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Metadata for Researchers


Simply put, metadata are data that describe other data.  In practical terms, metadata are bits of information that allow people and/or machines to understand the meaning of the data being presented, such as the content typically found in the header row of a table or spreadsheet, or along the axes of a graph.


Without metadata, data are difficult or even impossible to interpret.  For example, if the header were missing from the table below, would it be clear that 2009-07-18 is a purchase date?  It could just as easily be a publication date, the date the item was added, or some other code.




Confusing or incomprehensible data can't be reviewed or used by others - and may not be of long-term value to you either - so aim to provide unambiguous and complete metadata for all your data-sets.

  • Consistency:  Everyone on your project team should agree - before data collection begins - to record metadata for all the same fields and to the same level of detail - while using the same metadata standard, coding system(s), date format, software/file type(s) etc.

Metadata vs. Documentation

Metadata vs. "Documentation"


Metadata is highly-structured information describing your data  - from individual variables to entire data-sets. Metadata are generally entered into pre-determined fields, e.g., Name, Program, Date, as metadata need to be both human-readable and machine-readable - which is more difficult when metadata are entered as unstructured free-text.

  • Metadata standards exist for most disciplines, to provide the structures and elements that make the most sense for the discipline.  To learn more see the Metadata Standards page in this guide.


Documentation: covers a wide range of contextual information that makes your data comprehensible - both to yourself and others - at the project, file/database and variable levels, such as codebooks, questionnaires, file version history logs etc.  To learn more see the Accompanying Documentation page in this guide.