Data Life Cycle

The data life cycle is a conceptual tool which helps to understand the different steps that data follow from data generation to knowledge creation. Data sharing and reuse begins with good data practice in all phases of the data life cycle. Under the current situation, the understanding of data as raw material for academic publications is shifting and they are rewarded as valuable products themselves. Thus, it becomes relevant to understand the different phases of the data life cycle. GFBio fosters the ideal data life cycle composed of 10 steps: planning, collection and organization of data, quality assurance and quality control, metadata creation, preservation, data discovery, integration, analysis and visualization and data publishing. All steps are individually described in the following Fact Sheets.

However, in reality the data life cycle is implemented differently, depending on the relationship between the researcher/research project and the data. GFBio has focused on two main groups, data producer and data re-user.

The data life cycle is usually not realized as part of the research process, yet, and realization might vary depending on the type of conducted research, the type of collected data and the type of researchers. GFBio has identified four scenarios of the data life cycle based on the type of researcher, such as data producer and data re-user. Feel free to explore them in the animation and to find out which scenario fits best!

There are ideal practices and steps within the data life cycle that are not fully applied nowadays in the process of science, but are fundamental for good data management.

Data Life Cycle - Fact Sheets

Learn more about each step in the data life cycle.

Get to know how GFBio supports you and find useful links.

  1. Plan
  2. Collect
  3. Assure
  4. Describe
  5. Submit
  6. Preserve
  7. Discover
  8. Integrate
  9. Analyze
  10. Publish

Detailed Workflows

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