The benefits of data management planning

Effective management of research data provides many benefits to research staff, including:

  • decreased risk of data loss or misuse
  • good research practice ensures integrity and quality of data
  • well described data can be used immediately or archived for future use
  • helps research gain access to data management expertise and resources at Swinburne
  • enables researchers to identify research storage needs and request permanent allocation on Swinburne Research Storage
  • enables researchers to be more proactive about their research needs
  • increased researcher profile through data dissemination and re-use
  • enables researchers to clarify ownership, assign responsibility and set up technical standards and frameworks in research collaborations
  • enables researchers to more easily defend their research method or outcomes if requested

Researchers are encouraged to keep clear and accurate records of their data – raw or archived – including information about data access, retention, ownership and sharing agreements.

Currently there is no internal requirement for Swinburne researchers to centrally lodge a data management plan. However, it is anticipated that the ARC and NHMRC will increasingly require grant holders to produce data management plans in line with practices emerging from the UK and US. For this reason we recommend that researchers, where practicable, include data management plans as part of their everyday research. In the case of human research or biohazardous data, data management is important to ensure compliance with legal and ethical requirements.

Best practice – data management and record keeping

Good record keeping ensures that the data will be easy to locate, understand and in the case of academic disputes, defend. Documentation of how the data collection relates to the final analysis also supports the integrity, reproducibility and re-use of publicly available or collaborative data. It is critical that data description provide provenance and contextual information for the data so that is can be understood in the future or by other researchers.

Record keeping requirements will vary depending on the discipline and type of research being conducted. Producing good documentation is easier if it is planned from the start of a project and considered throughout the lifecycle of the data. Data-level descriptions can be embedded within data files themselves. Many software packages allow data annotation or the inclusion of metadata. Data documentation may also be contained in publications, progress reports, online lab books, data management plans, or created as a personal user guide.

Human research and data management planning

Researchers should carefully plan and submit data management proposals as part of their ethics or biosafety clearance application. Once the proposals are approved, researchers should monitor and maintain the data management arrangements for the duration of the project or the required retention period (as applicable).

Creating a data management plan

Data management plans should be created at the beginning of a project and treated as a "working" document. It is up to the researcher to decide the format of a plan, which may be a simple text document, a spreadsheet or a form. Swinburne Library has developed a Research Data Management Checklist for researchers. The checklist takes you through the components of an effective data management plan (the library also provides resources for managing publications and linking data to your publications in Swinburne Research Bank).

Ideally, a comprehensive data management plan should include the following information:

  • Description/Context of the project: includes title, summary, collaborators, funding, duration, details of external policies that may impact. e.g. external funding, research groups
  • Information about the data: what is the ‘data’ for this project, where is it stored, who owns the data, who is responsible for backing up data, what is the extent of size of the data, file formats
  • Access to the data: who has access to the data, how is it shared, is the data proprietary, what is the embargo period, is the data confidential, and are there third party agreements associated with the project and data
  • Retention requirements for the data: These may be specified by the discipline, or funding body.
  • Short-term data planning: how will the data be managed throughout the duration of the research project
  • Long-term data planning: how will the data be managed once the research is published, will the data be archived, will the data be made available to the public, who will be responsible for managing the data

At the data-level, documentation may include:

  • Description of file formats
  • Definitions of specialist terminology or acronyms and abbreviations
  • Technical aspects of data collection, e.g. intstrument parameters
  • Data quality flags or descriptors
  • Hardware or data processing parameters