Documenting your data
Once you are confident that your data is accurate you are ready to document it. The more complete your documentation, the more useful and understandable your data will be when it comes time for you or others to use it.
The basic information about the entire dataset can be accessed by selecting the Settings tab or by clicking Edit from your dataset main page:
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Within the settings, you can:
Add a short, meaningful description to summarize your data
Add tags (individually or comma-separated)
Select the license type for your dataset (see the article on license types for more details)
Edit the visibility level
Manage auto-sync functions: if files are connected through a virtual connection or via a URL, choose to automatically refresh hourly, daily, or weekly
Select how to import compressed files
Delete the dataset
In addition to the metadata under the settings tab there are two more ways to document your dataset: the summary and the data dictionary. Both are accessed from the information box at the top of the overview page:
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Use the Summary section to tell your data's story:
Where did the data come from? Cite and link to your sources or include details for a 'citation request'. Not only does this give "credit where credit is due", it helps other members evaluate the data's suitability for their needs.
If you think a particular piece of context will be useful to other members in understanding the data, add it.
The best summaries cover the “who, what, where, when, why, and how” of the data.
Make it visually friendly with Markdown styling. It's easy to learn and goes a long way.
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Back on the dataset main page, click View next to the data dictionary to further document the files and columns within a dataset. You can add descriptions and labels to the individual files, change column types, and add column descriptions:
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Select + Add a description under a filename to add a description and labels to the file:
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Within the above file view, select Column details to add detailed descriptions for each of the columns, particularly if your column names are obscure. You can also specify the data type:
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