## Steps to archiving

Right now archiving might seem a long time away, therefore you can always keep note of this section of the course and refer back when you come to the later stages of your research. Please refer to the Faculty Archiving Guidelines for a comprehensive understanding of the requirements and their rationale.

The following steps are a reiteration of the good research data management practices we've been exploring throughout this course. By incorporating these steps into your workflow during the research process, rather than leaving them until the end, you will reduce your overall workload and ensure more accurate and consistent documentation. Many of these practices are already defined in your data management plan, which is why developing that plan early on is such an important part of effective data management.

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### Steps to archiving your data:

1. Organize your data
   - Clean your files: remove duplicates, temporary files, or test versions.
   - Use clear file names: follow a consistent naming convention.
   - Structure your folders logically: group files by data type, collection date, or experiment phase.
2. Document your data package (metadata & ReadMe)
   - Create a README file: describe what each file contains, how it was created, and any necessary context for reuse or interpretation.
   - Include metadata: use a formal metadata schema if possible (e.g. Dublin Core, DataCite).
3. Check for sensitive or confidential data
   - Anonymize human subjects if necessary and if possible.
   - Redact or restrict sensitive elements that can't be shared.
   - Review consent forms and consider what data sharing is possible (document this also).
4. Ensure data integrity and reusability:
   - Use open, non-proprietary file formats where possible (e.g. .csv instead of .xlsx).
   - Test your data files to make sure they open and function correctly.
   - Include any software/ code or tools used to process the data, along with documentation.
5. Package the data
   - Bundle:
     - Research data,
     - Code/ scripts,
     - Documentations files (README, metadata, methodology, licenses)
   - Compress the files logically (e.g. ZIP the entire package or subfolders by type).
6. Select an appropriate repository
   - Choose a trusted archvie that aligns with your research area, such as:
     - Disciplinary repositories (e.g. GenBank, ICPSR, Dryad)
     - Institutional repository (Yoda)
     - General-purpose repositories (OSF, DataverseNL, Zenoda)
   - If your dataset contains sensitive/ personal data, Yoda is the recommended option.
7. Assign a License and DOI
   - Choose a clear license (e.g. Creative Commons/ VU custom license) that explains how others can use the data.
   - Use a repository which will assign a DOI (Digital Object Identifier) for citation and persistent access.
8. Verify and submit
   - Double-check all files and metadata for accuracy and completeness.
   - Submit your data package and confirm that it's discoverable and accessible.
9. Cite and promote your data
   - Include the DOI in your publications.
   - Register your dataset in Pure if using a VU external repository.
   - Promote your dataset in presentations or on your professional profiles (eg. ORCID, LinkedIn).