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Data Documentation / Metadata

Learn best practices for describing your data to support sharing and reusability. Includes discipline specific metadata standards and best practices.

about data documentation/metadata

About Metadata and Documentation

Creating and maintaining complete and accurate documentation and metadata is an essential part of data management. Maintaining good metadata ensures discoverability and longevity of your data. 

  • Metadata is "data about data" and describes the key attributes of each data element or collection of elements. Using a standardized metadata schema improves interoperability. Also, it's typically a requirement for depositing data in a repository or when publishing in a research journal.
  • Documentation is “all about the use” and makes reference to data in the context of their use. This includes how the data was collected, structure and organization of data files, data manipulations through data analysis from raw data etc.

How to use this guide

Browse metadata and documentation resources by discipline using the the menu to left. For each schema you will find:

  • Description of the schema
  • Links to documentation for the schema
  • Additional Learning Resources, which provide articles, videos and other materials to assist you in using the schema.

Metadata explains your data so it can be found, understood, shared and archived.  All metadata standards provide standardized structured information explaining:

  • Purpose
  • Origin
  • Time References
  • Geographic Locations
  • Creator
  • Access Conditions and terms of use of data

The graphic below demonstrates the wide variety of discipline specific metadata standards. Use the tabs to the left for information on specific metadata standards.



Documentation provides information about the project and the methods used to create, collect, process, and analyze the data. Documentation is typically presented in a readme file and provides context for the data and increases the understandability of your data.

Here are a few things to consider when creating documentation.

Project Level Documentation

  • What's important to document?
  • Context of data collection
  • Data collection methodology
  • Structure and organization of data files
  • Data validation and quality assurance
  • Data manipulations through data analysis from raw data
  • Data confidentiality, access and use conditions

Data Level Documentation

  • Variable names and descriptions
  • Definition of codes and classification schemes
  • Codes of, and reasons for, missing values
  • Definitions of specialty terminology and acronyms
  • Algorithms used to transform data
  • File format and software used

Don't know where to start? Try our Documentation Template

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