HE-R04 — Research Data Management

Course Code: HE-R04  |  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

Research data is the most valuable and least well managed asset most universities hold. It sits on personal laptops, in departmental drives, in retired postdoctoral researchers’ email accounts and in file formats no longer readable, and it is routinely lost when the person who created it leaves. Funders have responded by making data management planning a condition of award, which has made data stewardship a compliance requirement before most institutions had made it a competence.

The intellectual framework of the course is the FAIR principles — findable, accessible, interoperable, reusable — published by Wilkinson and colleagues in 2016 and now embedded in funder policy worldwide. Learners will apply them concretely rather than invoke them, which means confronting the fact that accessible does not mean open, that interoperability requires disciplinary standards many fields do not have, and that reuse requires documentation most researchers never write.

The course gives equal weight to the CARE principles for indigenous data governance, which insist that collective benefit, authority to control, responsibility and ethics sit alongside technical openness. Where FAIR asks whether data can be reused, CARE asks whether it should be, and by whom. Learners working with community or sensitive data are expected to reconcile the two frameworks explicitly.

Learning outcomes

On completion, a successful learner will be able to:

1. Apply the FAIR principles to a specific dataset and identify concretely where a current practice fails each principle.
2. Write a funder-compliant data management plan that is operationally realistic rather than aspirational.
3. Design a data organisation, naming, versioning and documentation scheme that a successor could use unaided.
4. Select appropriate storage, backup and preservation arrangements proportionate to the value and sensitivity of the data.
5. Apply legal and ethical requirements including data protection, consent for reuse, anonymisation and licensing.
6. Evaluate repositories and deposit data with adequate metadata and a persistent identifier.
7. Apply the CARE principles to research involving indigenous or community-held data and reconcile them with openness requirements.

Who this course is for

Doctoral researchers, principal investigators, research data managers, librarians with data stewardship responsibilities, research office staff, and IT staff supporting research infrastructure.

Prerequisites

No formal prerequisites. Learners should bring a real or planned dataset to work on throughout the course. No programming is required, though those who code will find additional application.

Syllabus

Module 1 — Data as a Research Asset

Data loss in universities is normal rather than exceptional, and the causes are organisational rather than technical. This module establishes the scale of the problem and the FAIR framework as a response.

Lessons. 1.1 The research data lifecycle from planning to preservation  ·  1.2 The FAIR principles examined individually and critically  ·  1.3 Why data is lost: turnover, formats, media and undocumented context  ·  1.4 Institutional roles and where responsibility actually sits

Deliverable. A diagnostic note assessing one existing dataset against each FAIR principle, with specific evidence of compliance or failure.

Module 2 — Planning and Documentation

A data management plan written to satisfy a funder and never consulted again is worthless. The module writes plans that function as working documents and builds the documentation that makes data intelligible later.

Lessons. 2.1 Funder requirements and the DCC data management plan checklist  ·  2.2 File organisation, naming conventions and version control  ·  2.3 README files, codebooks and data dictionaries  ·  2.4 Capturing provenance and analytical context

Deliverable. An analytical brief containing a complete data management plan for one project, written against a named funder’s actual requirements.

Module 3 — Storage, Security and Preservation

Storage decisions are usually made by default and discovered to be inadequate at the point of failure. This module makes them deliberately and proportionately to risk.

Lessons. 3.1 Active storage, backup strategy and the three-copy rule  ·  3.2 Security classification and controls for sensitive data  ·  3.3 File formats, obsolescence and preservation-ready formats  ·  3.4 Retention schedules, appraisal and defensible disposal

Deliverable. A design artefact: a storage, backup, security and retention plan for one dataset, with the risk assessment that justifies each choice.

Module 4 — Law, Ethics and Sharing

Most data can be shared in some form, and the common claim that ethics approval prohibits sharing is usually a failure of consent design rather than a legal barrier. The module works through the actual constraints.

Lessons. 4.1 Data protection law and lawful bases for research processing  ·  4.2 Consent designed to permit future sharing  ·  4.3 De-identification, disclosure risk and controlled access  ·  4.4 Licensing, ownership and third-party data restrictions

Deliverable. An evaluation report assessing the shareability of one dataset, specifying what can be shared, in what form and under what access conditions.

Module 5 — Repositories, Metadata and Indigenous Data Governance

Deposit is where data management becomes visible to others, and where the tension between openness and community authority becomes concrete. The module handles both the technical and the political dimensions.

Lessons. 5.1 Selecting a repository: disciplinary, institutional and generalist  ·  5.2 Metadata standards, persistent identifiers and citation of data  ·  5.3 The CARE principles for indigenous data governance  ·  5.4 Reconciling FAIR openness with CARE authority to control

Deliverable. An implementation plan for depositing one dataset, including repository selection, metadata record, access conditions and, where relevant, a community governance arrangement.

Module 6 — Capstone: A Complete Data Stewardship Package

The capstone produces everything required to hand a dataset to a stranger and have them use it correctly. It is assessed by whether a competent outsider could in fact do so.

Lessons. 6.1 Finalising the plan and documentation  ·  6.2 Preparing data for deposit  ·  6.3 Writing the metadata and access statement  ·  6.4 Testing intelligibility with an outside reader

Deliverable. A complete data stewardship package for one dataset: data management plan, documentation and codebook, storage and retention plan, deposit record and a 1,000-word reflective commentary.

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. Operational realism. The plan describes what will actually be done, with named responsibilities and resources.
2. Documentation quality. An independent researcher could understand and reuse the data from the documentation alone.
3. Legal and ethical accuracy. Data protection, consent and licensing positions are correct and specific.
4. Governance sensitivity. Community authority, sensitivity and disclosure risk are addressed where relevant rather than defaulted to open.

Reading list

Core. Wilkinson, M. D. et al. (2016) ‘The FAIR Guiding Principles for Scientific Data Management and Stewardship’, Scientific Data, 3, 160018.

• Corti, L., Van den Eynden, V., Bishop, L. and Woollard, M. (2019) Managing and Sharing Research Data: A Guide to Good Practice, 2nd edn. London: Sage.
• Briney, K. (2015) Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success. Exeter: Pelagic Publishing.
• Borgman, C. L. (2015) Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, MA: MIT Press.
• Carroll, S. R. et al. (2020) ‘The CARE Principles for Indigenous Data Governance’, Data Science Journal, 19(1), article 43.
• Digital Curation Centre (2013) Checklist for a Data Management Plan, version 4.0. Edinburgh: DCC.

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.