Documenting your data

How do I document my data?

Documentation can take many forms. It can be written in free text, such as a readme file, or the metadata can be captured in a structured, machine readable file, encoded using an xml format.

Structured, discipline specific metadata is preferable, but if no standard exists, writing “readme” style files are the most simple way of recording metadata.

Readme files

A readme file provides information about a data file. It allows yourself and others to understand and reuse the data at a later date.

Best practices:

Follow the Cornell guide to writing "readme" files.

• Start writing the readme files at the beginning of the research project.
• Record the information in a text file (.txt)
• Use a template to help guide you, but tailor it to the needs of the project and kind of data that is being documented. Template examples:
  • Cornell
  • Oregon State
• Update the file as the research progresses.
• When the research is complete and ready to be shared, deposit the readme file alongside the data in a repository.

Data dictionaries & codebooks

Data dictionaries and codebooks provide variable-level metadata. These two types of documents may provide overlapping information.

• Data dictionaries: describe the names, definitions, and attributes of the data elements in a file. Find out more:
  • How to make a data dictionary (OSF)
  • Describing your data with data dictionaries (Smithsonian Libraries)
  • USGS Data Managament guide on data dictionaries
• Codebooks: used by survey researchers to provide information about the data from a survey instrument. Find out more.

Lab notebooks

Lab notebooks (print or online) are also a great way to document your research. They include methodology, results, calculations, etc. They are helpful for publishing, sharing, or reproducing your research.

• Information on lab notebook best practices
• Information on choosing an electronic lab notebook:
  • Harvard
  • Cambridge

Metadata standards

Find out if your discipline uses a metadata standard to describe data. In fact, specific disciplinary data repositories may require a formal standard. These metadata files are often saved in a machine readable format, such as xml. There are tools that can help with the creation of these metadata files. See the Tools section for more information.

To find an appropriate metadata standard for your discipline, consult the following resources:

• Disciplinary metadata guide (Digital Curation Center)
• Open directory of metadata standards (Research Data Alliance)
• Metadata standards catalog (Research Data Alliance)