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Research Data Management: Practical Tools & Examples

Introduction

Practical planning is the key to a strong Data Management Plan (DMP). This section provides hands-on tools and examples to help you structure, document, and organise your research data so it remains secure, reusable, and aligned with FAIR principles.

Examples

1. File Naming Conventions

  • Why it matters: Clear file names make it easier to identify versions, avoid confusion, and support collaboration.

  • Best practices:

    • Be consistent.

    • Avoid spaces/special characters (use underscores or dashes).

    • Include a date in YYYYMMDD format.

    • Add a version number.

  • Examples:

    • 20250921_ProjectName_V1.0.xlsx

    • 20250921_InterviewTranscript_Participant1_V1.0.docx

    • Dataset_Phase1_V2.1.csv

2. Version Control

  • Why it matters: Keeps track of data changes and reduces the risk of overwriting or losing work.

  • Strategy:

    • v1.0 Raw data – unaltered from collection.

    • v1.1 Cleaned data – corrections applied, errors checked.

    • v2.0 Analysis-ready – formatted for analysis.

  • Tips:

    • Maintain a changelog.txt file.

    • Document who made changes and when.

    • Use version control software (Git/GitHub) for code.

3. Documentation (README Files)

  • Why it matters: Good documentation makes your dataset understandable to others — and to you in the future.

  • What to include in a README.txt:

    • Project name & description.

    • Dataset overview (files included, format, size).

    • Variable descriptions (column names, units, codes).

    • Methods (data collection, instruments, calibration).

    • Data processing steps.

    • Contact information.

  • Downloadable template: Provide a sample README text file researchers can adapt.

4. Metadata Standards

  • Why it matters: Metadata is the “data about data” that ensures discoverability and reuse.

  • Examples:

    • Generic: Dublin Core, DataCite.

    • Social sciences: DDI (Data Documentation Initiative).

    • Biosciences: MIAME (for microarray experiments).

  • Tip: Always deposit metadata in repositories even if data can’t be openly shared.

5. Checklist for Researchers

Before moving forward, ask:
✅ Do your files follow a consistent naming convention?
✅ Do you have a README or data dictionary?
✅ Are versions documented clearly?
✅ Have you selected a metadata standard appropriate to your field?