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Managing Your Research Data

Tips & tools to help you manage your research data with less stress


Why is it so important to document or describe my research?
The difference between science...

Adam Savage (Mythbusters) says it best.

An important part of the research process is documenting the plan, what actually happened, and your thoughts about what happened or didn't, and why.


Always take better notes than you think you need. Many problems that happen during analysis and reporting can be prevented by taking detailed notes. Think of it as writing notes to your future self.

Think about what information would be needed to understand and analyze your data, and/or replicate your results in 20 years. Then think about how researchers in your field usually do that. Do they use lab notebooks, procedures manuals, protocols, readme.txt files, or something else? If you don't know, ask your faculty advisor or supervisor.

The answers to these two questions should tell you what about your research is important to describe and how. For more details about what you might need to document for your project and the data specifically, see the lists below.

Project-level details you should document:

  •  Name of the project
  •  Dataset title
  •  Project description
  •  Dataset abstract
  •  Principal investigator and collaborators
  •  Contact information
  •  Dataset handle (DOI or URL)
  •  Dataset citation
  •  Data publication date
  •  Geographic description
  •  Time period of data collection
  •  Subject/keywords
  •  Project sponsor
  •  Dataset usage rights

Data-level documentation is much more specific and may include, among others:

  • Data origin: experimental, observational, raw or derived, physical collections, models, images, etc.
  • Data type: integer, Boolean, character, floating point, etc.
  • Instrument(s) used
  • Data acquisition details: sensor deployment methods, experimental design, sensor calibration methods, etc.
  • File type: CSV, mat, xlsx, tiff, HDF, NetCDF, etc.
  • Data processing methods, software used
  • Data processing scripts or codes
  • Dataset parameter list, including
  • Variable names
  • Description of each variable
  • Units