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Research Data Management: Home

Help guide to assist NT Health staff undertaking research to understand the importance and process of managing data

Introducing Research Data Management

The very first step in talking about data is to ask yourself "What is data'?

There are many definitions of data and it comes in a variety of types and forms. A definition from the OECD, 2007 is: 

 'factual records (numerical scores, textual records, images and sounds) used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings."

Data may be gathered from raw or primary sources (obtained directly from a measurement or collection) or it could be extracted from a larger primary dataset and 'cleaned up', for use in another project. Data is primarily used to support or validate a research projects findings.

The following table outlines some of the common types and formats that can represent data - this is not an exhaustive list!

Data formats Data Types
Documents (Text, pdf, MS Word) Results from clinical trials
Spreadsheets (MS Excel) Measurements collected from fieldwork
Questionnaires, surveys, transcripts Questionnaire responses
Slides, samples, specimens Images
Diaries, journals and notebooks Social media data (Tweets)
Raw data generated from software Transcripts
Log files and schemas from software Software programme outputs

Source: Exploring research data management


When you decide to undertake any kind of research you will need to consider how you will create, find, organise, store, share and preserve the data for the project. Once the project is finalised thought for the ongoing access and availability should also be considered.

Research Data Management should begin with the commencement of the research project and continue throughout the research lifecycle. 

If you intend to use NT Health data in your research project the Data Governance NT Health Framework  and Data Standards will assist you in understanding how NT Health have created best practice standards for ensuring long term preservation and access of health data. With this in mind, you can plan to manage the data from your own project and ensure it remains secure, accessible and viable into the future.

Managing your research data is important for the following reasons:

  1.  Data is fragile and can quite easily be lost if it is not stored correctly.  Imagine if your data was 'temporarily'  stored on a USB in your pocket and that USB  accidently went through your washing machine!
  2. Correct organisation can prevent errors and improve research quality. Following a systematic approach in how you decide to store, share and access your data will keep it 'tidy', easier to find and use.
  3. Research findings can be replicated and validatedOn occasion you may be asked to share your data with others, or it may be requested to help substantiate another research project. 
  4. There are time and resource savings to be gained by applying data management practicesThink how much time is spent actively ensuring your data is safe and secure in comparison to scrambling to locate it many years down the track when it may be required again.
  5. Publishers and funders may request to see your data Imagine if publication of your research requires the submission of your data as validation of your findings - and you don't remember whose local drive it is stored on!
  6. Data in published journal articles is valuable research output. Others can benefit from all your hard work if your data can be used outside of your project. 


First things

No one should ever embark on a major journey without a plan. This should also be the case when you decide to take on a research project. Thinking ahead and planning is an important first step in considering all the possible details that will impact the process and its eventual completion.

In Exploring Research Data Management, Cox and Verbaan (2018, p.116) assert that a data management plan has the potential to improve research data management as long as the following concepts are considered:

  1. They should be actively used throughout the project, rather than being written and filed away.
  2. They should be updated according to any necessary changes identified as the project unfolds
  3. They should be informed by best practice.

There are many data management plans available through reputable research organisations that can give you guidance about what is expected in preparing your own data management plan. We recommend you look at the following sites and documents and adapt one that will suit the research project that you are undertaking

Clinical Research Data Management Templates

NHMRC Management of Data and Information in Research > View
Medical Research Council (MRC) Template for a Data Management Plan > View
National Cancer Institute Master Data Management Plan Template > View
NIH NIDCR’s Clinical Data Management Plan Template > View

Non-Clinical Research Data Management Templates

Australian National Data Service (ANDS) Data Management Plans > View
Australian Research Data Commons - Data Management Plans > View

This short video takes a look at the what, why and how of a data management plan