More and more, grants are requiring a data management plan in order to meet their funder requirements. A good data management plan will also help you manage your data and help others use your data if shared.

A good resource available to you at the university is the DMP Tool. This tool provides templates for Data Management and Sharing Plans that comply with NIH and other funder requirements. You can access the tool with your Mizzou email address, enabling you to share your plans with a data librarian for review and access templates containing language specific to Mizzou’s resources.  

Not sure where to start? Schedule a Data Management and Sharing Consultation with a librarian.

If you have a data management and sharing plan already, you can submit your Data Management and Sharing Plan for a Data Librarian to review.  

Finding where to store your data can difficult. Our data services librarian has curated a robust list of data repositories based on discipline.

This extensive list includes:

• A description of each repository
• any costs associated with storing the data
• submission information
• who has access to the data

But how will know what repository is best for you? Consider the following:

1. Depending on your funding and the data that your project generates, you may be required to deposit your data in a specific repository. If this is the case, your funding agency/institution will explicitly state where you must store your data.
2. Otherwise, if there is a repository that is commonly used within your discipline, you should store your data there. A great way to locate these repositories is by consulting mentors on where they have stored project data in the past.
3. In the absence of a funder-mandated or a discipline-specific repository, you should store your data in either a generalist or an institutional repository.

If you’d like to discuss the best place to store your data, connect with our data services librarian.

Finding available data can be difficult, but our data services librarian can help with the process. To get started, you can find some available health datasets linked on our guide.

Once you find your data, how do you make sure the data you find is credible? Here are some steps for you to keep in mind to ensure what you find is useful for your project.

1. Look for supporting documentation outlining what the data is, how it was collected, and how to interpret the data.
   • Tip: Look for readme files, data dictionaries/codebooks, and a collection methodology
2. Make sure you can open all files associated with the data.
3. Ensure that all files are clearly labeled and store the information and/or data that is indicated in the file name.
4. Within the data files, check for the following:
   • Variables are clearly labeled with standard naming conventions.
     • Example: First names are labeled as FirstName and last names are labeled as LastName
   • Units of measurement for different variables are explicitly stated.
     • Example: You can tell if measurements are given in centimeters (cm) or inches (in)
   • Each variable contains a discrete unit of information.
     • Example: blood pressure and zip code are stored in separate columns
   • Variables follow data standards and have consistent formatting.
     • Example: All dates are in yyyy-mm-dd format

Still need help finding and evaluating data? Connect with our data services librarian who can help you find the data you need.