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Research Data Management Guide: Describe your data

This guide aims to provide information and resources to support best practice in managing research data at Charles Darwin University (CDU).

What is metadata and why is it important?

Data documentation (also known as metadata) enables you to understand your data in detail and will enable other researchers to find, use and properly cite your data.  The term metadata refers to descriptive information about data. It includes information such as the dates and location of the creation of the data, data author and the parameters of data creation.

It is critical to begin to document your data at the very beginning of your research project, even before data collection begins; doing so will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project.

Data documentation explains how data was created or digitised, what data means, what their content and structure is, and any manipulations that may have taken place. It ensures that data can be understood during research projects, that researchers continue to understand data in the longer term and that re-users of data are able to interpret the data. Good documentation is also vital for successful data preservation.

Metadata is typically used for resource discovery, providing searchable information that helps users to find existing data, as a bibliographic record for citation, or for online data browsing.

The EDINA Data Centre has created a video (9 mins) called ‘What is Metadata?’ which explains metadata through a range of examples.

What metadata elements should you include?

Metadata can be at an item level to describe a single object, or at a collection level to describe an aggregation of objects. Metadata elements may be descriptive, technical, specify access or rights, or provide information for preservation purposes.  The following are essential elements to consider when describing your data:

• Title - name of the dataset

• Creator - names and addresses of the organisations or people who created the data

• Identifier - an unique number used to identify the data (see the 'citing data' tab for more details)

• Date - key dates associated with the data, including project start and end date, and any other important dates associated with the data

• Content description - keywords or phrases describing the subject or content of the data. It can also include Fields of Research codes and Socio-Economic Objective codes as defined by ANZSRC (Australian and New Zealand Standard Research Classification)

• Technical description - file types, e.g. Word, PDF, Excel

• Rights/access - any known intellectual property rights, statutory rights, licenses, or restrictions on reuse of the data. Details on how your data can be accessed by other researchers

CDU Library staff can discuss and provide assistance with any specific metadata requirements that you may have.

Examples of metadata standards

There is no single metadata standard to "rule them all." Each discipline or community of practice may have its own preferred standard that best meets the needs for that research area. When describing a research dataset, it is important to choose the metadata standard that most closely fits and use that standard to describe the dataset as richly as possible. Below is a list of some of the established metadata standards used for research collections.


Dublin Core - useful for many disciplines and for interoperability across disciplines

MODS (Metadata Object Desciption Schema)

RIF-CS - used by the Australian National Data Service (ANDS) and used in Research Data Australia


Darwin Core - used in biodiversity


EML (Ecological Mark-up Language)

Geographic data

ANZLIC -The governments of Australia and New Zealand have formed ANZLIC to help set metadata standards for geospatial data across their jurisdictions including the Australian Government Locator Service (AGLS) record keeping standard

CSDGM (Content Standard for Digital Geospatial Metadata)

ISO 19115:2003 (Geographic information metadata standard)


VRA Core (Visual Resources Association)

Social Sciences

DDI (Data Documentation Initiative, Social Sciences)

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Research data inventory

Your metadata documentation should be stored with the other documentation related to the specific research project. In addition, the Australian Code for the Responsible Conduct of Research also requires you to maintain a catalogue of research data (Section 2.6.5).

Metadata records and data files (medium to small size) can be stored the CDU eSpace repository by means of the Self Submission Portal. The Library's Digital Services team make these descriptions (metadata) available to national/global registers (i.e. Research Data Australia).  

CDU Library has also produced a RDM planning template to assist researchers with the descriptions and management of their data: 

Related resources from ANDS

ANDS Guide to Metadata (awareness level)

ANDS Guide to Metadata (working level) - written for people who have to "do something" with metadata

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