Data documentation

All data sets published on DataShare must be accompanied by documentation. At minimum, your data documentation should address:

• Where and how data was obtained/generated/collected/compiled.
  • include citations to other data, tools, software, etc.
• How files are organized and named, and what they contain.
• How the data is structured and defined variables.
• What was done to the data and files (if not sharing raw data).
  • i.e. steps or code used to clean or standardize data

Readme files

The simplest and most flexible way to document your data is through a README file - a text document that acts as a 'user manual' for your dataset. README files are most often found as plain text (.txt, .md) or PDF files. It's possible to insert a codebook or data dictionary into a README but it may not always be practical.

Readme templates

Data dictionaries and codebooks

The terms codebook and data dictionary are often interchangeable. In general codebooks are more formal in content and format, often only addressing data variables, while data dictionaries may incorporate elements of a readme file and list sources, methodologies, etc. 

Data dictionaries and codebooks work best for tabular data (i.e. spreadsheets and tables). They explain the attributes of the data elements that are not within the data itself, such as what each column of the data represents and how variables are defined. For example, a column titled "date" does not tell you why the date matters, only that it is one.

• DataShare codebook template (Google Sheets)
  • Row 2 contains instructions; rows 3-5 contain examples.
• Example codebook within a readme file
  • A very simple data set and codebook about superhero movies.
• What is a codebook? - ICPSR 
  • While aimed at social science research, especially survey data, this page provides examples from a wide variety of sources.

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