Research Data Management
Metadata describes and documents research data. Metadata will make your datasets searchable in an archive or repository, easily located from a citation, and easily understood by people who might want to use your data.
There are many metadata elements that you should consider when describing and documenting your research data, including:
- Creator (Principal Investigators)
- Date Created (also versions)
- Unique Identifier (ex. doi)
- Description of the specific data resource
- Coverage of the data (spatial or temporal)
- Publishing organization
- Type of resource
- Rights (ethics/legal/etc)
- Funding or Granting Agency
Different disciplines have different metadata standards. A metadata standard is a standardized way of describing data. These are often standardized for a particular discipline or data type. The Digital Curation Centre in the UK has a list of some common disciplinary metadata standards. Contact your librarian if you are unsure how to apply metadata to your data.
In addition to the elements above, it is usually advisable to provide a readme file with detailed instructions on how others can use your data. You can use this template to develop a good-quality readme file. Cornell University offers further guidance on writing Readme files.
What is a data dictionary, and how can I create one?
A data dictionary defines what your variable names and values mean. It is crucial to creating a shared understanding for all members of the research team that remains consistent over time, as well as providing guidance to others on how to use your data. The Open Science Framework offers a guide to making a data dictionary.
- Last Updated: Feb 2, 2021 10:58 AM
- URL: https://library.ucalgary.ca/guides/researchdatamanagement
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