Q. What is the Data Life Cycle?

The Data Life Cycle, or Research Data Life Cycle, is a way to conceptualize the steps required when working with data in a research project.  There are many different models available, but they all share a common set of components.  The data life cycle is a good model to follow whether you are doing funded research, working on a project, or a class assignment.

• Planning: review existing data sources, identify potential archives, create a data management plan.
• Project start-up: make decisions about documentation, methodology, materials, tools, and pretest collection methods.
• Collecting: organize files, variable names, file names, data integrity, active storage, backups.
• Analyzing: software tools, data analysis, versioning, codebooks, Quality Assurance, scripts, code.
• Sharing/Repurposing: file formats, anonymizing & deidentifying, archive requirements, policies.
• Preserving: selection, deposit in archive, curation.

Our Research Data Life Cycle model: http://data.library.virginia.edu/data-management/

The Digital Curation Lifecycle (UK) provides an overview of the stages for curation and preservation of research data. http://www.dcc.ac.uk/sites/default/files/documents/publications/DCCLifecycle.pdf  

The DataONE Data Life Cycle model: https://www.dataone.org/best-practices.

The United States Geological Survey has a Research Life Cycle workflow: https://my.usgs.gov/confluence/display/cdi/Science+Data+Life+Cycle+Model+for+the+USGS%3A+Model+Development+Workspace