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.
Steps to archiving your data:
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.
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).