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To assist users in interpreting the data for potential replication or reuse you should add information that helps tell others exactly how you created and used your datasets. For example tabular data can still be hard to read if someone doesn't know what your column headers or variable names mean. Projects are varied and you should not assume someone can just figure out what you did. A readme file, code sheet, or data dictionary can help mitigate confusion and help your data align with our charge to make research data findable, accessible, discoverable, and reusable (FAIR). These elements can be uploaded directly into your dataset or added within the metadata.

For more information see:

• How to make a Data Dictionary (Center for Open Science)
• Guide to writing "readme" style metadata (Cornell University)
• ASU Library Data Management Tutorials