Confluence Retirement

In an effort to consolidate USGS hosted Wikis, myUSGS’ Confluence service is scheduled for retirement on January 27th, 2023. The official USGS Wiki and collaboration space is now SharePoint. Please migrate existing spaces and content to the SharePoint platform and remove it from Confluence at your earliest convenience. If you need any additional information or have any concerns about this change, please contact Thank you for your prompt attention to this matter.

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Management, policy, and standards include data stewardship, the implementation of the Science Data Management Life CycleLifecycle, knowledge management, data standards, governance, and policy.


USGS SCIENTISTS conduct MONITORING, ASSESSMENT, AND RESEARCH that generates DATA ASSETS. Through the application of business, computational, and analytical processes and technologies, these USGS DATA ASSETS flow vertically through the SSF from a base of MONITORING, ASSESSMENT, AND RESEARCH through the Scientific Science Data Life Cycle Lifecycle Model (SDLCSDLM) processes, applications, Web services, and semantics. The DATA ASSETSare transformed into INFORMATION products that benefit from data and knowledge management and also increase KNOWLEDGE and understanding of the Earth's physical and biological systems. Data assets also flow horizontally through the SSF from and through science projects to data and knowledge management.



Science Inputs (the brown elements)

Monitoring, Assessment, & Research: USGS scientists conduct monitoring, assessment, and research that generates data assets. Through the application of business, computational, and analytical processes and technologies, these assets are converted into information products that can be shared with other researchers, stakeholders, and citizens to increase our knowledge and understanding of the Earth's physical and biological systems.

Science Project Support: Successful science projects encompass a range of activities represented in the SDLCSDLM. At each step in the cycle, researchers and data stewards rely on an array of sophisticated tools and services for data, information and knowledge discovery, acquisition, integration, management, and sharing.

Communities of Practice (the tan element)

Communities of practice are the foundation for CDI and all its products – the communities of people working towards the goal of advancing scientific data and information management and data integration across the USGS.

Data & Information Assets (the blue elements)

USGS assets include Data (e.g., raw data, databases, and linked open data (RDF1)); Information or derived/interpreted information products in the broad sense (e.g., published or shared maps, reports, datasets); and Knowledge of all types and in all forms — recorded, organized, and preserved in the form of various artifacts. Knowledge can then be improved; shared across groups, organizations, and domains; and reused to support individual and group learning and research.

Computational Tools & Services (the green elements)

Scientific Science Data Life Cycle Lifecycle processes include tools and services that move data through the SDLC, human and machine interactions, and interactions with data through technology. 

Detailed descriptions of SDLC Processes:

  • Planning – A documented sequence of intended actions to identify and secure resources and gather, maintain, secure, and utilize data assets;
  • Acquisition – The series of actions for collecting or adding to data assets;
  • Processing – A series of actions or steps performed on data to verify, organize, transform, integrate, and extract data in an appropriate output form for subsequent use;
  • Analysis – A series of actions and methods performed on data that help describe facts, detect patterns, develop explanations, and test hypotheses;
  • Preservation – Actions and procedures to keep data for some period of time; to set data aside for future use; and
  • Publishing/Sharing – To prepare and issue, or to disseminate data or information products.
Semantics convert raw data into data that can be interpreted by machines: Machine Readable Metadata, Semantic Mediation for Data Integration & Discovery, Ontologies/Vocabularies, and World Wide Web Consortium Standards.

Web Services include machine to machine data exchange, SOAP,2 REST,3 SPARQL4 EndPoints, and other protocols and services.

Applications include human readable data services and user interfaces to data driven applications.

Management, Policy, & Standards (the purple elements)

Data Management includes data and metadata standards and policies and occurs in all phases of the Science Data Life Cycle Lifecycle from scientific research to finished information products.

Knowledge Management involves the creation, standardized documentation, and organization of knowledge using tools such as SKOS5 Vocabularies and information modeling, resulting in the formation of knowledge bases.

1 Resource Description Framework

2 Simple Object Access Protocol

3 REpresentational State Transfer

4 SPARQL Protocol and RDF Query Language

5 Simple Knowledge Organization Systems