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New to Data Management?

Research data and material collections are intellectual assets. How are you managing yours? 


Why manage data? The benefits:

  • avoid data loss by appropriately storing and indexing your data 
  • avoid duplicating others' work 
  • compete for future funding: be equipped with a sound data management plan to meet eligibility criteria for ARC and NHMRC funding 
  • identify potential collaborators in your fields of research
  • participate in accelerated research: by discovering and re-using prior publicly-funded research discoverable in RDA, researchers can accelerate their own findings 
  • practise sound data management: manage data according to best practice, thus supporting the integrity, credibility and repeatability (where applicable) of your research
  • showcase your data: make your data discoverable for citation purposes, and to be shared with the right people at the right time 

What if I don't? The risks of poor management:

  • you may lose opportunities to collaborate with others 
  • you may lose your data and have your research integrity questioned
  • you may not be competitive for Future Funding without a DMPlan
  • you may miss out on having your data cited amongst your research peers

What is research data management?

Good data management supports and enables your research. It involves planning and making decisions about how you will collect, organise, manage, store, back-up, preserve and share your data throughout its lifecycle. That lifecycle typically involves:

  • evaluating your needs: team, file formats, permissions
  • establishing good storage, indexing, backups, and access for your research team
  • describing your data
  • knowing your sharing limits and responsibilities

Data management may be defined as any and all of the following activities:

  • organising data into directories/folders and using systematic and helpful filenames
  • organising materials collections such that the items are easily located, including indexing
  • ensuring security measures are applied to confidential data
  • keeping backups of data (in a different location to the original data), such that you can retrieve it if you accidentally delete or lose data
  • synchronising data between desktop, laptop, USB key, cloud storage, etc.
  • making data available to others via an archive or website
  • storing final state data in an archive
  • collaboratively creating and sharing data with other researchers
  • storing and backing up a bibliography and electronic copies of relevant literature

What is considered research data?

Research data encompasses data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media.  Data may typically take any of these forms:

Data that may more easily be digitised:

  • collection of digital objects acquired and generated during the process of research 
  • contents of an application (input, output, schemas) 
  • databases
  • documents, spreadsheets and presentations 
  • field notebooks, diaries 
  • laboratory notebooks
  • methodologies and workflows 
  • models, algorithms, scripts 
  • questionnaires, surveys, transcripts 
  • test responses or results 

Data that has a material or tactile origin:

  • audio and video tapes
  • material collections of artefacts, specimens, samples 
  • photographs, films, slides