When you have a conversation, context drives understanding. The same goes for data: data without context is either unclear or unintelligible, as you won’t know how it came to exist, where, and why.
The context of your data is called metadata , which means “data describing other data”. When you provide metadata along with your research, others can reuse it. Your data becomes more easily discoverable and the potential for citations increases. Note that good metadata can also help its original creator should they wish to return to it in the future.
Ideally, you should prepare metadata at study level and at data level (for the stored datasets). The former should include information on:
- The aims of your project
- The methods used to collect data
- The contents of your data
- The folder structure and file naming conventions
- The data processing techniques used
- The modifications made to the initial data throughout the project
- Data validation and other quality assurance processes
- Roles and responsibilities within the project
- Details on identifiers, licencing, and sensitive information.
Metadata at the data level is a completely different story, as disciplinary differences and different standards exist (more on this in the section below).
Creating metadata for your research results
In practice, creating metadata means filling in some fields with information about your work. However, finding out which fields you should use and when can be a daunting task. The two options to create metadata for your work are using templates or using standards. Templates are most often simplified versions of standards and you will just have to look at what other people have done and follow their example.
Standards are more complicated and require technical understanding, but maximise reach and discoverability. If you wish to use a metadata standard, this page developed by the Research Data Alliance can help you pick one that is appropriate to your field. Another good starting point is the FAIRsharing website . If in doubt, you can use generic schemas such as Dublin Core .
As a side note, mainstream software for quantitative and qualitative analyses support the addition of metadata. This includes, SPSS, MS Access, or MS Excel for quantitative studies or NVivo for qualitative ones.