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Research Data Management (RDM): Data Description

Data management essentials for Melbourne Polytechnic researchers

What is metadata?

Metadata is information about your data.   
It is often presented in the form of descriptions (but also exists in a variety of other forms). 
Good quality research metadata is important:  it enables data to be sought, discovered and reused.

View this video to learn more about how metadata works.

If properly composed, metadata enhances discovery by search engines, and provides a greater depth of information to the users of the data.

Metadata: within the research context

Within the research context, there are two types of metadata: 
Project-level metadata:  a broad, descriptive form of metadata that names various elements of the research, such as dates, project details, and  geographic locations.  It tends to provide details and descriptions that are contextual to the project.

Dataset-level metadata:  this form of metadata provides information about the data itself.  

Source:  Research Data Services, Oregon State University:

Examples of metadata (Note: this list is an indicator - not an exhaustive list)

The name(s) of the data’s creator  
The research-relevance of the data
The purpose of the data  
The content of the data file
The filename in which the data sits
Details from when the data was generated
The place and date that the data was generated
The purpose underpinning why the data was generated
The process for generating the data
The terms under which the data may be reused

Collectively, this type of descriptive information (data about data) is referred to as "metadata".

Source: Adapted from ANDS   

Best practice: Documenting & describing

Document and describe your data  
Present an accurate record of your research project's data files through a carefully prepared and created data description document.  This will serve as both a record of the data that has already been collected; and as a resource for those who may wish to employ the data in later research projects.  

Typical data characteristics that feature in a data description document include:  
Data formats
Methods of data capture
Database size
Data types
Basic statistics derived from the key attributes

Data description documents vary in size and content.  At a minimum, it's recommended that you include:
A list of each file in the dataset
Briefly described contents of the data
Software used to create the data  

Research data that is well-documented, described and organised facilitates easy retrieval for those who wish to locate and re-use the data.  

Source:  BYU Library:  Scholarly Communications  

Data descriptions and formats: points to consider

The approach that you take towards data description is shaped by numerous considerations and choices:

  • Data types:  which types of data will you need to gather (eg sounds, visuals, numeric, survey responses, physical samples, digital data)
  • Naming and organising your files:  which formats do you propose?
  • Data capture:  which methods will you be employing?
  • File types:  which will you use for capturing or recording your data?

Indentifying what to describe

Ask these questions of your data.  Your responses will help you to identify which elements to describe.

  • What it is?
  • How will it be collected? By whom?
  • How much data will be generated?
  • What data formats do you use?
  • Any equipment or software used? 
  • Is there any personal identifiable information or confidential data?
  • Are you using data that someone else produced? If so, where is it from?
  • What programs or code is needed to read or understand these files?
  • What was changed? New project members? New methods? When did it happen? Why?
  • Who will be a custodian of the data?

Recommended Resource

Australian Research Data Commons


"This Guide is intended to provide a simple generic working-level view of the needs, issues, and processes around metadata collection and creation" 

Read-me files for metadata

A Readme file contains information about a data file.  Its purpose is to describe the data to the extent that correct interpretations of it will be maintained over a period of time.  

For instruction, details, templates and and other related resources consult  Cornell University's Guide to writing ReadMe style metadata