Data Management

Data Lifecycle

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

USGS Science Data Lifecycle Model

USGS Science Data Lifecycle Model

The USGS Science Data Lifecycle Model (SDLM) illustrates the stages of data management and describes how data flow through a research project from start to finish.

Table of Contents

This page briefly describes the USGS Science Data Lifecycle model components and how they are used to organize the content on this website.

Plan

Acquire

Process

Analyze

Preserve

Publish / Share

Cross-Cutting Elements (Describe, Manage Quality, Backup & Secure)

    Cover image USGS Open-file Report 2013-1265

    USGS Science Data Lifecycle Model
    This Open-File Report outlines data management steps to help ensure that USGS data are discoverable, and preserved beyond the research project. (Public domain)

    Plan

    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:

     

    Acquire

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

     

    Process

    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.

     

    Analyze

    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

     

    Preserve

    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.

     

    Publish/Share

    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 unauthorize access. View the Backup & Secure page.