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Research Data Management: Managing research data

Resources for learning about best practices in research data management across a variety of disciplines.

Additional resources on data management & curation

Describing data

Sharing data (with others or within a lab over time) is impossible without proper data documentation. "Metadata" is data about data. It's structured information that describes content and makes it easier to find or use. A metadata record can be embedded in data or stored separately. Any data file in any format can have metadata fields. In social science, this record is called the "codebook" or "data dictionary."

There are many metadata standards and which one is right for your data will depend on the type, scale, and discipline of your research project.

Some examples of metadata standards are:

For more examples, see the Research Data Alliance Metadata Directory.


If your field doesn't have a metadata standard (it may not be listed above) or if you just need a simpler system to keep track of data within your own lab, consider that there are three main types of metadata addressed by most standards:

  • descriptive: describes the resource for identification and discovery
  • structural: how objects are related or put together
  • administrative: creation date, file type, rights management

Also consider this advice from the UK Data Archive [pdf]:

Good data documentation includes information on:

  • the context of data collection: project history, aim, objectives and hypotheses
  • data collection methods: sampling, data collection process, instruments used, hardware and software used, scale and resolution, temporal and geographic coverage and secondary data sources used
  • dataset structure of data files, study cases, relationships between files
  • data validation, checking, proofing, cleaning and quality assurance procedures carried out
  • changes made to data over time since their original creation and identification of different versions of data files
  • information on access and use conditions or data confidentiality

 At the data-level, documentation may include:

  • names, labels and descriptions for variables, records and their values
  • explanation or definition of codes and classification schemes used
  • definitions of specialist terminology or acronyms used
  • codes of, and reasons for, missing values
  • derived data created after collection, with code, algorithm or command file
  • weighting and grossing variables created
  • data listing of annotations for cases, individuals or items

Organizations related to data management and preservation