HE-R03 — Grants and Research Funding

Course Code: HE-R03  |  School: School of Higher Education  |  Cluster: Research Management

Level: Intermediate  |  Estimated duration: 6 weeks (approximately 40 hours)  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

Competitive research funding is allocated by a process that is simultaneously indispensable and demonstrably imperfect. Success rates in major schemes frequently sit below fifteen per cent, inter-reviewer agreement on proposal quality is often barely better than chance, and the Matthew effect documented by Bol and colleagues shows that early funding success produces later success independent of merit. Some serious scholars now argue for modified lotteries above a quality threshold.

This course is candid about that context while still teaching learners to compete effectively within it. The core skill is not persuasive writing in the abstract but the ability to read a funder’s intent accurately, to select the right scheme, and to construct a proposal in which significance, approach and feasibility are all evident to a tired reviewer reading twenty applications in a weekend.

Learners write. The course is structured around the production of a genuine, complete proposal for a real scheme, developed through structured peer review that mirrors panel conditions. Budget construction, data management planning, impact articulation and the handling of rejection and resubmission are all treated as examinable competencies.

Learning outcomes

On completion, a successful learner will be able to:

1. Identify and evaluate funding opportunities against a specific research idea and select the best-fitting scheme with justification.
2. Interpret a funder’s published priorities, assessment criteria and review process, and align a proposal to them precisely.
3. Construct a compelling case for support with a clear statement of significance, approach, feasibility and innovation.
4. Build an accurate, justified budget and write a budget narrative that withstands scrutiny.
5. Produce the standard supporting components, including data management, impact and risk plans.
6. Review proposals as a panel member would, and use structured peer critique to improve a draft.
7. Analyse the empirical evidence on peer review reliability and bias and respond strategically to rejection.

Who this course is for

Early-career researchers, doctoral candidates approaching completion, principal investigators seeking to improve success rates, and research development professionals who support applicants. Also useful to those who serve on funding panels.

Prerequisites

Learners must bring a research idea substantial enough to support a full proposal. HE-R01 Research Management Fundamentals gives useful context on funding systems but is not required.

Syllabus

Module 1 — The Funding Landscape and Scheme Selection

Most rejected proposals were submitted to the wrong scheme. This module builds systematic opportunity identification and honest self-assessment against eligibility and fit.

Lessons. 1.1 Mapping national, international, industry and philanthropic funders  ·  1.2 Reading a call for proposals for its unstated priorities  ·  1.3 Eligibility, career stage and the strategic timing of applications  ·  1.4 Assessing fit honestly before investing writing time

Deliverable. A diagnostic note evaluating three funding schemes against one research idea, with a reasoned selection and two rejections explained.

Module 2 — The Case for Support

Reviewers decide quickly, and a proposal that buries its significance in paragraph four is usually lost. The module builds the argumentative architecture that survives fast, tired reading.

Lessons. 2.1 Significance: why this problem, why now, why this matters  ·  2.2 Approach: design, methods and their justification  ·  2.3 Feasibility: track record, resources and preliminary work  ·  2.4 Structure, signposting and writing for the non-specialist reviewer

Deliverable. An analytical brief comprising a two-page case for support with significance, approach and feasibility fully argued.

Module 3 — Budgets, Justification and Compliance

Budgets are read as evidence of whether the applicant understands their own project. This module constructs one accurately and writes the narrative that defends it.

Lessons. 3.1 Staff, equipment, consumables, travel and overhead  ·  3.2 Full economic costing and funder-specific eligible cost rules  ·  3.3 Writing a budget justification that pre-empts objections  ·  3.4 Value for money without under-resourcing the work

Deliverable. A design artefact: a complete costed budget with justification narrative for the proposed project.

Module 4 — Supporting Components and Impact

Data management plans, impact statements and risk registers are frequently written last and score accordingly, despite carrying real assessment weight. The module treats them as substantive.

Lessons. 4.1 Data management planning and the FAIR principles  ·  4.2 Articulating impact credibly without inflation  ·  4.3 Risk registers, contingency and honest limitation  ·  4.4 Ethics, collaboration letters and institutional support

Deliverable. An evaluation report containing a data management plan, impact statement and risk register for the proposed project.

Module 5 — Peer Review, Bias and Resubmission

Understanding how proposals are actually assessed changes how they are written, and understanding the documented unreliability of that assessment changes how rejection is interpreted. The module simulates the panel.

Lessons. 5.1 How panels operate: triage, scoring, discussion and ranking  ·  5.2 Empirical evidence on reviewer agreement, bias and the Matthew effect  ·  5.3 Conducting structured peer review of a colleague’s proposal  ·  5.4 Reading referee reports and constructing a resubmission strategy

Deliverable. An implementation plan: a full structured review of a peer’s proposal plus a revision plan responding to the reviews received on your own.

Module 6 — Capstone: A Complete Funding Proposal

The capstone is a submission-ready proposal for a named real scheme, complying with that scheme’s actual page limits and required components. Compliance failures are penalised as a real funder would penalise them.

Lessons. 6.1 Finalising the case for support  ·  6.2 Assembling all required components  ·  6.3 Compliance checking against the call  ·  6.4 Preparing for submission and for rejection

Deliverable. A submission-ready funding proposal for one named real scheme, with case for support, budget, justification, data management plan and impact statement, plus a 500-word reflection on scheme fit.

Assessment

Pass mark 70 per cent. All assessed components must be attempted. Every mark in this course is issued by a human assessor; no assessment outcome is generated automatically.

Rubric criteria

Each assessed artefact is marked against four criteria at four levels (distinction, pass with merit, pass, fail).

1. Argumentative force. Significance, approach and feasibility are established quickly and convincingly.
2. Funder alignment. The proposal answers the call actually issued, in the format and to the criteria specified.
3. Technical accuracy. Budget, data management and compliance components are correct and internally consistent.
4. Critical self-assessment. Weaknesses and risks are identified and addressed rather than concealed.

Reading list

Core. Aldridge, J. and Derrington, A. M. (2012) The Research Funding Toolkit: How to Plan and Write Successful Grant Applications. London: Sage.

• Licklider, M. M. (2012) Grant Seeking in Higher Education: Strategies and Tools for College Faculty. San Francisco: Jossey-Bass.
• Gerin, W., Kapelewski Kinkade, C. and Page, N. L. (2017) Writing the NIH Grant Proposal: A Step-by-Step Guide, 3rd edn. Thousand Oaks: Sage.
• Lee, C. J., Sugimoto, C. R., Zhang, G. and Cronin, B. (2013) ‘Bias in Peer Review’, Journal of the American Society for Information Science and Technology, 64(1), pp. 2–17.
• Bol, T., de Vaan, M. and van de Rijt, A. (2018) ‘The Matthew Effect in Science Funding’, Proceedings of the National Academy of Sciences, 115(19), pp. 4887–4890.
• Fang, F. C. and Casadevall, A. (2016) ‘Research Funding: The Case for a Modified Lottery’, mBio, 7(2), e00422-16.

Academic integrity and use of AI

Generative AI may be used in this course as a drafting, translation and critique aid, and its use must be disclosed. Every submission carries a short use-of-AI statement naming the tools used, the tasks they performed and the checks the learner applied to the output. Using AI to fabricate data, invent sources, impersonate an interview participant, or produce an artefact the learner cannot explain in a live viva is prohibited.

All citations are verified before submission; a reference that cannot be located by the assessor is treated as fabricated. Fabrication, plagiarism and undisclosed ghost-authorship result in failure of the component and referral to the academic integrity panel. Marks in this course are issued by human assessors, and any learner may be asked to defend a submission orally before a mark is confirmed.