Data Documentation and Metadata

Data Documentation

Data documentation will ensure that your data will be understood and interpreted by any user.  It will explain how your data was created, what the context is for the data, structure of the data and its contents, and any manipulations that have been done to the data.

What's important to document? 
  • Context of data collection
  • Data collection methodology
  • Structure and organization of data files
  • Data validation and quality assurance
  • Data manipulations through data analysis from raw data
  • Data confidentiality, access and use conditions
Data-level documentation
  • Variable names and descriptions
  • Definition of codes and classification schemes
  • Codes of, and reasons for, missing values
  • Definitions of specialty terminology and acronyms
  • Algorithms used to transform data
  • File format and software used

Metadata

Metadata explains the origin, purpose, time, geographic location, creator, access, and terms of use of the data. Structured metadata follows a standard and is usually stored in a specific format. Information in the structured metadata is used for retrieving and indexing data in a repository or archives; and for the citation. Metadata can be harvested by search engines for discoverability. 

There are a variety of metadata standards, usually for a particular file format or discipline.  Some examples include the following:

Consult these directories for more comprehensive lists (and tools) of disciplinary metadata.

The UA Library can help you select the most appropriate metadata standard to use.

When creating metadata, a best practice is to use controlled vocabulary, standard terminology for your discipline.  Using an accepted standard, controlled vocabulary or an authority list will help in the retrieving and indexing of your data. 

Consider keeping metadata records in a spreadsheet, CSV file or tab-delimited file.  Additional information needed to interpret the metadata, such as explanations of variable, codes, acronyms or abbreviations, or algorithms used, should be included as accompanying documentation.

Suggested Metadata Elements

In the absence of a standard in your discipline, the University of Arizona Libraries suggests the following metadata elements. In their simplest form, these can be included as part of a readme file. 

ElementDescription
TitleName of the project or collection of datasets
CreatorNames and institutions of the people who created the data
DateKey dates associated with the data, such as dates covered by the data or date of creation
DescriptionDescription of the resource
Keywords or SubjectsKeywords or subjects describing the content of the data
IdentifierUnique number or alphanumeric string used to identify the data like a DOI. Many repositories provide DOIs for deposited datasets.
Coverage (if applicable)Geographic coverage
LanguageLanguage of the resource
PublisherEntity responsible for making the dataset available
Funding AgenciesOrganization or agency who funded the research
Access restrictionsWhere and how your data can be accessed by other researchers
LicenseE.g., CC0, CC By 4.0, MIT, etc. See the ReDATA license matrix for help selecting a license.
FormatWhat format your data is in
Example Readme Files 
  • UA Research Data Repository Readme: This readme is used for all datasets deposited into ReDATA. The format is plain-text but can be rendered as Markdown. Template, Example.
  • Comprehensive readme: This readme is part of a survey dataset. The readme is exemplary in that it documents the data analysis process and explains each file and folder. Example. 
  • Additional templates/examples: