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Research Data Management

Workshop Registration

Data Management, Part 1

Several federal research agencies now require data management plans as part of their funding proposals. Researchers are increasingly expected to provide open access to publicly funded research as part of verifying and replicating research results. This Workshop provides a high-level overview of the research data lifecycle, focusing on areas to consider in order to effectively and responsibly manage research data.

Participants will learn about the basic requirements of a data management plan and where to go for additional, customized help in data management planning. Additionally, time will be set aside in the workshop to discuss future topics for additional workshops focusing on data management.

Location: Havener Center, Missouri Ozark Room.
Date: Monday, October 5, 2015
Time: 3:00pm - 4:30pm

Register Here

Data Management Planning Tool

Create, review, and share data management plans that meet institutional and funder requirements.

  • Free and open to anyone.

  • 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.


Why Manage Your Research 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 in

  • 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

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.

What Is the DMPTool?

Link to DMPTool video

Institutional Repository & Digital Collections Librarian

Roger Weaver
Curtis Laws Wilson Library
400 W. 14th. St.
Rolla, MO 65409-0060
(573) 341-4221