Describe your research data , Whakaahuia i tō raraunga rangahau

Find out how to describe and explain your data using README files, data dictionaries and metadata.

It’s important you create documentation to describe and explain your data. This will help you and others:

  • make sense of your data in the future
  • understand the processes you followed to collect, process and analyse it.

Watch our video that gives an overview of explanatory documentation

Descriptive and explanatory information is created at two levels:

  • the study level
  • the data level.

Study-level description

At the study level, your descriptive and explanatory information provides an overview of the following:

  • research context and design
  • data collection methods
  • data preparation
  • results or findings.

The Massey University Code of Responsible Research Conduct says that, at a minimum, researchers should keep detailed records describing the methods used and the results observed, as well as records of any approvals granted as part of the research process.

Massey University Code of Responsible Research Conduct

Data-level description

At the data level, your descriptive and explanatory information can be recorded in a structured document or embedded in data, for example headers in an interview transcript. It may include:

  • file names and versions
  • variable descriptions, data types and values
  • header column location
  • codes or classification systems
  • missing values
  • software or hardware information specific to the creation of a particular dataset.

UCLA guide to study and data level description, including examples

Methods of explanatory documentation

README files

A README document is a classic way to record explanatory documentation. A README is a plain text document that is stored alongside a data file.

See an example of a README file published in DRYAD

READMEs are started during the data collection process and updated as the research progresses. The easiest way to start is with an outline.

Outline and best practice for writing README documents, including examples

Data dictionaries

A data dictionary is a collection of the names, attributes and definitions of data elements used in your study. By including a data dictionary, you make sure variables are used in a standard way across a cohort of researchers.

Guide to making a data dictionary, including examples

Metadata standards

Metadata is structured information that describes and enables finding, managing, controlling and preserving other information or data over time.

Metadata serves the same function as a label. Just like other labels, metadata gives information about an object.

Types of metadata

There are two distinct groups of metadata:

  • descriptive
  • technical.

Descriptive metadata describes the data itself, for example title, author and date.

Technical metadata is system-generated and describes the means by which the digital object was created, for example camera type and settings.

Unlike README documentation, metadata complies with a formally agreed set of standards, often tailored to particular types of need or disciplines.

Discipline-specific metadata

If you are working with large datasets, databases or data management systems, consult with your school or department for advice on metadata standards that might be appropriate for your area of research.

Disciplines often have their own metadata standards. These may include vocabulary standards. A vocabulary sets out the common language a discipline has agreed to use to refer to concepts of interest to that discipline.

Find out more about vocabularies and research data

Learn about what metadata standards are available for your discipline from the Digital Curation Centre

Resources for learning about metadata

Metadata Guide - Australian Research Data Commons (ARDC)

The University of Edinburgh’s video about metadata

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