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Data Management for Research

This guide presents information on the effective management of data created through research — including creating a data management plan for grant or project proposals, preserving data after project completion and sharing data with other researchers.

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Helena Marvin
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UMSL GUIDE TO DATA MANAGEMENT

This guide presents information on the effective management of data created through research — including creating a data management plan for grant or project proposals, preserving data after project completion and sharing data with other researchers.

To help UMSL Faculty with their data management needs they can also reach out to librarian Helena Marvin by email at marvinh@umsl.edu or have a meeting. If you would like to book an appointment you can do so automatically at https://umsl.libcal.com/appointments/marvinh.

Introduction to DMPTool.org

The Data Management Planning Tool

DMPTool is a service provided by the library to all researchers on on campus. The tool allows a researcher to create, review, and share data management plans that meet the needs of your research funders and our institution. DMPTool is customized to our campus and provides detailed templates for 100's of different funding agencies. 

  • Free and open to all campus researchers.

  • UMSL users can log in using their Shibboleth / SSO ID and password.

  • Guides you through the process of creating a data management plan to meet funder requirements.

  • Provides links to funder information, suggested answers, and data management resources.

To access the tool click the DMPTool logo or visit: http://dmptool.org/.

The UMSL Libraries provide access to the DMPTool, a free resource that enables researchers to create data management plans for their research projects or grants by offering funder-specific templates, example answers, and guidance. Recently, the DMPTool has developed a new template in response to the NIH's Data Management and Sharing Policy, which took effect on January 25, 2023. To better assist the research community, UMSL Libraries have added guidance and examples specific to UMSL to the DMPTool template. Below is an outline of the process to utilize this template, which is also illustrative of how the DMPTool works.

First, become familiar with the DMPTool:

  1. Visit https://dmptool.org
  2. Enter your UMSL email address in the "Sign in / Sign up" box and click "Continue."
  3. You should receive a prompt that states "Your address is associated with: University of Missouri-St. Louis (umsl.edu)."
  4. Click "Sign in with Institution to Continue."
  5. Enter your UMSL credentials when prompted and click "Sign In."
  6. You should be successfully signed into the DMPTool.

Next, create a new plan using the "NIH-GEN DMSP (2023)" Template:

  1. Click “Create plan” from the top menu.
  2. Enter a project title for the research project you are planning (this should be consistent with the title of your NIH proposal).
  3. Confirm "University of Missouri-St. Louis" is the primary research organization.
  4. Select "National Institutes of Health (nih.gov)" as the primary funding organization.

A new page with a green box at the top should appear with the note: "Successfully created the plan.
This plan is based on the National Institutes of Health (nih.gov): 'NIH-GEN DMSP (2023) ' template." 

Finally, here are some steps to successfully use the Template and associated resources:

  1. In the "Project Details" tab, ensure you have selected guidance from "University of Missouri–St. Louis (umsl.edu)" and DMPTool.
  2. The "Contributors" tab allows you to add the project’s team members, such as the Principal Investigator, Data Manager, or Project Administrator. You can also add collaborators to invite specific people to read, edit, or administer the plan.
  3. In the "Write Plan" tab, there are questions to help you write your plan for each DMP section. On the right side of the screen, under the "Guidance" tab, you'll see the option to select "DMPTool" and "University of Missouri–St. Louis (umsl.edu)" tabs, each of which provides useful suggestions for filling out the different sections.
  4. In the "Research Outputs" tab, you can list potential research outputs you anticipate creating. For example, a dataset to be deposited in a repository, a paper, presentation, or software.
  5. When you are ready to download your plan from the "Download" tab, it is most useful to download your DMP as a docx or text file. You may need to do some additional formatting to ensure your plan meets the page limit when submitted with your grant application.
  6. Lastly, the "Finalize/Publish" tab allows you to set your plan visibility and register your plan. Registering your DMP gives a unique identification number to your plan, linking metadata for funders, institutions, and repositories, ultimately making your plan machine actionable.

What is Research Data

Research data is any systematic collection of information that is used by researchers for analysis.  Typical examples of data include: 

  • Observational data: data captured in real-time, usually irreplaceable
    Examples: Sensor data, telemetry, survey data, sample data, neuroimages
  • Experimental data: data from lab equipment, often reproducible, but can be expensive
    Examples: gene sequences, chromatograms, toroid magnetic field data
  • Simulation data: data generated from test models where model and metadata (inputs) are more important than output data. 
    Examples: climate models, economic models
  • Derived or compiled data: data that is reproducible (but very expensive)
    Examples: text and data mining, compiled database, 3D models, data gathered from public documents
     

Research data can also include video, sound, or text data, as long as it is used for systematic analysis.  For example,  a collection of video interviews use to gather and identify gesture and facial expressions in a study of emotional responses to stimuli would be considered research data.

All research data must be appropriately structured and documented in order for it to be used effectively for analysis.  Additionally, any unique programs or models needed to analyze the data should also be preserved.

Why Manage Research Data

  • Protect your data from loss by maintaining good backups and documentatio

  • Secure your data through effective management of sensitive data

  • Conduct research efficiently by analyzing your data practices

  • Simplify the use and reuse of your data through proper documentation and application of standards

  • Increase your research visibility by publishing your datasets and documentation

  • Meet funding agency, legal and ethical requirements for dissemination and documentation of your research

  • Preserve and provide access to your data in the long term, allowing future scholars to build on your work