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

A README file

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)

Don’ts:

• 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

Do’s:

• 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