What is Research Data Management?

Research Data Management (RDM) is the practice of organizing, preserving and sharing data collected during research activities. 

The United States Federal Office of Management and Budget defines Research Data as "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings."  Research Data can mean different things to different academic disciplines. It is "data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or other research output is based." (Queensland University of Technology, 2020)

RDM is an integral part of research practice because it helps: 

• Protect data from loss 
• Secure data
• Share data
• Increases visibility of data
• Improve research integrity, reproducibility, transparency

A Data Management Plan (DMP) is a "strategy document (or technical plan), which sets out how data will be managed over the life of a research project and beyond." (Sewell, 2019)  Many grant funding agencies require a DMP as part of the application process. SPARC maintains a list of Data Sharing Policy and Article Sharing Policy requirements by federal agencies. (links open in a new window)

A Good DMP contains the following components: 

• Source of data
• Type of data
• Data input methods
• Ethical and legal restrictions
• Who is responsible for maintaining the data
• Data backup strategy
• Data Organization
• Plans for Data Sharing

(Sewell, 2020)

Evaluating Your Data Management Needs

Planning for data collection is part of the research process, but managing your data is an ongoing process. It is important to think about what will happen to your data and any data needs you might have. 

These are good question to ask as you start thinking about a DMP:

• What type of data are you producing?
  • Gather a clear picture of what your data will look like. Is it, for example, numerical data, image data, text sequences or modeling data? Knowing this will inform decisions you need to make about storage backups and more. 
• How much data, and at what growth rate?
  • Once you know what kind of data you're producing, you'll be able to access the growth rate. For example, are you gathering data by hand or using instrumentation that is able to capture a lot of data at once?  Will there be more data as time goes on? Do you need to plan for growth and changes in storage needs?
• Will it change frequently? 
  • The answer to this question impacts how you organize the data and the level of versioning you will need to undertake. Keeping track of rapidly changing datasets can be a challenge, so it's imperative you begin with a plan that will carry you through the data management process. 
• Who will use the data? 
  • Who is your audience for the data? How will they use the data? The answer to this question will tell you how to structure the data and where to distribute it. 
• Who controls the data (you, the university, a research center, the funder)?
  • Before you decide how you will manage the data, you need to know if you have the authority to control it or if you have to abide by external requirements.
• How long should the data be retained? (e.g. 3-5 years, 10-20 years, in perpetuity)
  • Not all data needs to be retained indefinitely. Figure out what's important to keep long-term and make sure your plan for retaining those datasets is solid, and if necessary, addresses issues around long-term preservation and access.

DMPTool

The University of California Curation Center has created the free DMPTool (link opens in a new window) to help researchers create data management plans that meet funder requirements.  It walks users through creating a data management plan and allows you to work with collaborators, and share your plan with others for feedback.  

To create an account choose "Sign In" in the top right hand corner. First time users need to choose "Option 3" and create an account with your ETAMU email address. Once you have created your account you can sign in using "Option 2" for further visits.

Research Data Management Resources

The information on this page is derived from the following sources: (links open in a new window)

Sewell, Claire. The No-Nonsense Guide to Research Support and Scholarly Communication. London: Facet Publishing, 2019.
Patti Condon. The Data Management Toolkit. University of New Hampshire Library. 2022. Licensed under a CC BY-SA 3.0.