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Module 02 - The ethics and benefits of good data management practices

Effective data management not only helps make you more efficient and your research more reproducible, it can also mitigate the ever-present risk of data misuse. Misuse can range anywhere in severity from accidentally sharing data without authorisation to cherry-picking of data and outright theft.

The consequences of data misuse are infectious and can extend beyond an individual to the research group, collaborators, and their institutes in the form of serious legal implications, reputational risk, and negative impacts on career trajectories. On a broader level still, data misuse is harmful to the integrity of the research, science, and innovation sector, and has important social implications relating to public trust in science.

We're not intending to be dramatic by pointing out these consequences, or scare researchers into data management. Rather, we hope that by highlighting some of the potential risks and consequences, we can enable researchers to mitigate these by implementing conscientious and consistent data management practices.

We recognise that there remains a gap between knowing and doing, in part due to the scarcity of ethical frameworks and clear guidance on what day-to-day data management could look like. Different groups will be positioned at different points on their data management journeys.

While it is easy to envision the consequences of data misuse, what are the incentives to upholding good data management practices?

  • Good data management minimises the risks of data misuse, loss, or theft, improves transparency, and ensures data management aligns with the FAIR and CARE Principles as appropriate for the data.
  • Good data management benefits the researcher through increased efficiency and greater opportunities for and outcomes of collaborations, with flow-on effects that can help to accelerate career trajectories.
  • Good data management makes life easy!

We encourage researchers to view data management practices as behaviours intrinsic to the research process. A little data management often is a great way to start.