Sharing (2017)
    Review Assessed by peer reviewers against a published rubric Assessed as part of the wider proposal; monitored at end-of-grant reporting
    Extra checks Trusted research and innovation considerations required IP and patenting policy considerations required
    Repository expectation Discipline-specific repository, minimal restrictions Recognised community repository with persistent identifiers

    What are the sharing timelines and repository rules?

    Wellcome sets the more explicit timeline of the two funders. Its policy states that, as a minimum, data underpinning a research paper must be made available at the time of publication, along with any original software needed to view the dataset or replicate the analysis. For research related to public health emergencies, Wellcome requires quality-assured interim and final data to be shared “as rapidly and widely as possible”, ahead of journal publication.

    MRC’s policy is principles-based rather than date-bound: it asks applicants to share data “in a timely and responsible manner” with as few restrictions as possible, leaving the specific timeline to be justified case by case in the DMP itself.

    On repositories, both funders expect deposit in a recognised, discipline-appropriate service with persistent identifiers where possible. Wellcome additionally operates Wellcome Open Research, a publishing platform for rapid dissemination of funded results. On costs, both funders will fund justified data-sharing expenses within the grant; notably, in early 2018 Wellcome, the MRC, Cancer Research UK and the Bill & Melinda Gates Foundation jointly announced they would cover the costs of sharing clinical trials data via the Clinical Study Data Request (CSDR) platform — a rare example of aligned funder practice that removes cost as a barrier to compliance.

    Common questions about data management plans

    What is a data management plan?

    A data management plan (DMP) is a formal document describing how research data will be collected, documented, stored, secured and shared throughout and after a project. UK funders including MRC and Wellcome require a DMP, or an equivalent outputs plan, at application stage to demonstrate researchers have planned for responsible data stewardship and future reuse.

    How to write a data management plan?

    Writing a DMP means addressing data type and volume, documentation and metadata standards, ethical and legal compliance, storage and security arrangements, and a sharing and preservation route via a named repository. MRC applicants must use UKRI’s fixed template; Wellcome applicants draft a proportionate outputs management plan without a set format.

    What are the 5 steps to data management?

    Most funder templates cover five areas: data description, documentation and metadata, ethical and legal compliance, storage and security, and data sharing and preservation. MRC and Wellcome both map onto this structure, though Wellcome extends the final step to cover software and physical materials alongside data.

    What this means for UK bioscience grant applicants

    Researchers holding, or applying for, both MRC and Wellcome funding on related bioscience or clinical work cannot use a single generic DMP. The MRC’s fixed template and trusted-research-and-innovation checks demand a structured, form-based response; Wellcome’s proportionate outputs management plan demands editorial judgement about what counts as a “significant” output and how physical materials will be tracked alongside data.

    For institutional research administration teams, the practical implication is a checklist mismatch: MRC compliance is verified against a rubric at peer review, while Wellcome compliance is verified narratively at end-of-grant reporting. Multi-funder consortium grants — increasingly common in UK bioscience — should draft to the stricter of the two requirements (typically Wellcome’s publication-time data availability) and then map that single commitment back into each funder’s own plan format, rather than drafting two plans independently.

    As UKRI continues to harmonise data policy guidance across its seven councils, and as Wellcome’s outputs-based model gains attention from other biomedical funders, expect further convergence — but for now, grantees still need to satisfy two distinct documents, two distinct review processes, and two distinct definitions of what “data” even covers.

  • Research Data Repository: Generalist vs Subject

    Choose a discipline-specific repository whenever one exists for your data type, and fall back to a generalist repository such as Zenodo, Figshare or Dryad only when no subject-specific option is available. A research data repository is a system that assigns persistent identifiers, retains data over the long term, and exposes machine-readable metadata so datasets can be found, cited and reused. The right choice depends on discoverability within your field, what your funder actually mandates, and who is committed to curating the data after the grant ends.

    What is a research data repository?

    A research data repository is a curated system for depositing, preserving and exposing datasets independently of the article they support. Unlike a general-purpose cloud drive, a qualifying repository issues a persistent identifier (typically a DOI), retains fixity and version history, and publishes structured metadata that search engines and indexing services can crawl.

    Two broad categories exist. Generalist repositories — Zenodo, Figshare, Dryad, the Open Science Framework, Harvard Dataverse — accept any discipline and any file type. Discipline-specific repositories — the Protein Data Bank, OpenNeuro, ICPSR, the UK Data Service’s ReShare — are built around domain metadata schemas, controlled vocabularies and, often, expert curators who understand the data.

    Generalist vs discipline-specific: what’s actually different?

    The two repository types are not interchangeable, even though both can technically hold the same file. They differ in who finds the data, how deeply it is described, and how funders treat the deposit for compliance purposes.

    Factor Generalist repository Discipline-specific repository
    Discoverability Indexed broadly; weaker within a subject community High within the field via domain search portals and cross-references
    Metadata depth Generic (title, creator, subject, DOI) Domain-specific schemas (e.g. genomic, crystallographic, survey metadata)
    Curation Largely automated; minimal review Often expert-reviewed before publication
    Funder acceptance Accepted as a fallback by nearly all funders and journals Frequently the stated first preference where one exists
    Typical cost to depositor Free (Zenodo, OSF) or freemium (Figshare) Varies — free (ICPSR, OpenNeuro) to fee-charging (some subject archives)
    Best for Interdisciplinary, mixed-format, or “no domain home” datasets Data types the community already expects to find in one place

    The registries FAIRsharing and re3data.org, both supported by DataCite, list several thousand repositories across disciplines and are the standard starting point for checking whether a subject-specific option exists before defaulting to a generalist platform.

    Does your funder require a specific repository type?

    Funder and journal policy is usually the deciding factor, not personal preference. Most major funders now state an explicit hierarchy: use a recognised discipline repository first, and use a generalist repository — provided it is FAIR-aligned — only where none exists.

    Funder / body Repository requirement
    Horizon Europe Model Grant Agreement Article 17 requires deposit in a research data repository, following the principle “as open as possible, as closed as necessary”
    UKRI Open access policy (in force since 1 April 2022) requires data underpinning a publication to be findable, accessible, interoperable and reusable, with access details stated in a data access statement
    NIH Data Management and Sharing Policy, effective 25 January 2023, requires a data management plan and preference for an established public repository appropriate to the data type
    ICMJE journals Data sharing statement required for clinical trials that began enrolment on or after 1 January 2019

    Where a policy is silent on repository type, DataCite’s Repository Finder tool cross-references FAIRsharing and re3data metadata to surface certified, FAIR-aligned repositories for a given data type — a step that is worth doing before defaulting to whichever repository a colleague used last time.

    Which option wins on long-term curation and sustainability?

    This is the trade-off least discussed in generic repository guidance, and it matters more than discoverability once a dataset is more than a few years old. Discipline-specific repositories often provide deeper curation at deposit time, but many depend on renewable grant funding, which creates a real risk of the archive itself losing support, freezing new deposits, or migrating without notice.

    Generalist repositories carry a different risk profile. Zenodo is operated by CERN with backing from OpenAIRE and the European Commission; Figshare is commercially operated by Digital Science; the Open Science Framework is run by the non-profit Center for Open Science. None of these guarantees permanence, but their institutional backing is typically more diversified than a single-grant-funded domain archive.

    • Ask whether the discipline repository has a named institutional or consortium backer, not just a project grant.
    • Check whether the repository is a CoreTrustSeal-certified trustworthy digital repository — certification signals an audited preservation commitment.
    • If the domain archive’s funding horizon is unclear, consider a dual deposit: primary copy in the discipline repository for discoverability, mirrored DOI in a generalist repository as a preservation backstop.

    How do you actually decide? A five-step framework

    Use this sequence rather than defaulting to whichever repository is fastest to sign up for:

    1. Check the funder mandate first. If your grant agreement or journal’s data sharing policy names a required or preferred repository type, that overrides personal choice.
    2. Search FAIRsharing and re3data for a certified discipline-specific option matching your data type, format and jurisdiction.
    3. Assess curation depth needed. Complex, reusable data (genomic sequences, clinical trial data, crystal structures) benefits from expert domain curation; simple supplementary files often do not need it.
    4. Weigh sustainability. Prefer CoreTrustSeal-certified or institutionally-backed repositories over unaffiliated project archives, especially for data with a multi-decade reuse horizon.
    5. Default to a generalist repository only when no suitable, FAIR-aligned discipline repository exists — and record the choice and rationale in your data management plan.

    Answer-first Q&A

    What is a data repository in research?

    A data repository is a system or service where researchers deposit datasets to obtain a persistent identifier, structured metadata, and long-term hosting. It exists separately from a journal article so that data can be found, cited and reused independently of the publication it supports.

    What is an example of a data repository?

    Zenodo and Figshare are widely used generalist examples; the UK Data Service’s ReShare and the Protein Data Bank are widely used discipline-specific examples. Each assigns a DOI, retains version history, and exposes metadata for discovery by search engines and domain indexes.

    What is a research repository?

    “Research repository” is often used loosely to mean either a data repository (datasets) or an institutional repository (publications, theses). In a data management context, it specifically refers to a certified system for archiving and publishing the datasets underlying research outputs.

    What this means for your data management plan

    A data management plan should name the intended repository before data collection begins, not after submission. Reviewers at UKRI, NIH and Horizon Europe increasingly check whether the named repository matches the funder’s stated hierarchy — generalist repositories named without justification, when a recognised discipline archive exists, are a common cause of DMP revision requests.

    The practical position for most research teams is not “generalist or discipline-specific” as a permanent allegiance, but a per-dataset decision applied consistently: check the mandate, search the registries, weigh curation against sustainability, and document the reasoning. That documented reasoning — more than the repository name itself — is what demonstrates genuine engagement with FAIR data principles to funders, reviewers and future re-users.

  • Data Sharing Agreement Template UK: Research Collaboration Guide

    A data sharing agreement is legally required under UK GDPR when two or more institutions act as joint controllers of personal data in a research collaboration — Article 26 makes this a binding obligation, not a discretionary policy choice. It is a legal contract, distinct from a data management plan, with no equivalent status in data protection law. Searching for a generic data sharing agreement template UK institutions can copy is the wrong starting point: the correct document depends on your controller status, not a fill-in-the-blank form.

    A data sharing agreement is a written contract between two or more organisations that sets out the purpose, scope, lawful basis, security standards, and responsibilities governing an exchange of personal data. For research administrators coordinating multi-institution studies, knowing exactly when one is mandatory — and how it differs from a data management plan or a data processing agreement — determines whether a project is compliant before the first dataset moves.

    Data sharing agreement vs data management plan: what’s the difference?

    These two documents are frequently conflated in research administration, but they serve different functions. A data sharing agreement is a legally binding contract between institutions. A data management plan (DMP) is a research-planning document, usually required by a funder as a grant condition, describing how data will be collected, stored, and archived over a project’s life.

    • Legal status — a data sharing agreement can be a binding contract; a DMP is a funder deliverable with no contractual force.
    • Trigger — a data sharing agreement responds to UK GDPR obligations; a DMP responds to funder grant terms.
    • Audience — a data sharing agreement binds the named institutions; a DMP is submitted to and reviewed by the funder.
    • Content focus — a data sharing agreement covers lawful basis, security, and liability; a DMP covers data formats, repositories, and preservation.

    UKRI’s data policy expects funded researchers to produce a DMP, and Horizon Europe’s Model Grant Agreement requires one as part of its open science obligations. Neither substitutes for a data sharing agreement where personal data crosses institutional boundaries — the two are complementary, not interchangeable.

    When does UK GDPR require a data sharing agreement?

    UK GDPR does not impose a blanket legal requirement to have a written data sharing agreement for every instance of data sharing. Whether one is mandatory depends on the legal relationship between the parties, not on the existence of a research project alone.

    Under Article 26 of UK GDPR, organisations that jointly determine the purposes and means of processing personal data — for example, two universities co-designing a study and jointly deciding what data to collect and how to use it — are joint controllers. The law requires them to set out their respective responsibilities in an arrangement, including who handles privacy notices, subject access requests, and the primary contact point for data subjects.

    Where institutions instead act as independent controllers — each using the shared data for its own separate purpose, such as one university passing anonymised cohort data to a partner for an unrelated secondary analysis — UK GDPR does not legally mandate a written agreement. The Information Commissioner’s Office (ICO) nonetheless recommends one as good practice, since it helps demonstrate the UK GDPR accountability principle.

    The regulatory landscape shifted further with the Data (Use and Access) Act 2025, which received Royal Assent on 19 June 2025 and amends both UK GDPR and the Privacy and Electronic Communications Regulations — institutions should check DSIT’s commencement timetable before assuming legacy practices remain unchanged.

    What must a data sharing agreement contain?

    The ICO’s statutory Data Sharing Code of Practice sets out what a data sharing agreement should cover, regardless of whether it is legally mandatory in a given case. A research-focused agreement should address:

    • The identity of every party, including a named Data Protection Officer contact.
    • The specific research purpose and why the sharing is necessary to achieve it.
    • A precise description of the data items shared, flagging any special category or criminal offence data.
    • The lawful basis each party relies on, which may differ between institutions.
    • The designated point of contact for data subjects — mandatory for joint controllers under Article 26.
    • Security, retention, and end-of-project deletion or return arrangements.
    • Breach-notification procedures and safeguards for any international data transfer.

    The table below distinguishes the three documents most often confused.

    Document Legally mandatory? Governs Typical owner
    Data sharing agreement Only for joint controllers (Article 26) Lawful basis, roles, security, liability Data Protection Officer / legal team
    Data processing agreement Yes, always (Article 28) Processor’s instructions from the controller Data Protection Officer / procurement
    Data management plan Only if the funder requires it Data formats, storage, archiving over project lifecycle Principal investigator / research office

    Data sharing agreement vs data processing agreement

    A data sharing agreement and a data processing agreement address opposite relationships. A data sharing agreement applies between two or more controllers who each decide, jointly or independently, how personal data will be used. A data processing agreement applies when a controller instructs a processor — an organisation handling data solely on the controller’s instructions, such as a cloud storage provider — to process personal data on its behalf. Article 28 of UK GDPR makes the processing agreement mandatory in every controller-to-processor relationship, with terms prescribed by law; no equivalent blanket rule exists for controller-to-controller sharing.

    Common questions on data sharing agreements

    Is a data sharing agreement legally required?

    A data sharing agreement is legally mandatory only when two or more organisations act as joint controllers under UK GDPR Article 26. For independent controllers sharing data for their own separate purposes, the ICO’s data sharing code recommends but does not legally require a written agreement — though skipping one weakens your accountability defence if challenged.

    What is the difference between a data sharing agreement and a data processing agreement?

    A data sharing agreement governs data moving between two controllers who each decide how it is used. A data processing agreement is legally required under UK GDPR Article 28 whenever a controller instructs a processor to handle data on its behalf. Confusing the two risks drafting entirely the wrong contractual terms for the relationship.

    What are the 7 golden rules of data sharing?

    The “seven golden rules” originate from UK government safeguarding guidance for practitioners, not from UK GDPR itself. They emphasise that data protection law is not a barrier to justified sharing, that sharing should be necessary and proportionate, and that decisions must be recorded — sound principles, but not a substitute for a formal data sharing agreement.

    What is the data sharing law in the UK?

    There is no single “data sharing law” — sharing personal data is governed by UK GDPR, the Data Protection Act 2018, and, since Royal Assent on 19 June 2025, the Data (Use and Access) Act 2025, which amends both frameworks. Research collaborations must also observe common-law confidentiality duties alongside these statutes.

    What this means for research administrators

    For institutions running multi-site studies, the practical starting point is a controller-relationship analysis, not a template download. Research offices should determine whether partners are jointly designing the research question — pointing to joint controllership and a mandatory Article 26 arrangement — or each applying the data to its own distinct purpose, pointing to independent controllership and a recommended, non-mandatory agreement. This should run alongside, not instead of, the DMP required by funders such as UKRI or Horizon Europe. Bodies like ARMA (the Association of Research Managers and Administrators) increasingly treat this controller-status check as standard due diligence, sitting alongside ethics review rather than as a legal afterthought.

    Getting the agreement right

    A data sharing agreement and a data management plan answer different questions: one sets the legal terms under which personal data moves between institutions; the other describes how research data will be handled and preserved over a project’s lifecycle. Joint decision-making about personal data requires the former as a matter of UK GDPR law; funders increasingly require the latter as a matter of grant compliance. Treating the two as interchangeable is the most common compliance gap in multi-institution research — build the controller-status check into standard research administration workflow, before data starts moving.

  • Clinical Data Management Plan vs Research Data Management Plan: What’s the Difference

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    A clinical data management plan and a research data management plan are two of the most frequently conflated documents in the clinical trial lifecycle. Both use the acronym “DMP” in casual conversation, both get drafted before a study starts, and both concern “data” in the broadest sense — but they answer to different masters, cover different lifecycle stages, and are read by different audiences. Submitting the wrong one to the wrong reviewer is a recurring, avoidable compliance headache for trial units and research offices alike.

    What Is a Clinical Data Management Plan?

    A Clinical Data Management Plan (CDMP) is an operational, trial-specific document that describes exactly how data will move from case report form (CRF) to locked database. It is written by or with the clinical data management (CDM) function — not the principal investigator’s grants office — and it sits alongside the protocol as one of the working documents that Good Clinical Practice (GCP), per ICH E6, expects a sponsor to maintain and be able to produce on inspection.

    A CDMP typically specifies:

    • CRF or eCRF design and the electronic data capture (EDC) system to be used
    • Database build, edit-check specifications and data validation rules
    • Data entry conventions (single vs double entry, query turnaround)
    • Medical coding dictionaries and versions, such as MedDRA and the WHO Drug Dictionary
    • Discrepancy management and serious adverse event reconciliation procedures
    • Roles, responsibilities and sign-off authority for database lock

    Because it is inspected against GCP, a CDMP is a living, version-controlled document updated through the study rather than filed once and forgotten.

    What Is a Research Data Management Plan?

    A Research Data Management Plan (RDMP) is a funder- or institution-facing document submitted at the grant proposal stage, well before a trial’s CDMP would even exist. Its job is compliance with funder and institutional data policy, not trial operations. UK Research and Innovation (UKRI) requires a data management plan for relevant grant applications, Horizon Europe applicants complete one through the Data Management Plan template built into the Horizon Europe Programme Guide, and the NIH Data Management and Sharing (DMS) Policy has required a DMS plan for NIH-funded research since January 2023.

    An RDMP typically covers:

    • What data types and volumes the project will generate or reuse
    • How data will be described, documented and made findable (metadata, identifiers)
    • Storage, security and access-control arrangements during the project
    • Ethical, consent and legal constraints on sharing (particularly for identifiable participant data)
    • Long-term preservation and repository plans, often with a DOI issued via DataCite
    • Alignment with the FAIR principles — Findable, Accessible, Interoperable, Reusable

    Unlike a CDMP, an RDMP is reviewed once (or at defined milestones) by a funder or research office, not audited line-by-line by a regulator during a GCP inspection.

    CDMP vs RDMP: Side-by-Side Comparison

    The table below sets out where the two documents genuinely diverge, so institutions running funded clinical trials know they usually need both — not one instead of the other.

    Dimension Clinical Data Management Plan (CDMP) Research Data Management Plan (RDMP)
    Primary purpose Ensure trial data is accurate, complete and audit-ready for database lock Satisfy funder/institutional policy on data stewardship and sharing
    Governing framework ICH E6 Good Clinical Practice; sponsor/CRO SOPs Funder mandates (UKRI, NIH, Horizon Europe); institutional RDM policy
    Typical author Data manager / clinical data management lead Principal investigator, often with library or research office support
    Created at Study set-up, before first patient enrolled Grant proposal stage, before funding is awarded
    Primary audience CDM team, biostatisticians, sponsor, regulatory inspectors Funder, ethics/IRB reviewers, institutional research office
    Content focus CRF design, edit checks, coding, database lock procedures Data description, storage, ethics, sharing, long-term preservation
    Review cadence Continuously updated through study conduct; inspected on audit Reviewed at proposal and, for some funders, at defined milestones

    Common Questions Answered

    What does a clinical data management plan include?

    A clinical data management plan includes CRF or eCRF specification, database design, data entry and validation procedures, edit-check logic, medical coding dictionaries such as MedDRA, discrepancy and adverse-event reconciliation processes, and clearly defined roles and responsibilities through to database lock, all maintained as a living, version-controlled document inspected under Good Clinical Practice.

    What should a data management plan include?

    A funder-facing research data management plan should describe the data types and volumes a project will generate, how data will be documented and made findable through metadata, storage and security arrangements, ethical and consent constraints on sharing identifiable data, and the eventual repository and preservation route, typically aligned to the FAIR data principles.

    What are the three phases of clinical data management?

    Clinical data management is generally organised into three sequential phases: study set-up, covering database build and CRF design; study conduct, covering data entry, cleaning and query resolution; and study close-out, covering final reconciliation, coding sign-off and database lock ahead of statistical analysis.

    Why the Distinction Matters for Research Administrators

    Institutions running externally funded clinical trials almost always need both documents, produced by different teams on different timelines. A funder reviewer looking for a FAIR-aligned sharing and preservation strategy will not find it in a CDMP’s edit-check specification — and a GCP inspector auditing database lock will not accept an RDMP’s high-level data-sharing statement as evidence of query resolution procedure.

    This is precisely the coordination gap that research administration functions increasingly exist to close: aligning the pre-award compliance document (the RDMP, owned by the grants office) with the operational trial document (the CDMP, owned by clinical data management) so that neither is quietly missing when a funder audit or a regulatory inspection arrives. Institutions that treat the two as interchangeable risk both funder non-compliance and GCP findings — for two entirely separate reasons.

    Consistent terminology helps here. Reviewers, auditors and research offices benefit from a shared reference for what each document is called and what it covers; the CASRAI research administration dictionary maintains definitions for terms that span exactly this pre-award-to-conduct boundary.

    Looking Ahead

    The line between the two documents is not static. ICH’s ongoing revision of E6 Good Clinical Practice has pushed sponsors toward more explicit, risk-based data governance language inside the CDMP itself, while funders such as UKRI and the NIH continue to tighten expectations for FAIR-aligned sharing inside the RDMP. Institutions that keep the two plans distinct — but explicitly cross-referenced — will be best placed to satisfy both regulators and funders as each side’s requirements keep evolving.