Skip to Main Content

Graduate Student Success Hub

A McFarlin Library guide for Graduate Students.

Research Tips

Thinking Critically About Information

The Information Lifecycle

The Data Life Cycle

No two projects involving data are the same, but nearly all data projects follow the same basic cycle from start to finish, often called the Data Life Cycle (Harvard Business School Online).

Searching for Data Life Cycle information, you may notice that the number of steps vary from resource to resource, with the number of total steps ranging from five to eight steps. Regardless of the total number of steps, the Data Life Cycle generally includes:

  • Data Management Planning
  • Data Acquisition or Generation: Acquiring or creating data for research. Data can be created by the researcher directly, passed to the researcher for use, or similar.
  • Data Analysis and Processing: Data cleanup, versioning of data files, analysis, and processing through code or a program to extract meaning.
  • Publication and Data Sharing: Publication of research results, publication of data, publication of data in open repositories for others to use.
  • Data Storage and Preservation: Long term storage of data for preservation. Includes publication of data in an open repository for others to utilize.
  • Data Reuse: Allowing others to pull your datasets to extract meaning, reproduce findings, or to discover new information.

When you finishing working with one dataset and begin a new project with new data, or when others use your prior research data, the Data Life Cycle begins anew because the Data Life Cycle is a continuous process. 

An image of the Data Life Cycle. This image shows a circle demonstrating the cycle from data creation to data processing to data analysis to data preservation to data sharing to data re-use. This is a singular example of the explanation of the Data Life Cycle and is not the authoritative or definitive model.

This image shows a circle demonstrating the cycle from data creation to data processing to data analysis to data preservation to data sharing to data re-use. This is a singular example of the explanation of the Data Life Cycle and is not the authoritative or definitive model (Carpentries).

 

Helpful Resources