Prepare Data for Archiving

Get Your Data Archive-Ready

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This is an ideal time to get your data ready for archiving. Preparing it now helps ensure your dataset is complete, well-documented, and ready for upload by the Completion milestone.

Ideally, much of this work—such as organising folders, using clear filenames, and saving key documentation—will have been happening throughout your project as part of your research data management plan. (If you attended a RTN workshop we covered these principles)

If not, don’t worry—it’s not too late to get your data in shape now.

You will need to archive:

• The minimum dataset needed to support your findings or future research
• In non-proprietary, durable formats where possible (e.g. CSV, TXT, PDF/A)
• With supporting documentation (e.g. codebooks, R scripts, interview guides) to ensure your data can be understood and interpreted correctly.

Archive-Ready Data: Best Practice Checklist

✅ Export to durable formats:
Convert files to formats that support long-term access (e.g. PDF for documents, CSV for tabular data).

✅ Review your folder structures and file names:
Use consistent, descriptive names to help others (and your future self) understand what’s in the files without having to open them. Add a README file if needed. Avoid deeply nested folders (more details on this in the Archiving section) and don't use special characters such as ~ ! @ # $ % ^ & * ( ) ` ; < > ? , [ ] { } ' ‘ | in file names, as they can interfere with data transfer and archiving processes.

✅ Organise your data according to sensitivity:
Separate highly sensitive materials (e.g. identifiable interviews) from less sensitive files (e.g. de-identified survey data), and from non-sensitive documentation (e.g. interview guides, codebooks).
This helps you decide what can be published later, and makes it easier to manage archiving—some sensitive files may require special handling or may not be archived at all.

✅ Keep documentation with your data:
Organise and save supporting files (e.g., codebooks, scripts) with your dataset so it’s ready for archiving and others (and your future self) can interpret it correctly.

🔗 Additional Resources:

The website includes practical and detailed advice on:

• Documentation
• File Names
• File Formats
• Folder Structures
• Version Control

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