Data Documentation

Examine the dataset closely to understand what it is, how the files interrelate, and what information is needed to reuse.

  • Check for quality assurance and usability issues such as missing data, ambiguous headings, code execution failures, and data presentation concerns
  • Try to detect and extract any “hidden documentation” inherent to the data files that may facilitate reuse or expose unintended information
  • Determine if the documentation of the data is sufficient for a user with similar qualifications to the researcher’s to understand and reuse the data. If not, create additional documentation (e.g., a data dictionary)

Key Ethical Considerations

  • If working with human data, is this research done with and not on communities and populations involved?
    • Are there authoritative group representatives who should be contacted?
  • Are there labels or other descriptive indicators that could be applied to better represent or protect an identified group of people impacted by this dataset? (Example: TK labels)

Essential Tasks


Acceptable formats include txt, rtf, or mdl. For more information about READMEs, see Cornell University’s Guide to writing “readme” style metadata.

  • Include complete documentation about your dataset
    • Provenance Information
    • Licensing Information
    • Descriptions of Study Level and/or Data Level information:
      • Study-level data documentation:
        • Provenance of information gathered in this dataset
        • Methods used to acquire, organize, or normalize this data
        • Descriptions of any transformations that this data has undergone
      • Data-level documentation:
        • Contents of data files
        • Definitions of all variables, labels, descriptions, units of measurement used in data files
        • Defined labels or codes used to indicate missing data (ie no blank spaces, but what would indicate unknown or missing)
  • Include participant information and codebooks (if applicable)
  • Check the accessibility of all files
    • Each file can be opened without proprietary software
    • There are robust descriptions in plain text of data files and any images.

File and Folder Names

How are your files (and folders) named?

  • Are names consistent?
  • Are the names meaningful?
  • Have you documented each of the file (and folder) names?
  • Do the file and folder names reflect versioning? (i.e. updated files are named in a way that is findable without opening up the files themselves)


  • Use special characters in the file name
  • Leave blank spaces in the file name
  • Use different naming logics for each file or type of file


  • Use less than 25 characters per file name
  • Use ISO 8601 to format dates: YYYY-MM-DD
  • Use capitals or underscores instead of spaces or periods
  • Write down your naming convention