Good Data Practices

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  2. Good Data Practices

Good data practices are context-specific

The specific strategies for managing data differ between fields of research, like research design, methods, and expectations for research integrity can vary. However, there are core elements which remain consistent across all types of research. Core elements of good practices for data management and sharing include the following:

  • Develop a Data Management and Sharing Plan (DMSP) that documents your obligations and plans
  • Know your obligations with respect to data management and sharing
    • Legal requirements - international, federal, state, local
    • Ethical obligations of your professional and/or research communities
    • Funder or sponsor
    • Institution(s)
    • Publisher or journal
  • Know what you want to do with the data
  • Use of data management and sharing plans to ensure consistency in practices such as file naming, storing data securely, data curation, data retention and disposal, etc.
  • Adopt and implement relevant data and metadata standards, such as the FAIR Guiding Principles and the CARE Principles for Indigenous Data Governance

 

Other good research practices that effect data practices

  • Set clear expectations for research personnel as documented in team/lab manuals, including designation of responsibilities
  • Maintain project/study documentation that is accessible by all project/study personnel
  • All research personnel take responsibility for the trustworthiness of the research
  • Determine the constraints of your technological resources and recognize when external expertise (beyond the research team) is necessary, particularly when related to cybersecurity, contracts or licenses, and data curation

 

Key Resources