Data Management Planning

A guide to best practices for managing research data, including links to data services available to CSU Fullerton.

What Are Data?

Generally, data refer to entities used as evidence of phenomena for the purposes of research and scholarship. Examples of data include but are not limited to.

  • Digital observation 
  • Scientific monitoring 
  • Sensor data 
  • Metadata 
  • Model outputs and scenarios 
  • Qualitative or observed behavioral data 
  • Visualizations 
  • Statistical data (SAS/SPSS)
  • Databases (ex: spectrographic, genomic sequencing, and electron microscopy data)
  • Field or lab notebooks
  • Digital manifestations of literature (text, sound, still images, moving images, models, games, or simulations)
  • Non-digital images 
  • Artistic products
  • Geospatial data
  • Computer code 

Source: Adapted from Big Data, Little Data, No Data 

Managing Data

Data management allows data to be stored efficiently, discovered by secondary users, and used with confidence in its authenticity and integrity. In order to make data management as easy as possible, you can break up the data management process into discrete, doable steps. 

  • First, consider a data planning checklist to answer basic questions about your data and data management needs. This will help you avoid various issues down the road.
  • Create a data management plan to act as your research group's guideline and establish your data needs. Additionally, this data management plan may need to be submitted with your grant proposal. Use the variety of tools and resources available to simplify the process.
  • Document and create metadata, or descriptions, for your data. Documentation and metadata may be essential if: 
    • Many people and/or institutions will be working with the data. 
    • You want to return to this data after a period of time has passed. 
    • You need to publish your data alongside any articles or other output. 
    • You want others to find and cite your data. 
  • Organize your data by following certain conventions so you can track changes and locations.
  • Back up and secure your data to ensure continual access to and control of your data.
  • Understand the copyright and privacy concepts that may apply to your data.
  • Cite your data to ensure recognition and reward of data work, provide attribution detail, facilitate future access, and foster cross-collaboration.
  • Share your data to: 
    • Increase the visibility and impact of your research. 
    • Assist in the dissemination of knowledge by allowing others to replicate your results  or discover new results of their own (and cite you for it!). 
    • Satisfy funding agencies' requirements for disseminating research outputs. 

Source: Adapted from UCLA Libraries