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Literature Research for DLA and CU SPUR: Research Data Management

On This Page

On this page of the guide, you will explore:

  • What research data is and examples of research data.
  • What data management is and some recommendations to get started with practicing effective data management strategies.

The Center for Research Data and Digital Scholarship offers Interdisciplinary Data Consulting Hours during the semester where you can get your research data management questions answered

What is Research Data?

Research data refers to information that is gathered, collected, observed, or analyzed as part of the research process (sometimes called the research lifecycle). It can also include code, scripts, and software created to analyze other pieces of research data.

Many different types of data are used by researchers:

  • Primary (data you gather yourself) or secondary (data that has already been collected by other researchers)
  • Quantitative (data represented by numbers) or qualitative (data that can’t be represented by numbers)
  • Experimental (data from experiments with variables controlled by the researcher) or observational (data that does not have any direct involvement from the researcher)

Some of the forms research data can take include:

  • Spreadsheets containing measurements or statistics
  • 3D models
  • Audio recordings
  • Photographs and videos
  • GIS (Geographic Information System) files 
  • Computer code and scripts
  • Visualizations (charts and graphs)
  • There are lots more!

What is Data Management?

Data management refers to how researchers organize the data they create, collect, describe, store, and work with. Data management does not include data analysis (examining and interpreting the data).

When you start a new research project it can be valuable to think about what research data you’ll be using and how you’ll be using it as part of your project.

Be aware of your files

  • Before you start collecting data, consider how many total files you think you’ll end up with and how large all of them will be individually and combined. There’s a big difference between having 10 small files and thousands of large files. This will affect how and where you store your data.
  • Think about what file formats you plan on using and what software is needed to open those formats. Are you using a common file format that anyone will be able to use or will people need to have specific software to open your files?

Use consistent file and directory names

  • The most important thing to do when naming files is to be consistent. Try to use the same format of file names for all of your data. This will make it easier for you to find a specific file.
  • Use names that accurately represent the contents of the files (e.g. “DataSet001-2024-[your initials]”).
  • If you use any abbreviations or acronyms in your file names make sure you have these written down somewhere so people can easily know what they mean.
  • Use directories or folders to help people understand the different steps of your research process (e.g. have different directories for “unprocessed/raw data” and “processed/cleaned data”).

Make backups of your data

  • A backup is a copy of data that you create in case something happens that means you cannot continue to work with your original files, such as technology failure (spilling soda on your laptop), natural disaster (fires or floods), theft, or accidentally deleting everything.
  • A best practice for backups is to have one copy stored somewhere other than your primary computer. This could be an external hard drive or a cloud storage system (e.g. Dropbox or Microsoft OneDrive).
  • USB flash drives can be used for data transfer (between multiple computers), but should not relied upon for backups or long-term data storage.

Document your data

  • Documentation is the process of writing down what was done to the data throughout the research process. This includes:
    • What was done
    • Who did it
    • When it happened
    • Where it happened
    • How it was done
    • Why it was done
  • Create a “data dictionary” that defines acronyms, abbreviations, and other terms used in your data that may not be immediately obvious.