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Data Management: Documentation & Metadata

Connecting the UR Community to data management resources.


What is Metadata?

Metadata is structured information describing the characteristics of something; for example, the dates associated with a dataset or the title and author of a book. 

  • Metadata supports discovery, re-use and long-term preservation of resources. In order for your data to be used properly by you, your colleagues, and other researchers in the future, they must be documented.
  • Planning what metadata will be captured and how this data will be structured should occur at the beginning of a research project, before data collection begins.

Disciplinary Metadata Standards

General & Disciplinary Metadata Standards

Metadata standards provide specific data fields or elements to be used in describing data for a particular use. Some research fields have predefined metadata standards, such as those listed below. Wikipedia also describes and links to other disciplinary metadata standards

All disciplines

  • Dublin Core: A common standard vocabulary of fifteen properties for use in resource description. Because its elements are broad and generic, Dublin Core is usable for describing a wide range of resources.


  • Access to Biological Collection Data (TDWG): standard for the access to and exchange of data about specimens and observations (a.k.a. primary biodiversity data). 
  • Darwin Core: a standard that incorporates information about modern biological specimens, their spatiotemporal occurrence, and their supporting evidence housed in collections (physical or digital). 
  • Ecological Metadata Language: a standard for ecological data.

Social Sciences

  • Data Documentation Initiative: a standard to describe data that result from observational methods in the social, behavioral, economic, and health sciences. 

Arts and Humanities

Important things to do while you collect or create your data

Important things to do while you collect or create your data

  • Make a note of all file names and formats associated with the project, how the data is organized, how the data was generated (including any equipment or software used), and information about how the data has been altered or processed.

  • Include an explanation of codes, abbreviations, or variables used in the data or in the file naming structure.

  • Keep notes about where you got the data so that you and others can find it.

Courtesy MIT Libraries

Things to document about your data

Things to document about your data

  • Title
    Name of the dataset or research project that produced it
  • Creator
    Names and addresses of the organization or people who created the data
  • Identifier
    Number used to identify the data, even if it is just an internal project reference number
  • Dates
    Key dates associated with the data, including project start and end date, data modification data release date, and time period covered by the data
  • Subject
    Keywords or phrases describing the subject or content of the data
  • Funders
    Organizations or agencies who funded the research
  • Rights
    Any known intellectual property rights held for the data
  • Language
    Language(s) of the intellectual content of the resource, when applicable
  • Location
    Where the data relates to a physical location, record information about its spatial coverage
  • Methodology
    How the data was generated, including equipment or software used, experimental protocol, other things you might include in a lab notebook

Courtesy MIT Libraries

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.