Good Data Practices

  1. Center for Digital Scholarship
  2. Good Data Practices

This page is under development and will be completed by Monday, February 13. Thanks for your patience.

Provocation: Connect & Discover

  • If data-type or domain repositories do not exist for your research area, use sources like re3data to find data available for reuse.

 

Ideation: Plan & Design

  • Planning for data management and sharing is an integral part of the research process. Develop a living Data Management Plan for you and your research team. See our DMP checklist and template.
  • Keep up with emerging standards of practice in your field of research.
  • Assign data management tasks and document those roles and responsibilities in the DMP.
  • Choose storage that meets data security requirements.
  • Identify constraints imposed by your preferred tools (e.g., REDCap for data collection, SPSS for data analysis, etc.).

 

Knowledge Generation: Observe & Experiment

  • Good record-keeping practices are critical for data analysis, reporting, and validation.
  • Integrate your living DMP into the lab manual, procedures manual, SOP, or other team and study documentation.
  • Review and update the DMP frequently - monthly, quarterly, as things change, etc.
  • Assign data management tasks to team members and monitor progress, ideally in regular team meetings.

 

Preservation: Store & Maintain

  • Determine your data retention obligations and select a retention period. Multiple streams of data may have different retention periods (e.g., human participant data, mental health data, etc.). Document the retention period in the readme where you archive your data.
  • Choose a data archive or repository to retain your data for the appropriate amount of time
  • Choose storage that meets data security requirements.
  • Back up the data regularly. Confirm that the back up is intact and that you are able to restore the data.

 

Dissemination: Report & Share

  • Know your obligations for data sharing. These may come from funders, publishers, or your research community.
  • Know your obligations for data citation. These may come from funders and publishers or specific journals.
  • Make your data FAIR (Findable, Accessible, Interoperable, Reusable)
  • Choose a repository that creates rich metadata and assigns a DOI, when possible. See our guidance for choosing a data repository.
  • Formally cite your data in your publication, when this practice aligns with journal policy.

 

Validation: Analyze & Interpret

  • Plan for study close out using this checklist.
  • Execute study close out procedures.