Research Data Management 101

RDM101

Research Data Management

A comprehensive guide to Ethical Sharing, Archiving, and Reuse. Understand how to treat your data as a first-class research output.

25%

Citation Advantage

Studies suggest papers with openly available data receive up to 25% more citations than those without.

10 Years

Retention Standard

Most funding bodies require data to be securely archived for at least 10 years post-publication.

£000s

Lost Efficiency

Poor data management leads to significant time loss in cleaning and reorganizing data for reuse.

Why RDM Matters

Research Data Management (RDM) covers how you organize, structure, store, and care for the information used or generated during a research project. Good RDM is not just about compliance; it ensures your work is reproducible, efficient, and impactful.

The Research Data Lifecycle

Data management isn't a one-time task; it's a continuous cycle. Click on the stages below to explore the critical actions, risks, and best practices at each step of your project.

File Format Sustainability Analysis

Comparison of common formats for long-term archiving utility.

The FAIR Principles

The FAIR principles (Findable, Accessible, Interoperable, Reusable) are the gold standard for data management. Use the checklist below to assess how "FAIR" your current data practices are.

Self-Assessment Checklist

Findable

Assigned a persistent identifier (e.g., DOI). Described with rich metadata.

Accessible

Retrievable by their identifier using a standard protocol. Metadata remains accessible even if data is no longer available.

Interoperable

Uses formal, accessible, shared, and broadly applicable language. Uses vocabularies that follow FAIR principles.

Reusable

Released with a clear and accessible data usage license. Meets domain-relevant community standards.

Your FAIR Score

Check the boxes to analyze your data readiness.

Ethics, Licensing & Reuse

Sharing data ethically requires balancing openness with privacy. Choosing the right license ensures you get credit while allowing others to build upon your work.

Creative Commons License Selector

Answer two questions to find the appropriate license for your dataset.

1. Allow commercial uses of your work?

2. Allow adaptations of your work?

Recommended License:

CC BY 4.0

This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.

The Anonymization Spectrum

Direct identifiers must be removed, but indirect identifiers (quasi-identifiers) can still reveal identity when combined. Hover over the table rows to see how data transforms.

Technique	Before	After
Removal	John Doe	[DELETED]
Generalization	Age: 24	Age: 20-29
Pseudonymization	ID: 45912	ID: KEY_88A1
Suppression	Rare Disease X	*

Note: Always balance Data Utility (how useful the data is for analysis) against Disclosure Risk.