Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
What is Data Management? Why is it useful?
Research data management (RDM) is a concept used to describe the managing, sharing, and archiving of research data to make it more accessible to the broader research community. Research data management provides an opportunity for a researcher to create a plan that will ensure that their data will be organized so that it can be shared with other researchers and archived for long term preservation.
- Makes data more findable and usable, and reproducible
- Ensures an ethical, responsible research environment
- Meets funder and journal data-sharing requirements
Best Practices of Data Management
Consider these goals for your data -
1. Organization -
- Assign RDM responsibilities (or take responsibility)
- Use clear, descriptive file names
- Be thoughtful about how you structure your folders
- Use disciplinary standards and sustainable data formats Be consistent and document
2. Storage and backup -
Follow the 3 – 2 – 1 rule :
- Keep one original, uncompressed version
- Document versioning (with a standard naming convention)
- Consider security ○ During data collection: who can access it and what can they do with it?
- When storing: restrictions versus embargoes
3. Preservation -
- Software and hardware change
- Store data where it will be backed up, discoverable, and accessible (as appropriate)
- Document clearly and thoughtfully
4. Distribution and sharing - Consider :
- What data should be kept
- Ownership, privacy, and intellectual property concerns
- What data will be shared
- What is the best home(s) for it
Tools for Data Management