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Research Data Management (RDM): RDM Home

One stop shop for all things related to Research Data and how to manage your data throughout its entire lifecycle

Research data are not a mere by-product of scientific research, nor a simple means to (article) publication. They often have a much longer shelf life than the scientific publications they underpin:

  • They constitute the evidence needed to verify and validate published claims.
  • They can be reused for follow-up or new research, teaching, etc.

Therefore, research data should be cared for as first-class research objects. RDM is precisely about that.

What is Research Data

Research data is any information collected or generated for the purpose of analysis to generate or validate scientific claims and original research findings.

They are the evidential basis that substantiates published research findings.

They may be primary data generated or collected by the researcher or secondary data collected from existing sources and processed as part of the research activity.

In addition to the 'raw' data, research data include information about the means necessary to generate data or replicate results, such as computer code, experimental methods and instruments used, and essential interpretive and contextual information, e.g. specifications of variables. Therefore, besides research data, RDM also requires you to manage the documentation needed to make those data understandable.

Types of research data

There is a huge variety of data types.

Research data can be classified in different ways, for example, based on their:

  • Content: numerical, textual, audiovisual, multimedia…
  • Format: spreadsheets, databases, images, maps, audio files, (un)structured text…
  • Mode of data collection: experimental, observational, simulation, derived/compiled from other sources
  • Digital (born-digital or digitized) or non-digital nature (e.g. paper surveys, notes…)
  • Primary (generated by the researcher for a particular research purpose or project) or secondary nature (originally created by someone else for another purpose)
  • Raw or processed nature

Research data can take many forms. It might be:

  • documents, spreadsheets
  • laboratory notebooks, field notebooks, diaries
  • clinical records of treatments and test results
  • questionnaires, transcripts, codebooks
  • survey responses
  • audiotapes, videotapes
  • photographs, films
  • test responses
  • slides, artefacts, specimens, physical samples
  • collections of digital outputs
  • data files
  • database contents (video, audio, text, images)
  • models, algorithms, scripts
  • contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
  • methodologies and workflows
  • standard operating procedures and protocols


Research data management (RDM) is about collecting, caring for, using, preserving and sharing the data supporting your research. It encompasses all practices and actions to ensure that research data are secure, sustainable, easy to find, understand and reuse.

Key elements of RDM include:

  • how to store your data and back them up effectively so that they are protected against corruption and loss
  • how to organise your data, using meaningful file names and logical folder structures and applying version control to modified files
  • how to document your data, so that you (and others) can understand what the data are, how they were collected/generated, and how they have been processed and analysed
  • how to process personal and confidential data, to ensure that sensitive data is protected
  • how to preserve and share your data so that they can be consulted and re-used by other researchers, usually by using suitable data repositories

RDM starts with data management planning. A number of funders ask for a data management plan (DMP) to be completed as part of a grant application, and it is always advisable to create a DMP for any research project involving the collection of primary data.

RDM is especially important when applied to primary data, i.e. new data collected or generated in the research activity. Because these are new and, in many cases, are essential to validating your research findings, it is important to ensure they are properly curated from the beginning.

While you are not responsible for the preservation and sharing of secondary data that you use in your research, you will still need to consider a number of issues, including: how and on what terms are the data to be accessed and used; where and how any copies of data will be stored; and whether the data provider allows copies of the data or derived data to be distributed.


FAIR Principles

The acronym FAIR is used to describe qualities that research data can have which maximises how beneficial it can be. They describes how research outputs should be organised so they can be findable, accessible, interoperable and reusable. Major international and national funding bodies, including the ARC and NHMRC, promote FAIR data to maximise the integrity and impact of their research investment.

The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines. Making your research data FAIR can increase visibility and impact of yourself and your work, maximise potential from your data assets, and improve the reproducibility of your research. Following the FAIR guiding principles will also strengthen your research data management strategy. Even if you don’t intend to share your data with anyone yet, you will most likely reuse your own data.


8 steps to make your data more FAIR

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