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Data Lifecycle

Data are corporate assets with value beyond USGS's immediate need and should be managed throughout the entire data lifecycle. Questions of documentation, storage, quality assurance, and ownership need to be answered for each stage of the lifecycle.


Create a data management plan and learn about important planning activities. Prior to starting a project, it is important to plan how data will be managed throughout the lifecycle. This section of the website discusses the following topics:



Acquiring data for a project involves collecting or generating new data or obtaining existing data. This section of the website discusses the following topics:



Processing data involves various activities associated with the preparation of new or previously collected data inputs, including:

  • Validating Data
  • Summarizing Data
  • Transforming Data
  • Integrating Data
  • Subsetting Data
  • Deriving Data

This section also describes how to document processing steps through Workflow Capture.



Data analysis involves various activities associated with exploring and interpreting processed data. Analysis activities covered on the Analyze page include:

  • Statistical Analysis
  • Visualization
  • Spatial Analysis
  • Image Analysis
  • Modeling
  • Interpretation



Preservation involves actions and procedures used to ensure long-term viability and accessibility of data. This section of the website discusses the following topics:

This section also describes the USGS Science Data Exit Survey, a tool for documenting data and transforming the knowledge and experience of a departing employee.



Publishing and sharing data is an important and required stage in the research process, just like publishing traditional peer-reviewed journal articles. This section of the website discusses the following topics:


Cross-Cutting Elements 

Cross-cutting elements describe activities that must be performed continuously across all stages of the lifecycle to help support effective data management.

Describe (Metadata, Documentation)

Throughout the data lifecycle, documentation must be created and updated to reflect actions taken upon the data. This section of the website discusses the following topics:


Manage Quality

Data-quality management is a process where protocols and methods are employed to ensure that data are properly collected, handled, processed, used, and maintained at all stages of the scientific data lifecycle. The Manage Quality page covers the following topics:

  • Quality Assurance Plans
  • Quality Assurance
  • Quality Control
  • Documenting Data Quality


Backup & Secure

Backing up and securing data involves protecting data from accidental data loss, corruption, and unauthorized access. View the Backup & Secure page.