Organizing Data
This is the "Preparing a Data Management Plan" page of the "Data Management" guide.
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Last Updated: Mar 29, 2014 URL: http://guides.library.umass.edu/datamanagement Print Guide RSS Updates

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Why manage your data?

  • Organize your research. Remember that experiment you ran a few months ago? Do you remember all the protocols you used? Do your notes still make sense? Can you recreate it? If you're using someone else's results, do you understand them? Often many people work on the same project, and may leave the lab after a time. Will others on the same project be able to easily use their work? Managing your data increases efficiency by making it easier to understand the details and procedures relating to your data and data collection throughout the lifecycle of the project.
  • Satisfy funding requirements. The number of granting bodies requiring that data be preserved and shared is growing. A good data management plan will help you meet the   requirements of your funding agency and addresses preservation, documentation and verfification issues.  It helps reviewers understand the characteristics of your data. 
  • Preserve your data. The data you collect are the basis of your research. They are your unique contribution, and preserving them means that your work will be recognized by others. It also ensures that your work can support future research and facilitate new discoveries.
  • Safeguard your data. Developing a good data archiving plan makes recovery from disaster possible, faster and more complete.
 

Data repositories

Data sharing maximizes the impact of your work and increases the visibility of your research and extends its relevance. The best way to share data is by depositing well-organized, well-documented datasets in an appropriate data repository. You may already be aware of repositories in your discipline. These are a good way to make your data easily available to scholars who share your research interests. Enabling other researchers to use your data reinforces open scientific inquiry and can lead to new and unanticipated discoveries. And doing so prevents duplication of effort by enabling others to use your data rather than trying to gather the data themselves.

© 2014 University of Massachusetts Amherst.
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