Skip to Main Content

Data Management: Documentation

Resources and strategies for managing research data

Documenting Your Data

Improving Your Documentation

This page describes some of your many options for documentation. Pick the documentation structures that work best for you and your data.

Take Better Notes

Improving your documentation often means improving your note taking. Good notes are:

  • Clear and concise
  • Legible if handwritten
  • Understandable to someone with similar training
  • Include all relevant information (eg. methods, thoughts about current project, ideas for new research, etc.) – opt to record more information, not less

Try Templates

Templates add structure to handwritten notes. This ensures you record all of the necessary details and can help you search through your notes later.

To create a template, come up with a list of details to record every time you acquire a particular type of data. Use this list as a reference or a worksheet. See this post for further information.

Use README.txt Files

README.txt's document digital files and add context whenever clarity is required. They are useful for:

  • Providing a general project overview in the main project folder
  • Describing the organization or naming conventions for files in a folder
  • Providing clarity for a group of files
  • Detailing one particular file's contents

See this post for more information on README.txt files.

Adopt Data Dictionaries

Data dictionaries are particularly useful for spreadsheet data. Data dictionaries describe:

  • Variable names
  • Variable meanings
  • Null values
  • Variable codings
  • Relationships between variables in a dataset
  • Anything unusual about the data

Use a data dictionary when you share data, when you expect to reuse the data, or have a particularly large and complex dataset.

Click here to learn more about on data dictionaries or watch this video on data dictionaries.

Keep Methods

Methods are important documentation that you need to remember to keep with your data. Methods include:

  • Protocols
  • Code
  • Survey
  • Codebook
  • Data dictionary
  • Anything that lets someone reproduce your results

Leverage Metadata

Metadata, or highlight structured computable documentation, is useful for:

  • Managing a large amount of documentation
  • Searching through or analyzing large amounts of documentation
  • Enabling information sharing for large collaborations
  • Archiving or sharing your data in a repository

If you need a metadata schema for your discipline, refer to this list from the UK's Digital Curation Center.

More Information

The Data Services librarian position is currently vacant. You may direct your questions to the Scholarly Communication team at 

The content of this guide is available under a CC-BY license with attribution to UWM Libraries.