Documenting Data (Metadata)

In order for your data to be used properly by you, your colleagues, and other researchers in the future, they must be documented.  Data documentation (also known as metadata) enables you understand your data in detail and will enable other researchers to find, use and properly cite your data.

It is critical to begin to document your data at the very beginning of your research project, even before data collection begins; doing so will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project.

Researchers can choose among various metadata standards, often tailored to a particular file format or discipline.  One such standard is DDI (the Data Documentation Initiative), designed to document numeric data files.  Another standard is DCMI (Dublin Core Metadata Initiative), a general standard used to describe a variety of formats (texts, images, etc.).  This is the standard used to describe content in the library’s institutional repository MARS (Mason Archival Repository Service).

Following are some general guidelines for aspects of your project and data that you should document, regardless of your discipline.  At minimum, store this documentation in a readme.txt file or the equivalent, together with the data. One can also reference a published article which may contain some of this information.  For further help in documenting your data, contact us.


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
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
Access information Where and how your data can be accessed by other researchers
Language Language(s) of the intellectual content of the resource, when applicable
Dates Key dates associated with the data, including: project start and end date; release date; time period covered by the data; and other dates associated with the data lifespan, e.g., maintenance cycle, update schedule
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 one might include in a lab notebook
Data processing Along the way, record any information on how the data has been altered or processed
Sources Citations to material for data derived from other sources, including details of where the source data is held and how it was accessed
List of file names List of all data files associated with the project, with their names and file extensions (e.g. ‘NWPalaceTR.WRL’, ‘stone.mov’)
File Formats Format(s) of the data, e.g. FITS, SPSS, HTML, JPEG, and any software required to read the data
File structure Organization of the data file(s) and the layout of the variables, when applicable
Variable list List of variables in the data files, when applicable
Code lists Explanation of codes or abbreviations used in either the file names or the variables in the data files (e.g. ’999 indicates a missing value in the data’)
Versions Date/time stamp for each file, and use a separate ID for each version
Checksums To test if your file has changed over time

Below is a list of selected metadata standards:

General

Dublin Core — Dublin Core Metadata Initiative (DCMI), describes networked resources.  DCMI is the standard used to describe records in the Mason Archival Repository Service (MARS).

Geosciences

Federal Geographic Data Committee (FGDC) — used to document geospatial data and resources.

Life Sciences

Darwin Core — biodiversity information

Ecological Metadata Language (EML) — metadata specification for the ecology discipline.

Social Sciences & Humanities

DDI (Data Documentation Initiative) — social and behavioral sciences.

Text Encoding Initiative — An encoding standard for textual documents which specifies encoding methods for machine-readable texts, chiefly in the humanities, social sciences and linguistics.

 

Source: MIT Libraries, Documentation and Metadata

Featured Resource

Create, Review and Share Data Management Plans

  • Meet funding agency requirements.
  • Create ready-to-use data management plans for specific funding agencies.
  • Connect to DMPTool. Click Get Started and choose George Mason University from Select Your Institution.
  • At the log in screen, log in with your Mason e-mail username and password.

Want to learn more?