Data documentation will ensure that your data will be understood and interpreted by any user. It will explain how your data was created, what the context is for the data, structure of the data and its contents, and any manipulations that have been done to the data. Also see: Guide to Writing a "readme" File.
What's important to document?
Data-level documentation
Properly describing and documenting data allows users (yourself included) to understand and track important details of the work. In addition to describing data, having metadata about the data also facilitates search and retrieval of the data when deposited in a data repository. In a lab setting, much of the content used to describe data is initially collected in a notebook; metadata is a more formal, sharable expression of this information. Where no appropriate formal metadata standard exists, writing “readme” style metadata is an appropriate strategy.
Metadata is information about data, and describes basic characteristics, such as:
Metadata makes it easier to identify and reuse data at a later date.
| Title | Name of the project or collection of datasets |
| Creator | Names and institutions of the people who created the data |
| Date | Key dates associated with the data, such as dates covered by the data or date of creation |
| Description | Description of the resource |
| Keywords or subjects | Keywords or subjects describing the content of the data>/td> |
| Identifier | Unique number or alphanumeric string used to identify the data |
| Coverage (if applicable) | Geographic coverage |
| Language | Language of the resource |
| Publisher | Entity responsible for making the dataset available |
| Funding Agencies | Organization or agency who funded the research |
| Access restrictions | Where and how your data can be accessed by other researchers |
| Copyright | Copyright date and type |
| Format | What format your data is in |
Well-structured metadata supports the long-term discovery and preservation of research data, but allows for the aggregation and simultaneous searching of research data from tens or hundreds or thousands of researchers. This is why domain-specific repositories typically require highly structured metadata with your data submissions: it enables highly granular searches on their aggregated content. This in turn makes your data easier to find.
Metadata can take many different forms, from free text to standardized, structured, machine-readable, extensible content. Specific disciplines, repositories or data centers may guide or even dictate the content and format of metadata, possibly using a formal standard. Because creation of standardized metadata can be difficult and time consuming, another consideration when selecting a standard is the availability of tools that can help generate the metadata (e.g. Morpho allows for easy creation of EML, Nesstar for DDI data, etc.).
The Digital Curation Center provides a catalog of common metadata standards, organized by discipline: http://www.dcc.ac.uk/resources/metadata-standards.
Some specific examples of metadata standards, both general and domain specific are:
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