Research data toolkit

Research data toolkit

Research data management

This toolkit will assist Macquarie's research community with research data planning and management. It provides guidance and reference materials and enables researchers to include sound data management practices in their projects from the outset.

Intro to data management

Correctly managing your data protects you from data loss or duplicating others’ work. By managing, citing and sharing your data, you will be able to identify potential collaborators, accelerate your research and become more competitive for future funding opportunities.

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 so items are easily located (includes indexing)
  • applying security measures to confidential data
  • backing up data in a different location to the original data
  • synchronising data between storage locations (desktop, USB, cloud, etc.)
  • making data available to others via archiving or websites
  • storing final state data in an archive
  • collaboratively creating and sharing data with other researchers.

Defining research data

Research data encompasses data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which 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.

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

Create a data plan

Good data management begins with a data management plan: a document outlining how research data and materials will be managed, stored and secured throughout the project, as well as what will happen once the project is complete. It is best to have a plan from the start, but there is value in making one retrospectively.

For more information on forging a data management plan, see the following resources:

Code of conduct and policies

The Australian Code for the Responsible Conduct of Research's Section 2.6 'Manage storage of research data and primary materials' states that Researchers must:

  • 2.6.1   Keep clear and accurate records of the research methods and data sources, including any approvals granted, during and after the research process.
  • 2.6.2   Ensure that research data and primary materials are kept in safe and secure storage provided, even when not in current use.
  • 2.6.3   Provide the same level of care and protection to primary research records, such as laboratory notebooks, as to the analysed research data.
  • 2.6.4   Retain research data, including electronic data, in a durable, indexed and retrievable form.
  • 2.6.5   Maintain a catalogue of research data in an accessible form.
  • 2.6.6   Manage research data and primary materials according to ethical protocols and relevant legislation.

Macquarie researchers must be familiar with Macquarie's policies, procedures and conduct.

Describing data

Describing your research data allows for informed interpretation, verification and further analysis re-analysis by yourself and others.

Describing your data involves capturing information that the data itself does not contain, but is still vital to understanding it. These descriptors are referred to as “metadata”, and should be implemented from as early in the project as possible. Examples of metadata include:

  • when and where the data was created or collected
  • equipment used
  • code and abbreviation key
  • typical accuracy and/or resolution
  • methods of analysis
  • software code
  • technical requirements for access or re-use

Different projects will require different metadata standards. A broadly-applicable standard is Dublin Core. You can find more information on metadata at the following resources:

Storing data

Regardless of your project, you will need a plan for storing data. Consider the following:

  • Where and how to store your data
  • Backup methods
  • Who needs access to your data, and when?

Your methods for data storage and access should be governed by your project’s needs:

  • What is the volume of data needed and for how long?
  • What formats (.doc, .jpg, etc.) will be used and how easily can data be migrated to other formats?
  • What are your privacy and security requirements?
  • Is your desired storage method long-term or will it need to change for archiving?
  • Will working and final data be dealt with differently?

Macquarie provides low-volume storage for Macquarie staff through Truth. If you have larger storage needs or are collaborating with external staff, you can use an external storage system, or lodge a OneHelp Request at, including ‘Research Data Storage Request” in the email subject to discuss your needs and options.

Showcasing data

While some disciplines have traditions of showcasing data, all should consider showcasing metadata via Research Data Australia (RDA).

RDA promotes visibility of Australian research. Contact for further info.

Citing data

It is important to cite the data that you use to acknowledge its creator and provide context for your use. Data citation will be dependent on your discipline and publisher. If you cannot find a preferred format, refer to the DataCite Consortium, the University of Oregon Library and ANDS Data Citation Guide.

To make your data citable, we recommend you:

  • share your data so it is accessible to others
  • include a preferred citation when you publish your data
  • if determining rights for the use of your data, specify that attribution to the original creator is necessary.

Sharing data

Sharing your data benefits the wider research community and can increase your credibility. This does not necessarily mean making your data publicly available.

Options for sharing your data include controlled/mediated access such as providing access upon request, formal protocols for data access, or publishing your data in the public domain.

Sources of published data include:

External resources

Australian National Data Service - ANDS is funded by the Australian Government's Department of Industry (previously DIICCSRTE) to help transform Australia's research data environment.

To enable this transformation, ANDS is:

See the ANDS website for comprehensive information on Data Management, Metadata, Discovery, Access and Re-use of Data, Technical Resources and Guides.

Research Data Australia is an Internet-based discovery service designed to provide rich connections between data, projects, researchers and institutions, and promote visibility of Australian research data collections in search engines. There are many Macquarie data collections already described in RDA.

Research Data MANTRA is a course designed for PhD students  and others who are planning a research project using digital data.

ANDS DMP 21 is an expandable data management planning tool based around 21 questions.

The UK's Digital Curation Centre provides expert advice and practical help to anyone in UK higher education and research wanting to store, manage, protect and share digital research data.

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