Getting started with research data

Introduction to research data management

Good research data management is essential for conducting research in a responsible way.  It helps maintain research integrity and reproducibility, and prevents accidental data loss and disclosure.  Additionally, it helps you to meet ethical, legal and institutional requirements, as well as those set by funders and publishers.  Compliance, organisation and access are three components of RDM that can help you understand how to plan for and embed good RDM practice into your research projects.

    • Comply with data management requirements set by legal regulations, ethics committees, funding agencies and publishers.
    • Protect against data loss by implementsing proper storage, backup and security measures
    • maintian data records and information for the duration specified by the retention period

    • Spending time at the start of your project documenting how you will collect, organis, process and analyse  data will make it easy for you or someone else to reproduce your steps at a later stage
    • Crete streamlined and efficient workflows to ensure data is easy to find, access, use and share

    • Avoid duplication of effort by making data availible for reuse by sharing
    • Maintian public trust by protecting personal information and sharing data only when participant consent has been obtained
    • Wherever possible, openly publish data to enable communities to fully benefit from it particularly when your research is publicly funded

RDM at the University of Melbourne

Researchers and graduate researchers are responsible for managing their research data and records to ensure integrity, accessibility, security, and compliance.  It is essential that you read and understand the University's Research Data Management Policy (MPF1242), Research Integrity and Misconduct Policy (MFP1318) and the Records Management Policy (MPF1106)

These policies apply to all research undertaken by University of Melbourne students, staff and honorary staff. Below are some of the major points that relate to research data management.

  • Researchers must ensure that research data and records are accurate, complete, and a clear representation of research methods
  • Data and records should be organised and maintained in a way that allows for easy understanding, retrieval and access for as long as required.
  • Appropriate measures must be taken to ensure the safety and security of the research data and records, protecting them from unauthorised access, loss or damage.
  • You must comply with all relevant ethical and legal  obligations relating to the collection, storage and use of research data and records
  • Your research data and records must be retained for a minimum of five years after the publication or public release of the research, allowing for verification and potential future reference or analysis.
  • Wherever possible, the research data underlying your publication should be made publicly available to support research transparency and validation
  • Graduate researchers have a responsibility to plan for the proper handling and long-term management of their research data and records beyond the submission of their thesis.  Your plan details should include information about the transfer and deposit of all research data and records associated with your thesis to your department at the time of submission

RDM and Australian funding agencies

In line with researcher responsibilities outlined in the Australian Code for the Responsible Conduct of Research (2018) and international best practice, many research funding agencies either mandate or strongly encourage applicants to provide details of how their research data will be managed both during and after the project.

Below are the policies of two major Australian research funders relating to data generated through the projects that they fund.

Australian Research Council (ARC)

  • Since February 2014, the ARC has required researchers to outline how they plan to manage research data arising from ARC-funded research. From 2020, this requirement forms part of the agreement for funding under the National Competitive Grants Program.
  • The ARC is committed to maximising the benefits from ARC-funded research, including by ensuring greater access to research data. Since 2007, the ARC has encouraged researchers to deposit data arising from research projects in publicly accessible repositories. The ARC's position reflects an increased focus in Australian and international research policy and practice on open access to data generated through publicly funded research.
  • You can view the full ARC statement on Research Data Management online.

The National Health and Medical Research Council (NHMRC)

Benefits of managing your research materials and data

Managing your data effectively is crucial to the success of your research, not just within the immediate context of your thesis but as a practice that will benefit you throughout your career. It also supports research integrity and the long-term management of your research data, including facilitating the potential for re-use of your data beyond the current project.

The benefits of managing your data include:

    • Ensuring the integrity and reproducability of your research
    • Ensuring that research data and records are accurate, complete and authentic
    • Enhancing data security and minimising the risk of data loss

    • Increasing  your research efficiency by ensuring data is easy to find access and use
    • Preventing duplication of effort by enabling others to use your data

    • Facilitating future research by allowing others to build on or add to your research data
    • Increased citations of research data and of publications based on that data
    • Create more engagement and impact for research activities and outcomes

    • Meeting the University of Melbourne's policy requirements
    • Meeting requirements outlined by legislation, ethics committees and funding bodies
    • meeting publisher requirements for data access

Research data lifecycle

A research data management plan (DMP) should be completed at the start of any research project and for Graduate Researchers, at the beginning of your candidature. Your DMP will relate to various aspects in the research lifecycle and will need to be revised or updated as that project progresses or changes. As part of this process, you will need to carefully consider the handling of data at each stage of your project: from accessing, collecting or creating the data, to documenting and analysing it, organising and storing it, sharing, publishing and preserving it, and ultimately to its potential re-use in further research.

Your own research data will have a longer lifespan than just your MPhil or PhD. For example, you might continue to work on aspects of your data after you've completed your thesis; follow-up projects might arise in which you re-analyse your data or combine it with other data. Your data might also be re-used by other researchers who pursue related research projects long after your thesis has been accepted. Well organised, documented, and properly preserved data is easier to access, review and share; as such, it is invaluable to the advancement of research, knowledge and scholarship.

Diagram depicting the key lifecycle stages in research data management

Creative Commons Attribution 4.0 International

Image adapted from: ARDC. (2023). Research data management framework for institutions (Version 2).
Zenodo. ;https://doi.org/10.5281/zenodo.8433246

Data Management Plans (DMPs)

Data needs to be properly managed throughout its lifecycle, the most effective way to do this is to create a data management plan (DMP). All research students should produce a DMP and you should discuss this with your supervisor(s).  To help get you started, the University’s Data responsibilities and planning webpage provides useful information outlining key considerations to include in your plan.

Benefits of a DMP

  • Prioritising and balancing activities relating to research data collection and storage
    Mitigating data loss
  • Having consistency with how data is managed among collaborators
  • Reaching agreement between stakeholders about ownership and responsibility of data
    Reducing time and effort in the long term.