Skip to Content
Describe and explain all the information that will help you and others to make sense of your data in the future; and to understand the processes you followed to collect, process, and analyse it. Watch this video for an overview of creating explanatory documentation.
Descriptive and explanatory information is created at two levels:
Study level: provides an overview of the research context and design, data collection methods, data preparation and 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.
Data level: can be embedded in data (e.g. headers in an intervew transcript), or recorded in a structured document. It may include:
Document your descriptions by creating explanatory documentation, and by using metadata standards.
A README document is a classic way to record explanatory documentation. A README is a plain text document that is stored alongside your data.
A data dictionary is a collection of the names, attributes and definitions about data elements that are being used in your study. By including a data dictionary, you ensure a standard use of variables across a cohort of researchers.
Metadata is structured information that describes and enables finding, managing, controlling and preserving other information (i.e. data) over time. Metadata serves the same function as a label. Just like other labels, metadata provides information about an object.
There are two distinct groups of metadata: descriptive and 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 e.g. 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.
If you are working with large datasets, databases, or data management systems, consult with your School for advice on metadata standards that might be appropriate for your area of research.
Disciplines are also now establishing their own metadata standards. The standards may include content and 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:
Page authorised by University Librarian
Last updated on Friday 18 May 2018
Research Data Management Librarian
Guidance, training opportunities and practical support.
Research Development Team
Data management (funder retention requirements) advice for current and future research projects.
Contact a Subject Librarian
Personal research help by email, phone, or appointment.
These guidelines are informed by information provided under open licenses by other organisations including: