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Data Management : Data Management Planning

About This Guide

Managing your research data means making plans for how the data will be created, described, shared, store, and re-used. Most research funders and federal funders now require a Data Management Plans (DMP) to be submitted as part of a grant proposal. Depending on the funding agency, the DMP format and requirements vary. 

DMP Tool  can help you meet the DMP requirements of different institutions

Regardless of your funding agency's specific requirements, it is helpful to think of data management in terms of both policies and tools. For each area of data management, you will need to first determine your approach, and then find tools or standards to fit the needs of your data. 

This guide pulls together data management resources from the web and explains available University of Rhode Island services. 

A Data Management Primer

Why Manage Data?

  • Federal grant-funding agencies now require DMPs alongside grant applications to receive funding.

  • By properly assessing, documenting, storing, archiving, and sharing your data will, you will spend less time on data management and more time on research.

  • Well organized, documented, and preserved data will be easier for you to find, use, and analyze, and for your collaborators to use.

  • Be a catalyst for research and discovery. Show your support for open access by sharing your data. To learn more, check out the Open Data movement.

(Sources: MIT Libraries, Creative Commons Attribution Non-Commercial License;  Data One)

Contact Us!

This guide was jointly prepared by: 

Julia Lovett, Digital Initiatives Librarian
Karen Markin, Director, Office of Research Development
Updated (2016) by:
Samuel Simas, EPSCoR Graduate Assistant

The Data Life-Cycle

  • Plan: description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime.
  • Collect: observations are made either by hand or with sensors or other instruments and the data are placed a into digital form.
  • Assure: the quality of the data are assured through checks and inspections.
  • Describe: data are accurately and thoroughly described using the appropriate metadata standards.
  • Preserve: data are submitted to an appropriate long-term archive (i.e. data center).
  • Discover: potentially useful data are located and obtained, along with the relevant information about the data (metadata).
  • Integrate: data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed.
  • Analyze: data are analyzed 

(Source: DataONE)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.